esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Column Preparation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Probe Molecules** & **E** & **S** & **A** & **B** & **L** \\ \hline
1-Hexanol & 0.21 & 0.44 & 0.344 & 0.52 & 3.643 \\ Anisol & 0.712 & 0.768 & 0 & 0.311 & 3.808 \\ Cyclohexanone & 0.403 & 0.895 & 0 & 0.53 & 3.759 \\
1,2-Dichlorobenzene & 0.872 & 0.771 & 0 & 0.054 & 4.516 \\ Butyl benzene & 0.595 & 0.499 & 0 & 0.139 & 4.734 \\
1-Nonanal & 0.121 & 0.636 & 0 & 0.414 & 4.838 \\ Butyl Acetate & 0.079 & 0.57 & 0 & 0.438 & 3.409 \\ Iodobenceno & 1.182 & 0.784 & 0 & 0.135 & 4.548 \\
1-Nitropropane & 0.243 & 0.925 & 0.049 & 0.27 & 2.878 \\
1-Pentanol & 0.219 & 0.44 & 0.344 & 0.52 & 3.128 \\ Pyridine & 0.635 & 0.843 & 0 & 0.532 & 3.006 \\ Benzonitrile & 0.742 & 1.135 & 0 & 0.331 & 4.04 \\ Nitrobenzene & 0.846 & 1.138 & 0 & 0.269 & 4.539 \\ Naphthalene & 1.24 & 0.906 & 0 & 0.193 & 5.154 \\
2-Heptanone & 0.123 & 0.662 & 0 & 0.496 & 3.781 \\
1-Phenyl ethanol & 0.823 & 0.819 & 0.351 & 0.648 & 4.424 \\ Benzaldehyde & 0.813 & 1.025 & 0 & 0.394 & 4.005 \\
1-Octanol & 0.199 & 0.44 & 0.344 & 0.52 & 4.648 \\
1-Chloronaphthalene & 1.419 & 0.951 & 0 & 0.135 & 6.175 \\ Aniline & 0.955 & 1.003 & 0.249 & 0.425 & 3.956 \\ Fluorene & 1.664 & 1.12 & 0 & 0.252 & 6.921 \\
4-Chloroaniline & 1.017 & 1.128 & 0.366 & 0.309 & 4.972 \\ Benzyl Alcohol & 0.803 & 0.882 & 0.4 & 0.557 & 4.244 \\ Cyanmyml Alcohol & 1.119 & 0.971 & 0.451 & 0.606 & 5.475 \\ Phenol & 0.769 & 0.759 & 0.716 & 0.319 & 3.844 \\ Acetophenone & 0.806 & 1.026 & 0 & 0.503 & 4.533 \\
3-Methyl-1-butanol & 0.198 & 0.423 & 0.351 & 0.501 & 2.963 \\
1-Butanol & 0.224 & 0.44 & 0.344 & 0.52 & 2.578 \\ Ethyl Benzoate & 0.694 & 0.886 & 0 & 0.444 & 5.032 \\
2-Phenyl ethanol & 0.787 & 0.797 & 0.39 & 0.636 & 4.741 \\
1-Decanol & 0.191 & 0.44 & 0.344 & 0.52 & 5.589 \\ Undecane & 0 & 0 & 0 & 0 & 5.185 \\ Styrene & 0.849 & 0.671 & 0 & 0.177 & 3.860 \\
2-Chlorophenol & 0.882 & 0.668 & 0.538 & 0.342 & 4.118 \\
4-Chlorophenol & 1.016 & 0.794 & 0.886 & 0.205 & 4.802 \\
3,5-Dimethylphenol & 0.768 & 0.764 & 0.669 & 0.347 & 4.792 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Probe molecules and values of solute descriptors used in the characterization of PILs based on AAIL and divinylbenzene (DVB) stationary phases. programmed as follows: 45 \({}^{\circ}\)C for 1 min, increased at a rate of 10 \({}^{\circ}\)C min\({}^{-1}\) to 160 \({}^{\circ}\)C, and then held at this temperature for 2 min.
