esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Experiment  
### Experimental Environment and Parameters  
In this chapter, numerous simulations have been conducted to evaluate the performance of the CEAGA in task allocation for IUWSNs. In this experiment, the performance of these technologies is tested on PC using Pentium 2.40 GHz, 8 GRAM, WIN-10 OS and the MATLAB R2021a software to show their applicability to task assignment. It should be pointed out that regarding the comparison algorithms involved in this article, including the HGA, MBPSO, and ISA algorithms, the author only used the latest known versions to date. These versions have undergone comprehensive research and investigation to ensure the most accurate and reliable comparative results in the study. In the experiment, the population size and algebra of each algorithm were set to 100 and 1000, respectively. In the CEAGA, adaptive operators are used to calculate the probability of crossover and mutation in real time. In Equations (24) and (25), in combination with the task allocation problem in IUWSNs, \(Pc_{min}\) and \(Pc_{max}\) are set to 0.6 and 0.8, respectively, and \(Pm_{min}\) and \(Pm_{max}\) are set to 0.01 and 0.08, respectively. Therefore, the range of crossover probability and variation probability can be calculated as 0.6 to 0.8 and 0.01 to 0.08, respectively, according to Equations (24) and (25). Its parameters are set in Table 3. In GA, the crossover and mutation probabilities are set to fixed values, which are 0.8 and 0.01, respectively. Parameter settings are shown in Table 4. In the PSO, the learning factor and inertia weight are set to 1.49445 and 0.798, respectively, and the particle speed limit is set to \(-0.5\) to 0.5, as shown in Table 5. In the SA, the initial temperature is set to 1000, while the annealing factor is established at 0.7. The specific parameter settings are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm Name** & **Generations** & **Population Number** & \(h\) & \(Pc_{min}\) & \(Pc_{max}\) & \(Pm_{min}\) & \(Pm_{max}\) & \(\eta\) \\ \hline CEAGA & 1000 & 100 & 15 & 0.6 & 0.8 & 0.01 & 0.08 & 4.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental parameter setting of the CEAGA.
## 3 Smart Metering in Germany
### Secure Communication within the SMGW Architecture  
Not only do the technical requirements have to be considered when integrating an EMS into the SMGW infrastructure, but the extensive security requirements must be as well. The respective specifications are presented shortly and the implications for the prototype are further highlighted. For the SMGW and the security module, the protection profiles defined in [9; 10] determine security requirements. The overall communication infrastructure in the WAN underlies the SM-PKI defined in [29; 30]. While in [29] the architecture of the SM-PKI and the design of the used certificates within the PKI is illustrated, in [30] all requirements are specified that institutions participating in the SM-PKI have to satisfy according to their roles. The overall architecture consists of three hierarchical layers: The root-certification authority (CA) as trust anchor, the sub-CA as issuer of certificates for end users, and, as the last layer, the SMGW, GWA, and EMP as end users with role specific certificates. Certificates issued to end users are used to guarantee secure communication, integrity, and data privacy. Therefore, the issued certificate set for each end user consists of three certificates with different purposes: the TLS certificate for secured communication, the encryption certificate for information encryption independent of TLS, and the signature certificate to verify integrity independent of TLS authentication. While the three certificates together are needed for most WAN use cases, only the TLS certification is needed for the transparent data tunnel. Additionally, for the SMGW there are two types of certificate triplets. Firstly, during the manufacturing process of the SMGW, so-called quality seal certificates (\"Gutesiegelertifikate\") are stored on the security module. Secondly, to put the SMGW in a productive mode, those certificates have to be replaced with new ones issued from the respective sub-CA for the role SMGW.  
Requirements for issuing certificates in the SM-PKI are defined by [30], including technical, personnel, and organizational aspects. The policy distinguishes active and passive EMPs, with higher security requirements for aEMPs. As a comprehensive analysis of all requirements according to all roles surpasses the scope of this paper, we only discuss the most important regulations for companies acting as aEMP in Section 3.6. Additionally, the TR-03109-3 [32] has to be considered, as it technically specifies the cryptographic functions referenced in [29; 30]. While the SM-PKI does not cover communication in the HAN, the cryptographic requirements in [32] encompass HAN and WAN communication. Particularly, the use of a selection of elliptic curves and a restriction to five specific cipher suites for the TLS handshake is mandatory. For testing purposes, the BSI defines a second version of the SM-PKI, the so-called SM-Test-PKI with less restrictive security requirements. This SM-Test-PKI can be used to verify the technical compatibility of systems with the SM-PKI.
