esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### General Classes of Scheduling Problems
Nevertheless, these usually follow one of three basic types of formulations. In this context, [PERSON] and [PERSON] present an overview of these base formulations and compare different implementations against each other, concerning their optimality and CPU time [31]:  
**Time-Indexed formulation:**: These types of models index the generated schedules over discrete time instances, e.g., minutes or hours. Therefore, the schedules denote for each machine and each time instance, which operation of which job is currently processed. The drawback of these formulations is the high number of decision variables, which is at least quadratic over the number of machines and time instances. According to [PERSON] and [PERSON] [31], a significant drawback of these formulations is that the planning horizon must be estimated in advance so that the optimizer can schedule all operations. **Sequence-Position formulations:**: This type of formulation uses discrete slots instead of time instances, which refer to semantic concepts, e.g., to machining operations. Therefore, it represents each machine by a sequence of slots, assigning operations to them. This type of formulation closely resembles a time-indexed formulation but reduces the number of decision variables by summarizing these into discrete slots of equal length in time. For example, if a machine performs three operations of lengths 2 h, 2.5 h, and 3 h, a formulation of this class would most probably use slots of 3 h length. As a trade-off, this abstraction reduces the resolution of the model (for the example above by factor 3 compared to an hourly time-indexed model) but may lead to weaker solutions, e.g., if operations finish in less time than assigned to a sequencing slot. These formulations require an a-priori estimation of the number of slots to allow the optimizer to schedule all operations. Consequently, this class summarizes all indexed formulations, which do not focus on single time instances but higher-level concepts. This reduces the complexity and simplifies the formulation of semantic constraints, e.g., like not assigning a morning shift directly after a night shift. **Precedence-based formulations:**: In contrast to the formulations above, precedence-based models use a continuous representation of time. Therefore, the generated solutions are indexed over the number of operations to be performed, usually resulting in fewer decision variables. The schedules denote start or end times, and the assigned machine for each operation. In addition to the formulation itself, the environment poses an essential factor for the choice of approaches. Therefore, several authors, e.g., [28; 32], include the differentiation into static and dynamic JSSPs. In static scenarios, all variables and conditions are known a-priori. In contrast, dynamic scenarios include various kinds of dynamics like, e.g., variable processing times, machine breakdowns, or rush orders.
## 3 Damage Detection Problems and Possibilities
The red dotted lines in the figure separate the RMSD values from a stack of five metal layers, to a single metal layer (first nine dots are measured from a stack with five layers, the next 10 dots are from a stack with four layers, and so forth). Here, there is a clear difference in the RMSD values for the different number of layers, where five layers (0.75 mm) result in values of 2%-4%, four layers in 4%-7%, three layers in 6%-10% (except for the 23 rd measurement), two layers in 10%-14%, and one layer in 12%-18%. Thus, for this metal stack case, one can first acquire the impedance signature at five layers for use as a reference signature, then detach the PZT device for it to be used again in the future. When the stack experiences reduction in thickness, the PZT device should be attached to the same spot for measuring the impedance signature again. If the stack thickness has reduced to only two layers, a high possibility exists for the RMSD value to be within the range of 10%-14%. In addition, one can increase the accuracy of the result by simply measuring the impedance signature multiple times before averaging the RMSD values, as a single measurement can result in an unexpected RMSD value, leading to a wrong prediction. Based on this finding, it becomes possible for one to identify thickness reduction of structures subjected to corrosion such as bridges, buildings, and offshore structures, by using a drone equipped with a re-attachable PZT device.
# Blind Recognition of Forward Error Correction Codes Based on a Depth Distribution Algorithm
## 1 Introduction
Aiming at FEC codes in a non-cooperative communication system, a recognition method of code type based on depth distribution algorithm is proposed in this paper. Compared with the traditional matrix rank analysis algorithm, the anti-noise performance of this method is significantly improved and the recognition effect is better. 2. According to the code type characteristics of convolutional codes and Turbo codes, the code reconstruction algorithm is adopted to convert different codes into linear block codes, making it possible to identify them with the same algorithm. 3. To recognize the code length and starting bit of linear block codes, an improved matrix analysis method based on Gaussian elimination is adopted in this paper. By establishing decision thresholds for different distributions in the normalized columns of the analysis matrix, and randomly exchanging matrix rows and repeating operations, the accuracy of algorithm identification is effectively improved. The remaining contents are organized as follows: Section 2 presents the system model and recognition process. In Section 3, an overview of depth distribution algorithms are detailed. In Section 4, we introduce the recognition algorithms of three different coding types. In Section 5, various simulation results are provided to confirm the validity of the proposed schemes and in Section 6, conclusions are drawn.
