esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Implemented Controllers
### Type-1 Fuzzy PID Controller  
In this section, the general structure of the T1-FPID is presented. Before starting T1-FPID, the fuzzy PID control block for T1 and IT2 is shown in Figure 3[35]. The position error (\(e\)), and its rate of change (\(\Delta e\)) are shown as controller inputs [8; 9].  
As shown in Figure 3, the input scaling factors (SFs) are denoted as \(K_{e}\) and \(K_{d}\), while the output scaling factors (SFs) are denoted as \(K_{0}\) and \(K_{1}\). The equation is in the form  
\[E=K_{e}e,\quad\Delta E=K_{d}\Delta e, \tag{8}\]  
where \(E\) and \(\Delta E\) denote the FLC inputs. The output of the FLC (\(U\)) is transformed to the actual control signal of the T1 or IT2 fuzzy PID controllers (\(u\)) as follows:  
\[u=K_{1}U+K_{0}\int Udt. \tag{9}\]  
The employed fuzzy logic controller (FLC) is designed with \(3\times 3\) rules as given in Table 1. The consequent part of the rules is described with crisp singletons. Moreover, they are represented with five linguistic fuzzy terms, which are defined as follows: PB--positive big; PM--positive medium; Z--zero; NM--negative medium; and NB--negative big.  
In Figure 4, the membership functions (MFs) of the T1-FPID are presented for the UAV [8; 9]. There are three MFs in T1-FPID, N, Z and P abbreviated from \"negative\", \"zero\", and \"positive\", which are the linguistic fuzzy variables. The antecedents are defined with uniformly distributed symmetrical triangular MFs. In this study, triangular MFs are selected instead of Gaussian or other types of MFs. Triangular MFs provide a fast computational time and real-time implementation [7]. Moreover, the MFs values are determined based on empirical evaluations. The resulting control surface is shown in Figure 4. The implemented FLC uses the product implication and operates the centre of sets defuzzification method [36; 37; 38]. In this study, the fuzzy weighted average defuzzification method is used, and the equation is written as Equation (10):  
\[U=\frac{\sum_{i=1}^{M}y^{i}f^{i}}{\sum_{i=1}^{M}f^{i}} \tag{10}\]  
where \(U\) is the final output of the Takagi-Sugeno-Kang fuzzy inference system [39], \(f\) is the firing function of T1-FPID, \(y\) is denoted as consequent MFs, and \(M\) is the total number of rules. Table 1 is used for T1-FPID.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(E/\Delta E\) & \(\mathbf{P}\) & \(\mathbf{Z}\) & \(\mathbf{N}\) \\ \hline \hline \(\mathbf{P}\) & PB:1 & PM:0.65 & Z:0 \\ \hline \(\mathbf{Z}\) & PM:0.65 & Z:0 & NM:–0.65 \\ \hline \(\mathbf{N}\) & Z:0 & NM:–0.65 & NB:–1 \\ \hline \end{tabular}
\end{table}
Table 1: The rule table of T1- and IT2-FPID controller.  
Figure 3: Illustration of the T1- and IT2-FPID controller.
# A Survey on Recent Advanced Research of CPS Security
## 3 Reviews
### Overprivileged Permission Detection  
Overprivileged permission means the system declares extra permissions but does nothing within its function. It is unobtrusive but can lead to information leakage or more severe attacks. Recently, researchers have proposed frameworks to detect this problem in sensors, apps, etc.  
[PERSON] et al. [18] proposed Orpheus, an anomalous detection framework for data-oriented attacks, which is stealthy because it does not corrupt the program's control flow. To detect abnormal control behaviors caused by data-oriented attacks, they proposed an event-aware finite-state automaton (eFSA) model at the system call level. The authors evaluated their prototype in three real-world cases with data-oriented attacks, and the performance showed that the time overhead is negligible for state transition integrity checking.  