# Reliability Analysis of C\({}^{4}\)ISR Systems Based on Goal-Oriented Methodology
## 3 Case Study  
### The C\({}^{4}\)ISR System  
This paper analyzes the reliability of the C\({}^{4}\)ISR system. The C\({}^{4}\)ISR system is mainly composed of a database, an information desk, a command and control desk, and a commander center. In the database, the signal is the input of the signal receiver, and the outputs are two signal flow paths: the from the input to the server and information exchange module we well as from the input to the RAID control. Then the signal flows into the data storage module, and finally reaches the hard disk; The output of the database is the input of the information editing station. In this device, the signal reaches the data loading module and then goes to the motherboard module; The input of the command console is conducted by the intelligence editing station. The signal passes the USB interface module and motherboard module, then it is distributed in two paths: from the input to the touch display and from the input to the control exterior; After the headquarters receives the signal from the command console it sends the signal to the motherboard module. The schematic diagram of the C\({}^{4}\)ISR system is demonstrated in Figure 2. According to the C\({}^{4}\)ISR system, a GO model was constructed, see in Figure 3. The elements of the GO model are introduced in detail in Table 2. The failure rates of each unit are listed in Table 3.  
Figure 2: The schematic diagram of the C\({}^{4}\)ISR system. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Unit** & \begin{tabular}{c} **Operator** \\ **Number** \\ \end{tabular} & \begin{tabular}{c} **Operator** \\ **Type** \\ \end{tabular} & \begin{tabular}{c} **Function** \\ **Description** \\ \end{tabular} & **Unit** & \begin{tabular}{c} **Operator** \\ **Number** \\ \end{tabular} & \
\begin{tabular}{c} **Operator** \\ **Type** \\ \end{tabular} &
\begin{tabular}{c} **Function** \\ **Description** \\ \end{tabular} \\ \hline Input signal & 1 & 5 & Input unit & Monitor\_2 & 77 & 1 & Two-state unit \\ \hline Communication & & & & & & \\ module\_1 & 2 & 1 & Two-state unit & Control unit & 78 & 1 & Two-state unit \\ \hline Communication & & & & & & \\ module & 4 & 6 & Signal-on unit & Remote control & 79 & 1 & Two-state unit \\ \hline Communication & & & & & & \\ module & 4 & 6 & Signal-on unit & Communication & 80 & 1 & Two-state unit \\ \hline Network module & 5 & 1 & Two-state unit & Signal receiver & 9 & 1 & Two-state unit \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The C\({}^{4}\)ISR System software and hardware unit and their logical relationships. Figure 3: GO graph model for system reliability.
## 1 Introduction
### Background
To sum up, our research aims to accomplish the following goals: an automated alarm system that communicates with the security control center to propose an authentication protocol that supports real-time event status report/record, data traceability, data integrity, data non-repudiation, and proposes an arbitration mechanism when the dispute occurs.
## 4 Results
### Comparison with State-of-the-Art Methods  
This section provides a comparative analysis of the proposed approach against current state-of-the-art methods. Table 7 displays the reported results of various approaches reviewed in Section 2. The comparison was performed against the proposed method using the F1-Score, HD, accuracy, precision, sensitivity, and specificity metrics.  
The proposed approach achieved the highest F1-Score among all the compared methods and MRI modalities using the FLAIR sequences. Regarding the DWI images, the proposed method obtained better F1-Scores than the works in [18; 23; 24; 39]. Nonetheless, [PERSON] et al. [24] obtained the highest score in accuracy and specificity in DWI and in general. However, the results demonstrate that the proposed method is competitive in these metrics evaluation. Although [PERSON] et al. [9] reported the best HD using the combination of FLAIR and DWI sequences, the proposed method achieved narrow results. Finally, the proposed approach scored higher in the T1 and T2 modalities than [PERSON] et al. [18] and [PERSON] et al. [39].  
