esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Discussion  
### Experimental Setup  
To verify the feedforward control strategy of the clockwise operator model based on considering NCD characteristics proposed in this article, the hardware system of the optimized fourth-order actuation experimental platform mainly consists of a controller and multi-channel linear actuators.  
The functional testing platform is a one-dimensional piezoelectric actuation platform, whose function is completely equivalent to that of a lathe tool platform. A Renishaw XL-80 laser interferometer from England was selected as the displacement sensor. We also used theNational Instruments (NI) crio 9030 real-time controller from America. The entire experimental platform was built as shown in Figure 8.  
Figure 8: Experimental setup.  
Figure 7: Segmented clockwise operator and model: (**a**) Segmented clockwise operator. (**b**) Segmented clockwise operator model.  
The functional test platform utilizes a bolt and spring assembly to preload the piezoelectric actuator, with the addition of a guide block to ensure that the motion direction of the piezoelectric actuator remains consistent. A load platform is positioned between the piezoelectric actuator and the spring to secure the reflector, enabling displacement measurement. The specific functional test platform is illustrated in Figure 9. The piezoelectric actuator is driven by a linear power amplifier, and its displacement is measured using a laser interferometer.  
The piezoelectric actuator is fabricated from lead zirconate titanate (PbZrTiO\({}_{3}\), PZT) piezoelectric ceramic material, which exhibits a large piezoelectric charge coefficient, moderate dielectric constant, and high electromechanical coupling coefficient. Furthermore, the piezoelectric actuator (7 mm \(\times\) 7 mm \(\times\) 18 mm) is constructed by stacking multiple piezoelectric ceramic layers, and the input voltage is typically maintained as positive to preserve the actuator's performance.
## 4 Self-Healing Approaches
### Network Architecture
#### 4.2.8 Host Intrusion Detection System on IoT  
The host intrusion detection system (HIDS) is an approach for protecting IoT devices against threats. As noted by [37], each device performs this by installing detection agents. The alternative to HIDS is the network detection system (NIDS), which is the most common approach and more scalable because it does not require software installation on the IoT device. However, NIDS has limitations when compared with HIDS, such as the limited capability in its detection functionality, especially in situations where traffic on the IoT network is encrypted. A framework proposed by [37] can detect systems attacks in real-time and react to remediate the effects of such attacks. The framework is an automatic and collaborative host-based self-healing mechanism for IoT devices. The framework description involves using HIDS to protect a collective instance of IoT devices built into an IoT environment, such as a smart city. In a research study by [37], multiple IoT devices collaborate to train a deep-learning model. The best possible remediation is triggered once the HIDS issues an alert to the model, fulfilling the framework's self-healing functionality. The self-healing architecture proposed consists of three modules: HIDS, health monitoring, and auto-remediation. The HIDS module collects information from the IoT devices, analyses it, and determines if a threat could compromise the IoT device. The health-monitoring module is responsible for assessing the health state of the IoT device by collecting multiple data sources, such as memory usage, disk space, network metrics, etc. The auto-remediation module acts to remedy the effects of malfunction or intrusion of the IoT device.
## 2 Materials and Methods
### PelA\({}_{h}\) and PsGl\({}_{h}\) Stability--Differential Scanning Fluorimetry (DSF)  
Differential scanning fluorimetry employing the Prometheus NT.48 apparatus (NanoTemper Technologies GmbH, Munchen, Germany) was used to investigate the thermal stability of PelA\({}_{\text{h}}\) and PsGl\({}_{\text{h}}\) at given time points during storage at 4 \({}^{\circ}\)C as protein solutions or lyophilistates. The lyophilistates of PelA\({}_{\text{h}}\) and PsGl\({}_{\text{h}}\) were resuspended in distilled water at concentrations of 0.3 mg/mL. The PelA\({}_{\text{h}}\) and PsGl\({}_{\text{h}}\) in the solutions were used at concentrations of 0.3 mg/mL. The samples were then loaded into nanoDSF standard-grade capillaries and then heated from 20 \({}^{\circ}\)C to 95 \({}^{\circ}\)C at a rate of 1 \({}^{\circ}\)C per min. Protein unfolding was monitored by measuring the fluorescence intensity at two emission wavelengths of 330 nm and 350 nm. Analysis of the obtained data was performed using PRThermControl software (NanoTemper Technologies GmbH, Munchen, Germany), which allowed calculation of the proteins' melting temperatures (T\({}_{\text{m}}\)) based on the first derivative of the ratio of fluorescence intensity measured at 350 and 330 nm. Four independent measurements of T\({}_{\text{m}}\) were performed for each time point.