# Activity Prediction Based on Deep Learning Techniques
## 4 Results and Discussion  
### Activity Prediction  
As described in Section 2, the recorded data contain longitudinal and latitudinal location, accelerometer, and elevation information, and they were recorded in two-minute periods. Figure 8 shows the overall procedure for classification tasks beginning with the preprocessing of data. The raw dataset contained a large number of missing values, and the data containing the missing values were excluded for simplicity of simulation in the preprocessing step. The preprocessing step includes the normalization of the dataset, for which z-score normalization is used [33]. If a proper imputation method is chosen for our data, the method can be used to impute the missing values for further experiments. The dataset was then separated into training (70%) and testing (30%). The 13 activity patterns are enumerated and one-hot coded for the simulation, as mentioned in Section 2. We used Keras, an open-source software library that provides a Python interface for artificial neural networks [34]. Both MLP and LSTM models were set up, as shown in Table 4. In the LSTM model, three previous data points with five features each (window size was set to 3) were used for the prediction of the data point at the next time stamp, so the input shape is 3 \(\times\) 5. There is only one input and output layer, and the number of nodes at the hidden layer is chosen as 128. Both network models have 13 output nodes to match the 13 activity patterns. The activation functions chosen for the hidden layer and output layer were ReLU and SoftMax. The \"Adam\" was chosen as an optimizer [32].  
We performed an exhaustive search to determine the architecture of the two network models by changing the number of hidden layers from large values to small values, using sample training data as shown in Table 5. The table shows that the accuracy of LSTM significantly drops as the number of hidden layers increases, whereas MLP's accuracy stays relatively stagnant. Based on this pilot test, we chose the architecture of the models, as shown in Table 4. Moreover, we set the architecture and specification of the two models in close resemblance in order to run the simulations under similar environments.  
Figure 8: Activity prediction flow based on MLP and LSTM.
# Fault Detection and Isolation for Redundant Inertial Measurement Unit under Quantization
## 3 Experiments Results and Analysis
### Experiments of Ramp and Square Wave Faults
#### 3.3.2 Square Wave Fault  
The amplitudes of square wave fault is 5\({}^{\circ}\)/h, and the frequency is 5 Hz. The FDI results are shown in Figures 14-16.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Time Constant** & **3** & **3.85** & **5** & **8** & **10** & **20** \\ \hline
**False alarm rate** & 0.006 & **0.000** & 0.000 & 0.000 & 0.000 & **0.000** \\
**False isolation rate** & 0.702 & **0.720** & 0.600 & 0.560 & 0.540 & **0.240** \\
**PCD1** & 0.000 & **0.000** & 0.000 & 0.000 & 0.000 & **0.000** \\
**PCD2** & 0.940 & **1.000** & 0.860 & 0.100 & 0.020 & **0.000** \\
**PCD3** & 0.000 & **0.000** & 0.140 & 0.900 & 1.980 & **1.000** \\
**PCD4** & 0.000 & **0.000** & 0.000 & 0.000 & 0.000 & **0.000** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The relationship between time constant and the effect on detecting faults (ramp fault).  
Figure 13: Second-order channel filter. (**a**) Fault detection; (**b**) Fault isolation.  
Figure 14: Original channel filter. (**a**) Fault detection; (**b**) Fault isolation.  
Figure 15: First-order channel filter. (**a**) Fault detection; (**b**) Fault isolation.  
It is found that the fault detection function of the original channel filter has never exceeded its threshold value in Figure 14, indicating that no fault has been detected. Figures 15 and 16 show that the fault detection function exceeds the threshold in 500 s, indicating that the first-order channel filter and the second-order channel filter have detected the fault.  
Take the second-order channel filter as an example (the Monte Carlo simulation 1000 times). The statistical results are shown in the following Table 7.