Regarding the ISLES 2022 dataset, [PERSON] et al. [25] achieved the highest F1-Score using the DWI and ADC modalities, followed by [PERSON] et al. [27] and the proposed method, as can be observed in Table 8.  
Figure 13: Examples of ground truth mask of DWI images in the axial plane (first row) and the segmentation results by the proposed method using \(\lambda_{GDL}=0.3\), \(\lambda_{FL}=0.7\) (second row) and \(\lambda_{GDL}=0.1\), \(\lambda_{FL}=0.9\) (third row) for mask categories Small (**a**,**e**,**i**), Medium Down (**b**,**f**,**j**), Medium Up (**c**,**g**,**k**), and Large (**d**,**h**,**l**).
## 3 Smart Energy Management in the Future IoT
### AI-Enabled Energy Harvesting in IoT Devices  
Energy harvesting (EH) describes a technique for capturing and converting energy from the environment to electrical energy. This technique is explored in ref. [13] for its potential usage for IoT nodes and authors identified costs, miniaturization, harvesting efficiency, operating frequency, and protocols as some of the challenges against EH technologies in IoT. Further investigation of this concept is carried out in refs. [14; 15; 16], where authors modeled, simulated and optimized the use of solar EH in sensor nodes. The challenges associated with conventional EH techniques necessitate the use of AI to improve existing techniques. For example, nodes are deployed based on their application areas which imply that the availability of energy sources is not certain, especially if nodes are deployed in locations with limited access to renewable energy sources. This challenge can be mitigated using AI to predict energy availability [17], thereby scheduling the node's major energy-consuming tasks to the predicted period.  
Furthermore, AI can be used to develop energy consumption plans or analytics [18] that can provide insight into other avenues for conserving energy. In an EH-based IoT system, the total amount of harvested energy in a node until time \(t\) is given by \(e(t)\) shown in Equation (1) while the total amount of energy required to transmit collected data until time \(t\) is given by \(d(t)\) as in Equation (2) [17]. Thus, the total available system energy is shown in Equation (3). Research efforts in the field of IoT energy harvesting are aimed at maximizing the former while minimizing the latter.  
\[\mathbf{e(t)}=\int_{\mathbf{0}}^{\mathbf{t}}\mathbf{P_{captured}(t^{\prime}) }\mathbf{d}t^{\prime} \tag{1}\]  
\[\mathbf{d(t)}=\int_{\mathbf{0}}^{\mathbf{t}}\mathbf{P_{used}(t^{\prime})} \mathbf{d}t^{\prime} \tag{2}\]  
\[\mathbf{E_{available}(t)}=\mathbf{min(C,\;e(t)-d(t))} \tag{3}\]  
where \(\mathbf{P_{captured}(t)}\) and \(\mathbf{P_{used}(t)}\) are the harvested and consumed energy between time t and \(\Delta\)t respectively, \(\mathbf{C}\) is the total energy storage capacity. Some algorithms with potential usage in EH-based IoT related tasks are the regression models commonly used to predict a continuous value. Figure 5 lists some of the required data and other potential usage areas when combined with AI algorithms.  
Figure 4: Classification of the surveyed AI techniques.