# An ECC-Based Anonymous and Fast Handover Authentication Protocol for Internet of Vehicles
## 6 Performance Analysis  
In this section, the proposed protocol will be compared with existing handover authentication schemes [28; 29; 30] in terms of computational cost and communication cost.
## 2 Results and Discussion
### Quality Assessment of Pigmented Rice  
The statistical analysis presented in the preceding sections focused on a comprehensive analysis of metabolic differences among pigmented rice cultivars associated with their color. However, these analyses do not provide information on the nutritional composition of individual rice cultivars. Therefore, we performed quality assessment to identify rice cultivars containing high amounts of bioactive compounds. SW 505 (BR) rice cultivar had the highest anthocyanin content of all samples (cyanidin-3-\(O\)-glucose, 742.65 \(\pm\) 15.69 \(\upmu\)g/g; penonidin-3-\(O\)-glucose, 32.65 \(\pm\) 0.73 \(\upmu\)g/g) (Table S9). The concentration of catechin and epicatechin was generally the highest in AM (RR) rice cultivars (catechin, 630.85 \(\pm\) 152.48 \(\upmu\)g/g; epicatechin, 29.11 \(\pm\) 1.40 \(\upmu\)g/g). The volatiles were most abundant in most BR cultivars (Table S3). Specifically, the levels of volatiles, such as hexanal (57.56 \(\pm\) 3.13 \(\times\) 10\({}^{8}\) area/g), nonanal (28.46 \(\pm\) 1.37 \(\times\) 10\({}^{8}\) area/g), 1-hexanol (164.37 \(\pm\) 5.47 \(\times\) 10\({}^{8}\) area/g), 2-heptanone (14.30 \(\pm\) 0.80 \(\times\) 10\({}^{8}\) area/g), and styrene (41.81 \(\pm\) 5.27 \(\times\) 10\({}^{8}\) area/g) were the highest in JSHC (Table S12). In addition, 2-acetyl-1-pyrroline, responsible for the major flavor preferred by the customer, was only detected in HJJ and HH (Table S12).
# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
## 5 Evaluation
### Simulation Results
#### 5.5.4 Effect of the Degree of Irregularity  
The degree of Irregularity (DOI) is the measurement of variation for range and direction of radio transmission. DOI is measured because the perfect circle of radio transmission that is assumed in the simulation does not represent the real value of radio transmission. The irregularity of radio range follows a normal distribution with mean \(r\) and standard deviation \(\sigma\) to allow greater variation [16]. Each sensor is assumed to have the same ideal radio range, \(r\). \(\sigma\) is used to determine whether the sender or receiver is within \(r\). The \(\sigma\) values used in the simulation are 0, 0.1, 0.2, 0.3, 0.4 and 0.5. Default DOI value tested on previous research is 0. A high value of DOI represents a high variation of the radio range transmission that may be caused by a high density of obstacle or climate changes in the real environment.  
Figure 9a,b shows that the average estimation error and the total closeness value of all methods are rapidly increased when the DOI value is above 0.2 and 0.3, respectively. However, the proposed IACS demonstrates the lowest average estimation error and the average closeness for every DOI compared to MSL* and LCC. This shows that IACS is more robust in the environment with a wider variation of radio signal transmission.  
Figure 9c tells the average computation cost is at the maximum when the value \(\sigma=2\), where each blind node has the most number of neighbors. The computation cost is at the highest when \(\sigma\geq 0.3\), where the localization error starts increasing. When \(\sigma\geq 0.4\), the DOI is too high that some nodes may not relate to any nodes. Thus, the nodes will not have sufficient valid samples for the localization and results in high estimation error as shown in Figure 9a. The IACS method requires the least computation cost compared with the other methods.  
As the number of neighbors in the range of blind nodes decreased, the communication cost and the energy consumption are decreased (Figure 9d,e). The proposed method requires the least communication and energy cost in different DOI.
# Sphingolipidoses and Retinal Involvement: A Comprehensive Review
## 4 Subtypes of Sphingolipidoses
### GM2 Gangliosidosis: Tay Sachs Disease and Sandhoff Disease  
#### 4.4.1 Disease Pathogenesis and Systemic Symptoms  
TSD and SD are lysosomal storage disorders caused by defects in the enzymes hexosaminidase A and hexosaminidase A and B, respectively. Both diseases lead to the accumulation of GM2 gangliosides at various levels, primarily in the central nervous system, causing neurodegeneration [105; 106].  