## 3 Classification Method
### Conventional Classification  
In this section, we present the details of conventional classification. Twelve types of handcraft features, including shape, color, texture, and frequency, were adopted for handcraft feature extraction and the SVM method was adopted for classification.  
For the shape features, we extracted speeded-up robust features (SURF) [8], scale-invariant feature transform (SIFT) [9], dense SIFT [10], histogram of oriented gradients (HOG) [11], co-occurrence HOG (CoHOG) [12], extended CoHOG (ECoHOG) [13], and local binary pattern (LBP) [14]. SURF and SIFT comprised of two algorithms of feature point detection and feature description. Feature point detection is robust with respect to the changing scale and noise, and feature description is robust with respect to illumination change and rotation. Dense SIFT uses a grid pattern for feature points and denotes the same feature description as that denoted by SIFT. An example of the detected key point is depicted in Figure 5. The features extracted from SURF, SIFT, and dense SIFT were respectively clustered using k-means and vector quantization, conducted using a bag-of-features [15]. HOG is a histogram of the luminance gradient, and CoHOG is a histogram of the appearance frequency of a combination of the luminance gradients. ECoHOG is a histogram of the intensity of a combination of the luminance gradients. The LBP transforms into an LBP image that is robust to illumination change and exhibits its luminance histogram.  
As the color features, we calculated color histograms in each colorimetric systems of red, green, blue (RGB), hue, saturation, value (HSV), and L * a * b * [16]. The RGB color system uses a simple concatenation of R, G, and B channel histograms. HSV is a color feature that uses the HSV color specification system; when the color saturation is low in the HSV color system, the hue value becomes unstable. Therefore, the histogram of each channel excludes the value when the saturation value is 10% or less. L * a * b * is a color feature that uses the L * a * b * color system and a three-dimensional histogram of L *, a *, b *.  
Figure 4: System flow.  
Figure 5: Visualization of keypoint detection: (**a**) Speeded-up robust features (SURF), (**b**) scale-invariant feature transform (SIFT), and (**c**) dense SIFT.  
For the texture features, we calculated a co-occurrence matrix feature (GLCM: gray level co-occurrence matrix). We used contrast, dissimilarity, homogeneity, energy, correlation, and angular secondary moment as the statistical values of GLCM [17].  
For the frequency features, we utilized GIST [18], which is characterized by imitating human perception. GIST divides the image into small \(4\times 4\) blocks and extracts the structure of the entire image using filters with different frequencies and scales.  
Classification of the mosquito species was conducted using SVM and the aforementioned features. SVM considered a soft margin, and multiclass classification was performed using a one-versus-rest method.
## 5 Error Behavior of the Reliability Estimation  
This section studies the error-related properties of the proposed method, and Section 5.1 investigates the influence of the interval variables' distributions on the error of reliability estimation and Section 5.2 studies the convergency of error with respect to failure probability.
## 5 Analysis Cases  
In the case of a tank without baffles, the natural sloshing frequency closely matches the theoretical value derived from established formulas. Specifically, when the theoretically calculated natural frequency was used to model the tank, sloshing behavior was clearly observed, validating the theoretical approach. However, the situation becomes more complex when baffles are introduced. The presence of baffles alters the flow dynamics and, consequently, the sloshing frequencies, making it more challenging to predict the exact value of the natural frequency.  
To investigate the impact of baffles on sloshing frequency, a detailed case study was conducted. This study considered a tank with one vertical baffle and a tank with two vertical baffles. Additionally, the baffle length was varied relative to the water depth, with lengths set at 25%, 50%, 75%, and 90% of the water depth. The analysis cases based on the number and length of baffles are summarized in Figure 8.
# Future Industrial Networks in Process Automation: Goals, Challenges, and Future Directions
## 3 Technology Enablers for Future Control Architecture
### TSN in the Process Industry  
The attempt of standardization of network traffic types is still in progress under the joint IEEE/IEC 60802 standard. Table 2 shows the most developed proposal for traffic classification presented at IEEE/IEC 60802 via the Industrial Internet Consortium, IIC [13]. However, the values for a period, tolerance to loss, and criticality for each traffic type may vary with different applications, which may not strictly follow those listed in Table 2. Mapping various traffic types to TSN mechanisms that fulfill the requirements of each traffic type is also ongoing in IEEE/IEC 60802, with inputs from the Shapers Initiative via Open DeviceNet Vendors Association (ODVA). Table 3 aims to derive the specific TSN mechanisms together with the appropriate recommendation type based on the required QoS level.  