# Sensing of Transition Metals by Top-Down Carbon Dots
###### Abstract  
Carbon quantum dots (CQDs) are a new class of carbon-rich materials with a range of unique optical and structural properties. They can be defined as carbon nanoparticles, with sizes in the range of 1-10 nm, displaying absorption and emission activities in the UV-VIS range. Depending on the structure, CQDs display a wide variability of properties, which provides the possibility of finely tuning them for several applications. The great advantages of CQDs are certainly the ease of synthesis, non-toxicity, and the strong interactions with the surrounding environment. Based on this, CQDs are especially promising as selective chemosensors. The present study reports on carbon quantum dots synthesized with a top-down (TD) approach, and characterized by different optical, spectroscopic, and morphological techniques to identify the selectivity for metal ions belonging to the first transition series. In particular, the study focuses on the interaction between two samples, namely TD and TDA, featuring different surface functionalization, and heavy metal ions. Their sensing towards Co\({}^{2+}\), Cu\({}^{2+}\), Fe\({}^{3+}\), Zn\({}^{2+}\), and Ni\({}^{2+}\) has been tested by fluorescence (PL), steady state absorption spectroscopy, and time-resolved PL spectroscopy, in order to determine the fluorescence quenching. We found a PL quenching in the presence of concentrations of metal salts starting from 0.5 \(\mu\)M, and a selectivity towards the interacting ions, depending on CQDs' surface features paving the way for their use for sensing.  
carbon dots; sensing; fluorescence +
Footnote †: journal: Applied Sciences
# A Survey on Satellite Communication System Security
## 2 Existing Surveys and Motivation
### Existing Surveys
(2018) address the overall security and reliability issues in LEO satellite systems, offering solutions for these challenges. They also cover physical threats, which were previously under-addressed. However, this study only covers general attacks and solutions related to SCS networks and is not divided in detail according to clear classification criteria. [PERSON] et al. (2018) address the security threats, solutions, and challenges associated with the deployment and operation of SCSs, particularly at the link layer. This paper categorizes schemes into two principal domains: PLS and cryptographic schemes. [PERSON] et al. (2018) present a comprehensive analysis of the cybersecurity threats facing the space industry, especially in the context of the emerging New Space era. It assesses past security threats and incidents to understand adversarial threats to satellites, with a focus on the ground and RF communications as primary targets. However, they did not clearly present countermeasures against attacks. In Table 1, we compare and summarize the major attack and defense and main research points of each study.
## 4 Experimental Results And Analysis  
### Experimental Dataset  
The speech recordings used in the validation of the proposed method come from a proprietary dataset of 500 audio recordings from Spanish and English speakers. Each file is monophonic, with a length between 24 and 59 s, a sampling frequency of 8 kHz and 16 bits per sample.
## 2 Materials and Methods
### Thermal Analysis  
Differential scanning calorimetry (DSC) and thermogravimetric analysis (TG) measurements were performed with a Mettler-Toledo DSC 821\({}^{\circ}\) instrument (Mettler-Toledo GmbH, Greifensee, Switzerland), at the same time and under the same conditions. The samples were heated steadily from 25 \({}^{\circ}\)C to 500 \({}^{\circ}\)C in a non-hermetically sealed aluminum pan at a heating rate 10 \({}^{\circ}\)C/min. The mass of the samples was approximately 10 mg, and measurements were performed in a nitrogen atmosphere at a flow rate of 70 mL/min. Both TG and DSC signals obtained as a result of the measurement were recorded as a function  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Formulation**} & \multicolumn{4}{c}{**The Composition of Active Substances and Excipients (\(w/w\) \%)**} \\ \cline{2-7}  & **CsA** & **Ind** & **SPIR** & **Compritol** & **Stearic Acid** & **Tween 80** \\ \hline F1 & - & - & - & 10.0 & - & 3.0 \\ F2 & - & - & - & - & 10.0 & 3.0 \\ F3/4/F5/F6 & 0.1/1.0/2.0/5.0 & - & - & 10.0 & - & 3.0 \\ F7/78 & 0.1/1.0 & - & - & - & 10.0 & 3.0 \\ F9 & 1.0 & - & - & 10.0 & - & 5.0 \\ F10/F11 & - & 0.2/1.0 & - & 10.0 & - & 3.0 \\ F12/F13 & - & - & 0.1/0.5 & - & 10.0 & 3.0 \\ \hline \hline \end{tabular}  
* CsA: cyclosporine A, Ind: indomethacin, SPIR: spironolactone.  