Current smart devices leverage various sensors to provide friendly service; nevertheless, attackers can also gather user privacy data or transfer malware by just accessing the generic sensor API. In this paper, 6 thSense [30] was proposed; it is a context-aware intrusion detection system. 6 thSense creates a contextual model to monitor sensor data changes from different tasks and identify harmless and malicious sensor behaviors. It utilizes three different machine learning models (Markov chain, Naive Bayes, and LMT) to detect malicious behavior associated with sensors. After an evaluation on data from over 50 real users, 6 thSense was proven to be robust against three sensor-based threats in smartphone scenarios (malicious triggers, information leakage, and data theft) with higher accuracy and lower overhead.  
Event sensors play a critical role in smart homes, but the data from these sensors are not always reliable, leading to event spoofing. The goal of Peeves [29] is to learn smart home event signatures for various sensing modalities to protect against event sensor faults and complicated attackers. It recognized 22 event types with 48 physical sensors and achieved excellent classification results for 9 out of 22 events without false alarms.  
Apps are widely used to control devices in smart home systems (SHSs). Nevertheless, current security mechanisms do not consider user activities and sensor-device-user interactions holistically to detect malicious behaviors. Therefore, [PERSON] et al. proposed Aegis [31], a context-aware security framework for smart home systems to identify malicious and benign behaviors. The authors evaluated Aegis's efficacy and performance in three real-life environments and measured Aegis' security against five different malicious behaviors. The results show that Aegis can detect bad behavior in SHSs with over 95% accuracy and secure SHSs with all kinds of smart home layouts, device configurations, installed apps, and user policies.
# An LDPC-RS Concatenation and Decoding Scheme to Lower the Error Floor for FTN Signaling
###### Abstract  
Faster-than-Nyquist (FTN) signaling has attracted increasing interest in the past two decades. However, when the fifth-generation (5G) communication low-density parity check (LDPC) code is applied to FTN signaling with low Bahl-Cock-Jelinek-Raviv (BCJR) states of detection and few turbo equalization iterations, an error floor near \(10^{-5}\) is found, which does not exist in the original LDPC used for orthogonal signaling. This can be eliminated through many detection and decoding iterations, but this is unacceptable considering the increase in latency and storage. To solve this problem, we propose an LDPC and Reed-Solomon (RS) concatenation code, shortening, and perturbation scheme to lower the error floor. We propose a parallel encoder architecture for RS component code and a concise algorithm to calculate its constant multiplier coefficients, leveraging a traditional serial encoder, which can also be used for other parallelisms, rates, and lengths. The simulation results show that the proposed concatenation and shortening scheme can lower the error floor to about \(10^{-7}\). The proposed scheme has an error correction capability for coded FTN signaling and successfully lowers the error floor with the limitation of few turbo iterations and few BCJR states.  
LDPC; RS; error floor; perturbation decoder; FTN signaling Article  
An LDPC-RS Concatenation and Decoding Scheme to Lower the Error Floor for FTN Signaling  
1  
[PERSON]; [PERSON]; [PERSON] An LDPC-RS Concatenation and Decoding Scheme to Lower the Error Floor for FTN Signaling  
1  
H.; [PERSON]; [PERSON] An LDPC-RS Concatenation and Decoding Scheme to Lower the Error Floor for FTN Signaling  
1  
H.; [PERSON]; [PERSON] An LDPC-RS Concatenation and Decoding Scheme to Lower the Error Floor for FTN Signaling  
1
# Transfer Learning Based on Clustering Difference for Dynamic Multi-Objective Optimization
## 3 Proposed TCD-DMOEA
### Computational Complexity Analysis  
This section analyzes the computational complexity of TCD-DMOEA at one iteration. According to Algorithm 2, the main calculation of TCD-DMOEA comes from the following aspects. (1) The complexity of the target domain preprocessing process mainly lies in the use of K-means clustering and first-order difference to predict the non-dominated solution at the next moment. K-means clustering requires \(O(\textit{Inkm})\) calculation, where \(m\) is the number of element fields, \(n\) is the amount of data, \(I\) represents the number of iterations, and \(k\) is the number of clusters. Generally, \(I\), \(k\), and \(m\) can be considered as constants, so the computational complexity can be simplified to: \(O(n)\). The first difference requires \(O(n)\) computation. The computational complexity of the target domain preprocessing stage is \(O(n)\). (2) The transfer learning stage uses SVM as the basic classifier, and the SVM classifier costs \(O(N\!S^{2}\!d)\) to obtain a strong classifier, where \(S\) is the overall size, \(N\) is the number of iterations, and \(d\) is the dimension of the decision variable. To sum up, the computational complexity of TCD-DMOEA in this work is \(O(N\!S^{2}\!d)\).