These diseases present with distinctive neurological and ophthalmic manifestations, with the retina being one of the most affected areas, characterized in most cases by the distinctive cherry red spot. This is one of the most significant and earliest ophthalmic signs attributed to ganglioside accumulation, which remains a key diagnostic indicator [62]. Thus, despite the primary neurological symptoms, ophthalmic manifestations are commonly  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Disease (Incidence)** & **Defective Enzyme (Mutated Gene)** & **Primary Storage Compound** & **Systemic Symptoms and Manifestations** & **Ocular Manifestations (Non-Retinal and Retinal Manifestations)** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the characteristics of GM1 gangliosidosis.  
observed and provide valuable clues for early diagnosis. Tables 4 and 5 show the systemic and ocular manifestations of SD (Table 4) and TSD (Table 5).
## 2 Working Conditions and Mathematical Modelling of Masts
### Optimization Models  
In this paper, the minimum cross-section size of the mast is taken as the objective function of optimization, i.e., the principle of minimum weight, and strength, stiffness, stability, and process requirements are taken as the constraints, and a penalty function is used for limitation. By adding a penalty term to the objective function, the constrained optimization problem is transformed into an unconstrained optimization problem. The penalty coefficient is generally set to 12,000 in structural optimization problems. The constrained optimization model of mast weight reduction is shown in Equation (1).  
\[\left\{\begin{array}{l}\min A(x)=x_{1}\times x_{2}-(x_{1}-x_{3})\times(x_{2} -x_{4})+A_{\text{invariant}};\\ s.t.S_{i}(x)\leq 0\ (i=1,2,3,4,5,6,7,8);\\ x=[x_{1},x_{2},x_{3},x_{4}]^{T};\end{array}\right. \tag{1}\]  
There are four variables to optimize, i.e., cross-section width \(x_{1}\), cross-section height \(x_{2}\), twice the web thickness \(x_{3}\) = 2 \(\times\)\(T_{1}\), and twice the wing thickness \(x_{4}\) = 2 \(\times\)\(T_{2}\). \(A\) represents the total cross-sectional area, and \(A_{\text{invariant}}\) represents the invariant cross-sectional area, such as the guide rail area, as shown in Figure 2.
## 3 Red Shift of HHG in Resonant Systems
### Pulse Duration Dependence of Red Shift in MHOHG  
The red shift in MHOHG is a nonadiabatic process as it depends on the pulse shape \(f(t)\) in Figure 2b. An important parameter in this process is the pulse duration in addition to the laser  
Figure 4: Population of the first excited state \(2\,p\sigma\) (solid black line) and the total ionization rate (dotted red line) as a function of time. The laser parameters are the same as those in Figure 2.  
Figure 3: Four-step model for MHOHG in the resonant molecular ion system HeH\({}^{2+}\). Step (1): the laser field pumps the electron from the ground \(1s\sigma\) state to the localized long lifetime \(2\,p\sigma\) excited state; part of the electron is ionized from the excited state in step (2), then accelerated by the laser field in step (3), and then transits back to the ground state to emit photons in step (4) when the laser field changes its direction. Part of the electron in the excited state will directly transit to the ground state in step (2*) when the laser field changes its phase. This leads to a strong resonance in the harmonic spectra. The Stark shift of the energy levels \(E_{1}\) and \(E_{2}\) is included [11]. \(E_{1}^{+}\) and \(E_{2}^{+}\) refer to the dressed energies with the field along \(+z\) direction, while \(E_{1}^{-}\) and \(E_{2}^{-}\) refer to the dressed energies with the field along \(-z\) direction.  
intensity. This nonadiabatic effect is strongly related to \(dI(t)/\,dt\). Shorter pulses enhance this effect, while this nonadiabatic effect will be less noticeable for longer pulses. To emphasize this effect, MHOHG spectra for harmonic order 40 to 42 in the plateau produced by different duration of laser pulses are illustrated in Figure 5a. The laser intensity and wavelength are kept the same as those in Figure 2. The corresponding time profiles of harmonic with order 40 are presented in Figure 5b. The nonadiabatic spectral red shift of MHOHG gradually decreases as the laser-pulse duration increases from 10 to 20 optical cycles. From the time profile analysis, the harmonic generation process is gradually shifted from the falling part of the pulse to the rising part in agreement with the above prediction.
# Offloading of Atomic Tasks in Satellite Networks:
## 3 Task Offloading Model
### Problem Formulation  
To simplify the overly complex problem, we assume that the transfer rates are the same between all low-orbiting and medium-orbiting satellites and the transfer rates are the same between all high-orbiting and low-medium orbiting satellites. Furthermore, all atomic computing tasks on the low-orbiting and medium-orbiting satellites can be transferred to the high-orbiting satellites. Meanwhile, we assume that there are no atomic tasks that cannot be handled by high-orbiting satellites.  