\end{table}
Table 1: The composition of the investigated SLM dispersions.  
of temperature, and when both are presented, they are represented on one graph as a thermogravimetric curve and a DSC curve.  
Indium and zinc were used for calibration. To check the temperature and heat flow accuracy of the DSC modules, the indium standard (In, purity 99.999%) was selected for the low temperature and the zinc standard (Zn, purity 99.999%) was selected for the high temperature. The checks were within the defined limits.  
The DSC/TG analysis was performed on the following samples: placebo SLM, drug-loaded SLM, physical mixtures (binary mixtures of lipid and API), bulk solid lipids, APIs and other excipients. The SLM were tested both in the form of aqueous dispersion and powder obtained by spray drying. Liquid dispersions were prepared for thermal analysis: (i) by evaporation of water at room temperature under compressed air, (ii) by evaporation under the same conditions but preceded by centrifugation of the SLM dispersion or (iii) analyzed directly (without prior sample preparation). The lipid microspheres were also examined after shaking in a vortex for 5 min with methanol and centrifuging at 3500 rpm. This process, usually carried out during distribution studies, allowed for the dissolution and determination of the active substance, located on the surface of the microspheres, without dissolving the lipid matrix of the microspheres.  
Thermal characterization of the SLM was also performed after one or two years of storage (in the refrigerator or at room temperature) to evaluate the stability of the SLM dispersions and powders. Two parallel examinations were carried out for all samples. The obtained results were evaluated using STAR\({}^{\circ}\) SW 16.30 Software.
# RMFNet: Redetection Multimodal Fusion Network for RGBT Tracking
## 3 Our Method
### Improved Re-Detection Algorithm  
As shown in Figure 4, it is the flow of the re-detection algorithm of the original MDNet [12] algorithm, in which the pre-positioning at this time is still used as the target position of the current frame when the pre-positioning is judged to be unreliable, and the candidate frame is generated by expanding the search area around the predetermined position of this frame when tracking the next frame. However, changes such as deformation, rapid motion, occlusion of the target, or changes in illumination or background interference during the tracking process can cause the tracker to fail and lose the target. When the mean of the top 5 candidate boxes' scores is less than 0, the pre-positioning is deemed unreliable, and it is judged that the tracker may have drifted, causing the target to be lost. The re-detection process of the original MDNet algorithm causes a continuous accumulation of errors, which is improved in this paper, as shown in Figure 5. When the pre-positioning is unreliable, a global search is performed around the previous frame's target location by expanding the search area, and the current frame's target is repositioned. Target repositioning can detect the target again and reinitialize the tracker, avoiding the continuous accumulation of errors and improving tracking reliability. Moreover, target repositioning can be recovered promptly, preventing the tracker from continuously tracking the wrong target or losing the target, reducing the computation and time consumption and improving tracking efficiency. The reliability of repositioning is evaluated, and when it is reliable, samples are collected and updated around this position, and the tracker is updated. When repositioning is unreliable, the score of the pre-positioning and repositioning is evaluated, and the one with a higher score is selected as the target position of the current frame.
## 3 The Proposed Approach
### Adjustment of the Reliability Model
#### 3.1.1. Imperfect Preventive Maintenance Model  
The failure intensity function describes the failure rate as a function of time but does not consider the imperfection of preventive maintenance actions. In fact, the function defined in Equation (1) assumes that the failure intensity does not change after a preventive maintenance action and that the systems return to the state of \"as good as new\" after this action (i.e., perfect PM).  