# SingMonitor: E-bike Charging Health Monitoring Using Sound from Power Supplies
## 3 Background
### E-Bike Charging Generates Unique Power Factor Correction Signals  
In this section, we explain why e-bike charging can generate unique PFC signals.  
**Power factor (PF):** In electrical engineering, the power factor is defined as the ratio between real power consumed by a load and the total real-plus-reactive power, which is also known as the total apparent power [43], i.e., \(PF=P_{real}/P_{apparent}\). Figure 3 shows that PF can reach 1 when the input current can follow the instantaneous line voltage without distortion. This means that the total apparent power is the lowest while consuming the same real power, thus realizing the most efficient use of electrical energy. In other cases, PF is less than 1, meaning electrical energy is wasted in the distribution system.  
**Power factor correction (PFC):** PFC circuits [44] can increase PF to reduce power losses by eliminating high harmonics through low-pass filters or by shaping the input current. Therefore, PFC circuits are required in many types of electrical appliances according to  
Figure 2: 3-stage charging.  
the IEC61000-3-2 standard [45]. The PFC circuit in e-bike chargers is implemented by a pulse width modulator (PWM). A PWM modulates current by periodically generating high-frequency ripple to make the input current follow instantaneous line voltage. The modulation of the input current by PFC circuits injects feedback currents into the grid, i.e., PFC signals. The research [35] shows that load power consumption information can modulate the amplitude and frequency of PFC signals. The features of PFC signals can also be affected by the design and manufacturing process of PFC circuits. Therefore, as shown in Figure 4, there are obvious differences in the spectrum of the PFC signals generated by different e-bikes, appliances, and charging stages of the same e-bike. Because of the difference between PFC signals, _SingMonitor_ can accurately analyze whether e-bikes are charging and which charging stage they are in from the mixed PFC signals generated by multiple appliances.
# Calibration and Image Reconstruction in a Spot Scanning Detection System for Surface Defects
## 3 System Calibration and Image Reconstruction Methods
### System Calibration Method
#### 3.2.1 Extraction of Feature Sequence Number Sets  
The discrete intensity sequence acquired by the SS-SDES is one-dimensional. For each intensity signal in the sequence, although its magnitude can be used to roughly distinguish whether it is a defect signal, directly determining the location and characteristics of the defect to which the signal belongs is not possible. Hence, for the extraction of the sequence numbers corresponding to specific defects on the calibration plate, an image is first reconstructed using the ideal mapping relationship \(\mathbf{g}_{\Phi_{0}}\), called the original image. Then, image processing techniques are performed to find out the specific scratches and distance-marked dot pairs and to subsequently extract their feature points, which indicate the centroid for digs and the points on the skeleton for scratches. Finally, the sequence numbers corresponding to these feature points are obtained through a backtracking strategy: the discrete intensity sequence is traversed again to calculate the image coordinate for each sequence number; if the distance between the coordinate and any a feature point is less than 0.5 pixel, the sequence number is considered to correspond to this feature point. In addition, multiple groups of scratches and distance-marked dot pairs are selected to make the feature points locate at every part of the image as much as possible. Let \(M^{l}\) be the number of selected scratches and \(M^{k}\) be the number of selected distance-marked dot pairs. The obtained feature sequence number sets associated with them can be expressed by Equations (8) and (9), respectively:  
\[L_{m}:\left\{n_{m,l}^{l}\big{|}q=1,2,\cdots,Q_{m}\right\}\text{, }m=1,2,\cdots,M^{l} \text{,} \tag{8}\]  
\[K_{m}:\left\{n_{m,1}^{k},n_{m,2}^{k},D_{m}\right\}\text{, }m=1,2,\cdots,M^{k} \text{,} \tag{9}\]  
where \(n\) is the sequence number, and \(Q_{m}\) represents the number of sequence numbers in the \(m\)th scratch, and \(D_{m}\) represents the actual distance between the \(m\)th distance-marked dot pairs.