Meanwhile, we suppose that satellite networks can perceive each other and there is a visible time window between low-orbit and medium-orbit satellites, which links can be successfully established. Then, the time required to complete the task \(q\) execution on the satellite with the atomic task request in the initial state can be expressed as,  
\[c_{s}(t)=\varphi_{q}^{s}(t)T_{s,q}^{lm}+\Big{(}1-\varphi_{q}^{s}(t)\Big{)} \Big{(}1-\rho_{q}^{s}(t)\Big{)}T_{s,q}^{h} \tag{6}\]  
where \(c_{s}(t)\) denotes the time required for the task execution on the satellite \(s\) at time \(t\). Among them, \(1-\varphi_{q}^{s}(t)\) indicates that the task is offloaded and needs to be executed on a high-orbit satellite. Similarly, \(1-\rho_{q}^{s}(t)\) indicates that the task has been cached.  
The paper aims to scheme the execution order of all tasks and the satellite resource allocation scheme so that the time consumed by the execution of all tasks is minimized. The problem can be formally formulated as  
\[\begin{split}&\min_{s,q}\sum_{s\in\mathbb{S}}\sum_{q\in\mathbb{Q}_{s}}c _{s}(t)\\ & s.t.\\ & C1:T_{s,q}^{lm}\leq D_{s},\ \forall s\in S,q\in Q_{s},\\ & C2:\rho_{q_{t}}^{s}(t)\in\{0,1\},\\ & C3:\rho_{q_{t}}^{s}(t)\in\{0,1\},\end{split} \tag{7}\]  
As shown in Formula (7), \(C1\) indicates that the atomic task needs to be computed by the deadline \(D_{s}\), \(C2\) indicates the range of values for the atomic task caching decision, \(C3\) indicates the range of values for the atomic task unloading decision.
# Validating an Operational Flood Forecast Model Using Citizen Science in Hampton Roads, VA, USA
###### Abstract  
Changes in the eustatic sea level have enhanced the impact of inundation events in the coastal zone, ranging in significance from tropical storm surges to pervasive nuisance flooding events. The increased frequency of these inundation events has stimulated the production of interactive web-map tracking tools to cope with changes in our changing coastal environment. Tidewatch Maps, developed by the Virginia Institute of Marine Science (VIMS), is an effective example of an emerging street-level inundation mapping tool. Leveraging the Semi-implicit Cross-scale Hydro-science Integrated System Model (SCHISM) as the engine, Tidewatch operationally disseminates 36-h inundation forecast maps with a 12-h update frequency. SCHISM's storm tide forecasts provide surge guidance for the legacy VIMS Tidewatch Charts sensor-based tidal prediction platform, while simultaneously providing an interactive and operationally functional forecast mapping tool with hourly temporal resolution and a 5 m spatial resolution throughout the coastal plain of Virginia, USA. This manuscript delves into the hydrodynamic modeling and geospatial methods used at VIMS to automate the 36-h street-level flood forecasts currently available via Tidewatch Maps, and the paradigm-altering efforts involved in validating the spatial, vertical, and temporal accuracy of the model.  
hydrodynamic; modeling; sea level rise; mobile application; app; crowdsourcing; SCHISM; Tidewatch; StormSense; _Catch the King_
## 4 Methodology
### Measurement Model Assessment
#### 4.3.6 Discriminant Validity  
Discriminant validity refers to the extent to which a construct is truly distinct from other constructs [63]. To assess the construct discriminant validity, two common criteria, AVE analysis and the correlation coefficient are applied as follows: (1) the square root of AVE for each construct should be greater than the correlations of the construct with any of the other constructs [79], and (2) for the correlation coefficient, discriminate validity is satisfied if the correlation coefficient for any two constructs is less than 0.90 [63; 79]. It is worth noting that the second criteria can be used to check whether multicollinearity among variables or constructs is said to exist [70; 79]. Therefore, confirming the discriminate validity of a model remedies the multicollinearity problem as well.
## 4 Simulation and Results Analysis  
The control structure shown in Figure 1 was used as the simulation structure, and the circuit structure shown in Figure 2 was taken as the simulated structure of the inverter circuit model. The FOA offline optimization population scale was 100, and the online self-tuning was 20; the number of iterations was 100; the simulation time was 1 s; the sampling time was 50 \(\times\) 10\({}^{-6}\) s; the analog output power of the active power was 8 kW and the reactive power was 3 kVar; the output voltage was 220 V; and the frequency was 50 Hz. Offline optimization and online self-tuning optimization were performed for the inverter control parameters through analog simulation. Finally, the IAEs of the two optimizations in the step-response process were compared to verify the optimization effects.