## 4 Simulation and Experimental Results
### Simulation Model of an Inverter Connected to a Three-Phase Unbalanced Power Grid
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Physical Quantities** & **Symbol** & **Measured Value** \\ \hline \multirow{3}{*}{RMS of three-phase voltage at connection point} & \(V_{\text{PCC\_B}}\) & 211.1 V \\ \cline{2-3}  & \(V_{\text{PCC\_C}}\) & 211.1 V \\ \hline Three-phase unbalance degree & \(\varepsilon_{\text{U2}}\) & 0\% \\ \hline \multirow{3}{*}{The inverter RMS of three-phase grid current} & \(I_{\text{B}}\) & 9.448 A \\ \cline{2-3}  & \(I_{\text{C}}\) & **11.39 A** \\ \hline \multirow{3}{*}{Three-phase output power of the inverter} & \(P_{\text{A}}\) & 1994 W \\ \cline{2-3}  & \(P_{\text{B}}\) & 1994 W \\ \cline{1-1} \cline{2-3}  & \(P_{\text{C}}\) & 2405 W \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation results of the proposed control strategy. Compare Figure 12 with Figure 11, the grid current waveform with the proposed control strategy has almost no distortion, and the power quality is also improved. Therefore, the proposed control strategy is suitable for a three-phase unbalanced grid. Compare Table 3 with Table 2, the most obvious difference is that \(I_{\text{C}}\) is larger than \(I_{\text{A}}\) and \(I_{\text{B}}\) in Table 3, while \(I_{\text{C}}\) is smaller than \(I_{\text{A}}\) and \(I_{\text{B}}\) in Table 2. In both simulations, the C-phase load increases, so the C-phase power supply is expected to output lager current. This is exactly what the proposed strategy does. Therefore, the proposed control strategy is suitable for a three-phase unbalanced grid. Therefore, the proposed control strategy can realize three-phase energy mutual aid.
## 2 Subjects and Methods
### Statistical Analysis  
First of all, the incidence, first-year mortality rate and first-year standardized mortality ratio (SMR) were calculated by each year. The incidence of new hip fracture was calculated as the number of patients with new hip fracture divided by the live subjects in the same year and stratified them by gender. The first-year mortality rate of new hip fracture was calculated as the number of deaths divided by patients with new hip fracture. The first-year SMR was the ratio of observed deaths in patients with new hip fractures to expected deaths in the study population.  
An independent t-test was tested between the means in genders. Chi-square test was used to test the association between fracture numbers, death, first-year mortality rate and genders. We performed the linear regression for the long-term trend for incidence, first-year mortality rate and first-year SMR in elderly hip-fracture. Regression analysis was performed with logistic regression. Significance was defined if _p_-value \(<\) 0.05.  
Next, we used first univariate Cox regression analysis, controlling that the proportionality of hazards was not violated, to test the different variables for their ability to predict mortality at 5 years from hip fracture patient dataset. On the other hands, the hazard ratios (HR) can be interpreted as the relative risk of dying due to hip fracture, are presented with their 95% confidence intervals as Cox regression analysis. Then, the mortality probability for any case at 5 years from diagnosis was calculated [23]. Cox analysis was performed by the SPSS ver. 22.0 software.  
Moreover, the following rules of the classifies with data mining classifiers as below: hip fracture patients alive \(\geq\)5 years from hip fracture patients who died within 5 years from disease dataset of whole NHIRD were classified as \"dead\" while patients alive but with a disease duration \(<\)5 years were excluded from the analysis. Thus, we incorporated the following three steps in building our data mining based model: (a) attribute selection based on the literature; (b) comparative evaluation of different classifiers or learning algorithms to select one classifier is the best classifier in this study; and (c) decision tree were generated through data mining tool and identified variables respectively for helping the prognosis of hip fracture patients. The classifiers are re-learned from the resampled datasets to further improve the performance on the hip fracture survival task.
## 3 Result and Discussion
### Permeability  
Under the coupled effect of F-T weathering and acid erosion, permeability gets a substantial increase. The permeability of sandstone under different F-T cycles and acid solutions are listed in Table 1. As for those samples immersed in different acid solutions, their permeabilities have no obvious variation trends; they just fluctuate around a level. The data in Table 1 show that permeability obviously increases with F-T cycles and the trend is similar among samples treated by different acid solutions. Take samples immersed in acid solution with pH of 2.8, for example; the permeability of sandstone gains a 2165.56% augment. To put it more precisely, the permeabilities are \(79.46\times 10^{-4}\) mD, \(732.59\times 10^{-4}\) mD, \(957.74\times 10^{-4}\) mD, \(1415.71\times 10^{-4}\) mD and \(1720.76\times 10^{-4}\) mD for samples which underwent 0, 10, 20, 30 and 40 F-T cycles, respectively.  