As can be seen from Tables 2 and 3 the specification is on its way, but more work is needed to remove ambiguities. For instance, from a process automation perspective, the support for brown-field installations seems to be missing. Moreover, as can be seen from the captured data-set at Iggesund Mill in Section 2, it is far from trivial to map existing communication flows to dedicated TSN mechanisms. It is, by all means, no exercise that is quickly done and requires a lot of domain knowledge in order to derive the correct conclusions. The final performance of TSN networks will heavily depend on the input parameters, and those parameters might be difficult to derive. Assuming several parameters to be derived for each communication flow, the engineering efforts quickly become infeasible and require excellent tool support and engineering guidelines.
## 6 Conclusions  
In conclusion, we systematically investigated the impact of bias deviation, amplitude mismatch, and time skew for MZM-based short-reach DD links. The overall transfer function, including fiber dispersion and PD detection, were theoretically obtained and validated numerically. Specifically, bias deviation with single-arm driven mode and amplitude mismatch with differential driven mode can shift the frequency of fading notches, while time skew is capable of filling up the frequency nulls. In the experiment, 34 Gbaud Nyquist PAM-6/8 signals reached the 7% and 20% HD-FEC thresholds after 20 km SSMF transmission, after introducing one of the imbalances combined with VNE. For fading-dominated fiber transmission, the ROP sensitivity from best to the worse was sorted as: time skew, amplitude mismatch, and bias deviation. On the other hand, for the BTB case, the imbalance-induced penalty from the least to the largest could be sorted as: bias deviation, time skew, and amplitude mismatch. Both numerical and experimental results confirm that the dispersion induced power fading can be effectively suppressed with bias, amplitude, or skew imbalances, providing a valuable reference for the transmitter design in C-band short-reach optical transmission systems.  
Conceptualization, Y.Z. and W.H.; methodology, Y.Z.; software, Y.Z.; validation, Y.Z.; formal analysis, Y.Z.; investigation, Y.Z., X.M., Q.W. and L.Y.; resources, Y.Z.; data curation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z., X.M., Q.W., L.Y. and W.H.; visualization, Y.Z.; supervision, W.H.; project administration, Y.Z. and W.H.; funding acquisition, Y.Z. and W.H. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China (62001287); China Postdoctoral Science Foundation (2021M692098); National Key R&D Program of China (2018 YFB1800904).  
All data needed to evaluate the conclusions are presented in the paper. Additional data related to this paper may be requested from the authors.  
The authors declare no conflict of interest.
# Experimental Study on Repairing Corroded Cracks by Electrophoretic Deposition
## 4 Results and Discussion
### Accelerate Carbonization Depth and Water Absorption  
Figure 9 shows changes in the accelerated carbonization depths of the repaired mortars observed over 112 d. The accelerated carbonization depths of the repaired mortars decreased and their carbonization resistance improved with increasing repair time. After 112 d of carbonization, the accelerated carbonization depths of the P-T6, P-T-12, P-T-18, and P-T-24 mortars were 0.48, 0.38, 0.20, and 0.14 mm, respectively. The uncracked mortar carbonation depth was up to 5.2 mm, indicating that the carbonization resistance of mortars after electrophoretic deposition repair was significantly improved. Figure 10 shows the influence of the repair time on the water absorption of the repaired mortars. Compared with the cracked mortar without repair, the waterproofing performance of the repaired mortars was significantly improved, and longer repair times decreased the water absorption rates of the mortars. The maximum water absorption amounts of the P-T6, P-T12, P-T18, and P-T24 mortars were 0.435, 0.292, 0.135, and 0.089 kg\(\cdot\)m\({}^{-2}\), respectively, which are significantly lower than that of the unrepaired mortar (1.391 kg\(\cdot\)m\({}^{-2}\)). The 7-day maximum water absorption amounts of the P-T12, P-T18, and P-T24 mortars were even lower than that of the uncracked mortars (0.375 kg\(\cdot\)m\({}^{-2}\)).  