## 4 Performance Evaluation
### Remaining Energy Power of the Sensor Nodes for Data Communication  
To compare the impact of moving sinks on the location, we report the data communication overhead of SDLS, TTDD, and ALS except SLS, because SLS does not mention supporting mobile sinks after the location service. Figure 8 shows the remaining energy power of the sensor nodes for data communication. For the experiment, we assume the following parameter settings: number of sinks and source nodes = 30, number of sensor nodes = 400, size of the sensor field = 600 x 600 m\({}^{2}\), transmission radius = 50 m, initial energy = 0.1 J, sink speed = 10 m/s, and the source node generates data packets 30 times. The data communication overhead is the highest with ALS. TTDD consumes less overhead than ALS, because TTDD can find a new path by switching to a new grid node. However, this is not the shortest path. Therefore, SDLS provides the best performance using the LSR protocol.  
Figure 7: Location response time from anchor system.
## 2 Electricity-Gas-Heat Integrated Energy System Model
### Constraints
#### 3.4.2 Natural Gas Network Constraints  
The natural gas network model mainly includes three parts: gas source point, pipeline and pressure station (compressor). Natural gas is supplied through the gas source point, and then transmitted and distributed to the load through the pipeline. The pressure station can improve the node pressure and reduce the probability of natural gas clogging when the gas load peaks [10].  
(1) Gas source point  
The natural gas in the gas network is injected by the gas source point, and the gas flow output of each gas source point has upper and lower limits:  
\[S_{\text{gas},s}^{\text{min}}\leq S_{\text{gas},s,t}\leq S_{\text{gas},s}^{ \text{max}} \tag{25}\]  
where \(S_{\text{gas},s}^{\text{min}}\) and \(S_{\text{gas},s}^{\text{max}}\) are, respectively, the minimum and maximum output flow of natural gas at the gas source point \(s\).  
(2) Natural gas pipeline  
For the ideal adiabatic gas pipeline \(m\) and \(n\), the beginning and end points are \(m\) and \(n\), respectively, and the steady-state flow rate can be expressed as:  
\[Q_{mn,t}\ =\ D_{mn,t}C_{mn}\sqrt{D_{mn,t}-(\psi_{m,t}^{2}-\psi_{n,t}^{2})} \tag{26}\]  
\[D_{mn,t}\ =\ \left\{\begin{array}{ccc}1&,&\psi_{m,t}\geq\psi_{n,t}\\ -1&,&\psi_{m,t}\leq\psi_{n,t}\end{array}\right. \tag{27}\]  
where \(Q_{mn,t}\) is the natural gas flow through pipeline mn at time \(t\), \(C_{mn}\) is related technical parameter of pipeline, \(\psi_{m,t}\), \(\psi_{n,t}\) are the pressure values of the source points \(m\) and \(n\) at time \(t\). \(D_{mn,t}\) is the transmission direction of natural gas in the pipeline at time \(t\), \(1\) is the positive direction, \(-1\) is the negative direction.  
Upper and lower limit constraints of pipeline flow:  
\[Q_{mn}^{\text{min}}\leq Q_{mm,t}\leq Q_{mn}^{\text{max}} \tag{28}\]  
where \(Q_{mn}^{\text{max}}\) and \(Q_{mn}^{\text{min}}\) are, respectively, the upper and lower limits of natural gas flow through pipeline \(mn\).  
Upper and lower limit constraints on pressure of each node:  
\[\psi_{m}^{\text{min}}\leq\psi_{m}\leq\psi_{m}^{\text{max}} \tag{29}\]  
where \(\psi_{m}^{\text{max}}\) and \(\psi_{m}^{\text{min}}\) are the upper and lower limits of pressure of node \(m\), respectively.  