# Developing Productivity-Safety Effectiveness Index Using Data Envelopment Analysis (DEA)
## 2 Background
### Methodological Background
#### 2.2.2 Data Envelopment Analysis (DEA)
The types of DEA model are twofold: the CCR model (coined from the first letters of the authors' names), which assumes production with constant returns to scale), and the BCC model (also coined from the first letters of its authors' names) for cases involving variable returns to scale. When considering the nonlinear relationship between input and output, the BCC model has been widely applied in numerous studies [27,28]. The DEA models are also divided according to the purpose of the model: maximize outputs (output-oriented) or minimize inputs (input-oriented). For the function, the efficiency is represented as Equation (1):  
\[\text{Efficiency}=\frac{\sum_{k=1}^{n}u_{k}\cdot y_{kj}}{\sum_{i=1}^{m}v_{ i}\cdot x_{ij}}, \tag{1}\]  
where  
\(y_{kj}\): amount of output \(k\) produced by DMU \(j\);  
\(x_{ij}\): amount of input \(i\) used by DMU \(j\);  
\(u_{k}\): weight assigned to output \(k\);  
\(v_{i}\): weight assigned to input \(i\);  
\(n\): number of outputs;  
\(m\): number of inputs;  
and the objective function and constraints of the CCR model of DEA are formulated as Equation (2):  
Figure 1: Relative efficiency: concept of the DEA. \[\min z=vx_{0}-v_{0}.\]  
Subject to \(uy_{0}=1\),  
\[vX-uY-v_{0}e\geq 0, \tag{2}\] \[v\geq 0,u\geq 0,v_{0}\;is\;a\;free\;sign.\]  
In Equation (2), \(z\) is the efficiency score of each DMU, and \(v\) and \(u\) are the vectors of the weights in constraints given to the \(x_{0}\) values of the inputs and the \(y_{0}\) values of the outputs in the objective function, respectively. In addition, all DMUs' inputs and outputs are denoted as a vector space of X and Y, respectively [22; 28]. Also, \(v_{0}\) is the scalar associated with e \(\lambda=1\), where \(\lambda\) is the vector of the intensity variables. The first constraint means that the total efficiency is equal to 1 (or sometimes represented as 100%). The second constraint indicates that the efficiency is expressed by the output over the input. The decision variables of the problem are the values of the weights \(v\) and \(u\), which are chosen by assigning the best set of weights to each DMU [22; 28; 29]. Many studies have reviewed the applications of the DEA approach to identify the relative efficiency of units with multiple inputs and outputs as summarized in Table 2. The efficiency of activities of each topic is performed based on DEA at the national level, industry level, and corporate/project level. However, safety-related topics based on productivity have not been studied and this topic focuses on this gap.
## 3 Applications  
The multilateration method was investigated in MATLAB simulations by considering different source distances, sampling frequencies, arrival angles, and microphone spacings. In the simulations, a microphone array consisting of five microphones, one in the center and four arranged in a circle, was used. The Equation (13) was arranged for five microphones by considering the center microphone as the reference. The model we created for the TDoA time between two microphones in the simulations is shown in Figure 4.  
The TDoA model specifies that the \(N_{i}\) and \(N_{r}\) values represent the number of samples that will be formed until the sound reaches the \(i\). and the reference microphones, respectively, after it is formed. \(N_{err}\) is the amount of error that the GCC-Phat algorithm will produce due to environmental noise added to the source sound in real-time applications. The time \(\Delta t\) is the unmeasured time at which the sound arrives at the microphones, which, depending on the sampling frequency, is less than the sampling time \(t_{s}\). \(\Delta t_{i}\) and \(\Delta t_{r}\) are the unmeasured times at which the sound reaches microphone i and the reference microphone, respectively. In our simulation study, the sound source, which is assumed to be \(150\) m away, was examined at five different angles of \(0^{\circ}\), \(-30^{\circ}\), \(-45^{\circ}\), \(-60^{\circ}\), and \(-90^{\circ}\). The distance \(d_{m}\) between the microphones was increased from \(0.5\) m to \(3\) m in \(0.5\) m steps. In the simulation study, the sampling frequency \(Fs\) was set to \(44.1\) kHz. In Figure 5, the LSE solutions obtained by solving Equation (15) for each \(d_{m}\) distance are shown as \"*\", and the actual source position is shown as \"o\". It can be observed that the location estimates approach the actual location value as the microphone spacing is increased.