These samples gain their most remarkable permeability growth during the first 10 F-T cycles. This can be explained from the growth trend of pore distribution shown in Figure 5. It is known that micro pores and meso pores play major roles in initial sandstone; however, macro pores account for only 0.62% of porosity. As water majorly penetrates sandstone through cracks connected by macro pores, undeveloped pore systems with few macro pores provide few water channels; therefore, the permeability of sandstone under 0 F-T cycles is small. For sandstone which underwent 10 F-T cycles, macro porosity increased from 0.62% to 4.24%; more macro pores provide more water channels, so permeability gains remarkable growth after 10 F-T cycles.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Permeability/10\({}^{-4}\) mD**} & \multicolumn{4}{c}{**PH of Acid Solution**} \\ \cline{2-5}  & **2.8** & **4.2** & **5.6** & **7.0** \\ \hline \multirow{5}{*}{F-T cycles} & 0 & 79.46 & 54.92 & 51.78 & 43.17 \\  & 10 & 732.59 & 429.78 & 633.42 & 616.06 \\ \cline{1-1}  & 20 & 957.74 & 622.90 & 698.76 & 654.34 \\ \cline{1-1}  & 30 & 1415.71 & 1083.41 & 1127.65 & 1275.99 \\ \cline{1-1}  & 40 & 1720.76 & 1714.75 & 1461.22 & 1657.65 \\ \hline \end{tabular}
\end{table}
Table 1: Permeability of sandstone under different F-T cycles and acid solution.  
Figure 5: Porosity of sandstone immersed in acid solution after different F–T cycles: (**a**) micro-porosity; (**b**) meso-porosity; (**c**) macro-porosity; (**d**) porosity of samples under 40 F–T cycles in different acidic solutions.
## 3 Simulation Configuration
### Setup  
With the aforementioned simulation software, we simulated GNSS and LEO observations for ground stations on a global scale (they are grid points assumed to be distributed globally with the space resolution 5\({}^{\circ}\) by 5\({}^{\circ}\)) from 1 June 2014 to 7 June 2014. Then the adapted PANDA software is used to conduct the LeGNSS PPP in order to evaluate the performance of LeGNSS. In particular, four stations located at P1 (80\({}^{\circ}\)S, 30\({}^{\circ}\)W), P2 (0\({}^{\circ}\)S, 30\({}^{\circ}\)W), P3 (30\({}^{\circ}\)N, 120\({}^{\circ}\)E), and P4 (75\({}^{\circ}\)N, 120\({}^{\circ}\)E) are adopted for the kinematic mode of PPP tests in order to show the PPP performance more clearly.
## 3 Results  
### The Current State of Forest Distances in the Amazon  
We analyzed more than 150 million lidar shots from the GEDI mission (25 m footprint) for the entire Amazon region and calculated the distance to the forest edge for each measurement using the TanDEM-X forest/non-forest map (50 m resolution X-band radar) (Figure 2a). The distances cover a range with values up to 42 km. On average, the distance from a forest stand to the edge is 1750 m (Figure 2c). The obtained distances to the forest edge in areas along the Amazon River, in the area of the Arc of Deforestation and near the Andes are rather small. Here, the distances of forests to non-forest areas range from 0 to 500 m. These values indicate a strong fragmentation of the rainforest. More intact areas with larger distances to the edge can be observed in central Amazon. Here, minor fragmentation has occurred and the distances to the nearest forest-free patch are at least 1500 m.  
Figure 2: The Distance map of the Amazon rainforest is shown in (**a**). The distance map indicates the distance of each forest stand to the nearest forest edge. For visualization purposes the scale is limited to  
1500 m distance. The map has a resolution of 1 km\({}^{2}\), also the analysis was performed at 50 m resolution. In addition, a zoomed-in section of a selected region is shown. In (**b**), the TanDEM-X forest/non-forest map with a resolution of 50 m, as well as the GEDI footprints (grey) available in this area (here: approx. 190,000 shots) and a part of the Amazon river (black) is shown. In (**c**), the histogram corresponding to (**a**) with a resolution of 1 km\({}^{2}\) is shown. The mean value of the average distances to the forest edge is shown in red at 1750 m. The maximum value of the mean distance to the forest edge in the Amazon is 42 km. A histogram corresponding to (**a**) on footprint level can be found in the attachment (Figure S1).
# Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches
## 2 Security Challenges and Malware Attacks in the IoT
### IoT Characteristics and Challenges on Security
The IoT involves smart devices and sensors, some of which use non-chargeable batteries. This makes battery life one of the predominant challenges in IoT security. Running security rules will drain the battery resources. Applying minimum security requirement measures is not recommended and is risky when devices have access to (or collect) sensitive data. Increasing battery size and capacity is not always possible, because these devices are designed to be lightweight and low-cost. In addition to device limitation and object identification, device authentication and authorization are examples of the IoT network-layer security challenges. Issuing certificates to each object in the IoT is extremely challenging due to the number of connected objects and lack of a global root certificate authority. The Domain Name System (DNS), which is used to identify objects and their attributes, is another IoT network-layer security challenge. Data integrity is problematic here due to the possibility of being hacked by a man in the middle or a DNS cash poisoning attack. This attack is the act of placing false information to redirect Internet traffic to malicious websites. The threat of malware attacks arises in IoT due to these security challenges. Antivirus software is the main line of defense to detect known malware in real-time. However, the traditional security solutions have not been efficient and do not provide decentralized and strong security solutions in the IoT [9]. Due to the IoT device limitation and computing power, shifting similar solutions from traditional platforms to IoT might not be affordable [19]. Battery size and expected durability are challenges that make the implementation of security measures more limited, as a device has to be energy efficient as well as secure. Moreover, in IoT systems, network resources are integrated into devices that were never previously anticipated to be part of computer networks [20]. Integrating IoT devices into traditional networks introduces a new paradigm of security. The integrated systems inherit the traditional network security issues besides those targeting IoT devices [9]. Consequently, using traditional security measures is not enough to give IoT systems malware detection capabilities.
# Traceability and Spectral Analysis of Transmission Error in Rotary Vector Planetary Gear Reducers
## 6 Conclusions  
In this study, we analyzed the motion characteristics of key components in the rotating vector planetary gear reducer and conducted a root-cause analysis of TEs, identifying  
Figure 12: Characteristic frequency response of transmission error for 65 BX-121-RVE reducers.  
the characteristic frequencies of each component and mapping these frequencies to the corresponding parts. Testing eight prototypes across two models validated the accuracy of this root-cause analysis. The main conclusions are as follows:  
1. The errors in the sun gear and planet gears primarily affect the meshing state of the first-stage transmission, while the errors in the cycloidal gear and pin gear primarily impact the meshing state of the second-stage transmission. Errors in the crankshaft and flange primarily influence the gear meshing state of both stages, particularly the second stage, due to their rotation.
2. The quality of the cycloidal gear and pin gear is the primary factor influencing the transmission accuracy of the rotating vector planetary gear reducer, with the cycloidal gear being the most critical component. Therefore, ensuring the quality of the cycloidal gear is essential for maintaining transmission accuracy. The cycloidal gear's error possesses a single-source characteristic frequency, facilitating its identification.
3. Compared to the cycloidal gear and pin gear in the second transmission stage, the sun gear and planet gears, as components of the first-stage transmission, have a relatively smaller impact on the overall TE of the reducer.
4. Some characteristic frequencies are influenced by multiple sources, complicating the process of tracing errors due to their multifaceted origins. Further research is necessary to address this complexity in future studies.  
Conceptualization, W.S. and X.Z.; methodology, X.Z.; software, L.S.; validation, X.Z., Z.H. and W.S.; writing--original draft preparation, X.Z.; writing--review and editing, W.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Dataset available on request from the authors.  
The authors declare no conflicts of interest.