(3) Compressor  
The pressurizing station increases the pressure of the natural gas pipeline in which it is located. The most important part is the compressor. Ignoring the consumption characteristics of the compressor, the pressure relationship between the two ends of the compressor is as follows:  
\[\psi_{m}\ =\ \rho_{c}\psi_{n} \tag{30}\]  
where \(\psi_{m}\) and \(\psi_{n}\) are the pressure at the outlet and inlet of the compressor, respectively, \(\rho_{c}\) is the compression ratio of the compressor.  
(4) Natural gas flow balance  
According to the law of conservation of flow, the flow balance equation of each node in the natural gas network can be listed as follows:  
\[\sum_{n\in m}Q_{mn,t}+Q_{GT,m,t}+Q_{CHP,m,t}+Q_{load,m,t}\ =\ S_{m,t}+Q_{PEG,m,t} \tag{31}\]  
where \(n\)\(\in\)\(m\) is all nodes adjacent to node \(m\), \(Q_{GT,m,t}\) is
# Framing Algorithm-Driven Development of Sets of Objectives Using Elementary Interactions
## 4 Platform Concept
### Model Aggregator  
A characteristic of the platform is the continuing development process while the MCDA application is already capable of supplying an assessment result. This leads to the question, \"At which point in the development can such a model be considered as stable?\" It is suggested to introduce various attributes, each describing a validity measurement of an element. First, the attribute, _validity_, accumulates the element's validity. It determines, for example, if the name of the element is reasonable. Further, the attribute, _validityStructure_, holds a measure of the correct structural position of the element, i.e., if the element is located correctly in the SOO structure. Another measurement of validity can be the attribute, _validityChildren_, which is a measure of the stability of the subordinated elements, e.g., if those elements define a complete set and are mutually independent.  
The values of validity-describing attributes are continuously updated by EIs, which affect the related elements. For example, if multiple users name the same criteria via EI _Name_, (e.g., _direct costs_), the validity of the element (represented by the attribute _validity_) is increased with each mention. The confirmation of an element (EI _Confirm_) increases the value of this attribute, whereas a rejection decreases it.  
In general, the question of validity occurs on at least three levels. The first level is elements; the validity of an element is indicated by the attribute _validity_. The next level is made up of groups, which are the subordinated elements of a parent element, e.g., the criteria that belong to an objective. Group-based validity increases if EIs of the category, _Structure_, do not result in changes; each negation of an EI _Find Duplicates_ increases the validity, and each confirmation decreases it again. The children's attributes, _validityStructure_, contribute to the _validityChildren_ attribute. The third level consists of tiers. There are two tiers. The first one is the hierarchy of objectives, criteria, and indicators; the second one is given by the weights of objectives and criteria. The determination of the weights is reasonable only when the underlying first tier has been captured in a milestone, i.e., when it is no longer subject to changes. The decision, when the design of the first tier is completed, can be made by the platform automatically, dependent upon the tier's _validity_ attributes. When the average validity of the elements has reached a threshold, an SOO milestone is created. Thereafter, the step of determining weights is based on this milestone version of the SOO.  
Formulas to calculate the values of validity attributes have to take into account certain stipulations; an element's validity can be considered stable when its value does not change significantly during the last affecting EIs.  
In the following section, a validation study of two core components of the platform is described.
# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 3 Key Challenges in AI for Constrained Devices
### Reliability and Fault Tolerance  
Edge devices may operate in harsh or unpredictable conditions, such as extreme temperatures, vibrations, or exposure to moisture, which can degrade hardware components and sensors over time [66]. Ensuring consistent AI performance in these environments demands robust hardware design, but it also relies on fault-tolerant software that can handle partial failures gracefully. For instance, sensor readings may become noisy or intermittent, yet the AI system must still produce reliable inferences [29]. Traditional machine learning models trained on clean, consistent datasets may falter in such real-world conditions.  
In response, developers employ redundancy strategies, sensor fusion, and error-correcting techniques to mitigate hardware-induced variability [67]. At the software layer, models can be trained with augmented data that simulate potential noise or missing channels, improving resilience during inference [68]. Another approach is to integrate continual learning methods that periodically retrain or fine-tune models in situ, helping the system adapt to evolving environmental conditions or device aging [9]. These strategies, while effective, add additional layers of complexity that must be balanced against already-constrained resources.