However, in practice, preventive maintenance improves system performance for an intermediate condition, reducing failure intensity to a value between a state somewhere between \"as good as new\" and \"as bad as old\" after preventive activities [20]. Thus, considering the imperfect maintenance, the failure intensity function is rewritten by applying the improvement factor \(a\) after each preventive maintenance action in the instant \(T_{j}\), according to Equation (3).  
\[u(t)=\left\{\begin{array}{cc}u_{0}(t),&\text{T}_{0}\leq t<\text{T}_{1}\\ u_{0}(t-a\text{T}_{1}),&\text{T}_{1}\leq t<\text{T}_{2}\\ u_{0}(t-a\text{T}_{2}),&\text{T}_{2}\leq t<\text{T}_{3}\\ \vdots&\vdots\\ u_{0}(t-a\text{T}_{j-1}),&\text{T}_{j-1}\leq t<\text{T}_{j}\\ \end{array}\right. \tag{3}\]  
Therefore, in addition to estimating the parameters \(\lambda\) and \(\beta\), it is necessary to estimate the improvement factor that represents the influence of maintenance action on system failure intensity. In this paper, the improvement factor is defined as a function of the intervention level \(s\), as \(a(s)\), resulting in new expressions for the fault intensity function \(u(t)\) and the average number of failures \(\mu(t)\), according to Equations (4) and (5), respectively:  
\[u(t)=\frac{1}{\lambda^{\beta}}\cdot\beta\cdot(t-a(s_{j})\cdot T_{j-1})^{(\beta -1)},\ T_{j-1}\leq t<T_{j} \tag{4}\]  
\[\mu(t)=\sum\limits_{j=1}^{c-1}\left\{\left[\frac{1}{\lambda^{\beta}}\cdot(T_{ j}-a(s_{j})T_{j-1})^{\beta}-\frac{1}{\lambda^{\beta}}\cdot((1-a(s_{j}))T_{j-1}) \right]\right\} \tag{5}\]
# A Miniature Flapping Mechanism Using an Origami-Based Spherical Six-Bar Pattern
## 4 Experimental Results
### Thrust  
To check whether the proposed transmission properly generates the thrust or not, force measurement has been done. The data are collected through a load cell shown in Figure 11, sampled with the rate of 1000 Hz and filtered with the cut-off frequency of 100 Hz. The filtered data and the averaged data are given in Figure 12. The average thrust is measured as 2.34 gf (=22.9 mN) at the flapping frequency of 23 Hz.  
The thrust is successfully generated since the wing rotation angle is observed as we intended, which is shown in Figure 11B. For the wing rotation, a passive hinge has been applied to get an additional degree of freedom. The stoppers, however, have been added to prevent the excessive wing rotation. As a result, an angle of attack (AoA) of about 45\({}^{\circ}\) has been observed as shown in Figure 11B. This AoA maintains during both the upstroke and downstroke. Consequently, the thrust can be generated.  
Figure 11: Experimental setup. A load cell has been used to measure the thrust. The signal goes to the amplifier and the amplified data are logged to the PC.  
Figure 12: The measured thrust. The low-pass filtered data (solid line) and the average value (dashed line) are given.
# Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks
## 6 Conclusions  
In this paper, a novel CNN-based architecture is presented to accurately perform both defect detection and classification tasks for a metallic surface against complex industrial scenarios. Defect inspection is converted to the segmentation and classification problem based on the proposed method. The proposed CASAE module can transform a defect image to a pixel-wise prediction mask that contains only defective pixels and background pixels. To quickly obtain the defect category in real inspection environments, a compact CNN is presented. The IoU score of the inspection result of our method is 89.60% using the industrial dataset. The visual and quantitative experimental results have shown that our detection algorithm is sufficient to meet the requirements of the complex industrial  
Figure 9: Examples of CASAE-processed images with textured backgrounds. The first columns are examples of (**a1–a3**) defects images and (**b1–b3**) result images of defects marked in green.  
environment. Moreover, this generic method can be directly applied to the defect detection of other materials in industrial applications without much modification.  
One limitation of the proposed method is that the training of a deep network requires manually labeled data, which takes a lot of time and expense. In the future, our ongoing work will include reducing the labeling of data with semi-supervised learning, and the application of the proposed method to more real-world inspection problems such as the inspection of mobile phone screens.  
[PERSON] designed the algorithm, performed the experiments and wrote the paper. [PERSON] performed the image acquisition and prepared the ground truth images. [PERSON] and [PERSON] supervised the research. [PERSON] modified the paper.  
This research received no external funding.  
This work was supported by Science Challenge Project, No. TZ2018006-0204-02 and the National Natural Science Foundation of China under Grant 61703399, 61503376 and 61673383.  
The authors declare no conflict of interest.