Figures 9 and 10 also show that the maximum accelerated carbonization depths and maximum water absorption amounts of the P-T18 and P-T24 mortars are relatively similar. This finding indicates that increases in repair time do not greatly improve the anti-carbonation performance and water resistance of the mortars after 18 h of repair. Therefore, the electrophoretic deposition time can be shortened according to actual needs.  
Figure 8: Epoxy resin filling in cracks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mortar Type** & **P-T-6** & **P-T-12** & **P-T-18** & **P-T-24** \\ \hline Thickness/mm & 7.6 & 10.8 & 14.6 & 16.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average filling depth of resin for mortars.
## 5 Results
### Distance Analysis
#### 5.6.1 Effectiveness  
The model's effectiveness in predicting user access was assessed using a confusion matrix and related metrics. The success ratio was measured to ensure the model's reliability and usability in real-world scenarios. Figure 12 shows part of the success-ratio results derived from the total login attempts and the successful attempts.  
The snapshot shows a success ratio between 0.4 and 0.8, with successful logins generally exceeding failed logins.
## 3 Results and Discussion
### Firmness and SSC Prediction Results
#### 3.1.1 Partial Least Squares Regression (PLSR) Model Accuracy
Compared to the present study, these findings emphasize the need to further optimize the predictive models. Nevertheless, an RPD value between 2 and 2.5 is considered a rough but acceptable prediction, while a value above 2.5 represents a good or excellent prediction [41]. Therefore, the model developed for SSC (RPDP = 3.98) can be considered  
Figure 1: Performance of the selected PLSR models for firmness (SNV-PLS) and soluble solids content (RAW-PLS) in relation to cross-validation (CV) and external validation (EXT), after wavelength selection and outlier removal, along with the trend of RPD values in cross-validation (RPDcv) and external validation (RPDext) during the iterative optimization cycle of the models. The star point (\(\bigstar\)) on the RPDcv curve and the diamond (\(\blacklozenge\)) on the RPDext curve represent the maximum RPD value reached by the model, selected as the best compromise between cross-validation accuracy and prediction. The circle on the \(Y\)-axis represents the corresponding value of the optimized model after 100 Monte Carlo runs. reliable for practical applications, while the one for firmness (RPDP = 2.02) requires further optimization to enhance its predictive capacity.
###### Abstract  
To address issues of food authenticity, such as fraud and origin tracing, it is essential to employ methods in food fingerprinting that are efficient, economical, and easy to use. This review highlights the capabilities of vibrational spectroscopy techniques, including mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopy, as non-invasive tools for food authentication. These methods offer rapid, cost-effective, and environmentally friendly analysis across diverse food matrices. This review further discusses recent advances such as hyperspectral imaging, portable devices, and data fusion strategies that integrate chemometrics and artificial intelligence. Despite their promise, challenges remain, including limited sensitivity for certain compounds, spectral overlaps, fluorescence interference in Raman spectroscopy, and the need for standardized validation protocols. Looking forward, trends such as the miniaturization of devices, real-time monitoring, and AI-enhanced spectral interpretation are expected to significantly advance the field of food authentication.  
food authentication; vibrational spectroscopy; food fingerprinting 1
# Trajectory Tracking with Adaptive Robust Control for Quadrotor
## 3 Trajectory Planner  
Before designing the controller is necessary to generate the desired trajectory that must follow the UAV (Unmanned Aerial Vehicle). Then, we have defined a model that represents the acceleration of a particle or solid in the \(x-y\) axis given by  
\[\dot{x} = g(\theta\cos\phi+\phi\sin\psi) \tag{6}\] \[\dot{y} = g(\theta\sin\psi-\phi\cos\psi) \tag{7}\]  
Figure 1: Euler angles.  
Figure 2: Forces in quadrotor.  
Thus, Equations (6) and (7) can be seen as the reference trajectory that the UAV must achieve. To generate the desired trajectory, it is necessary to define two other equations:  
\[\bar{x}^{c} = k_{p1}(x_{d}-x)+k_{d1}(\dot{x}_{d}-\dot{x})+\bar{x}_{d} \tag{8}\] \[\bar{y}^{c} = k_{p2}(y_{d}-y)+k_{d2}(\dot{y}_{d}-\dot{y})+\bar{y}_{d} \tag{9}\]  
with \(k_{p1,2},k_{d1,2}>0\). Finally, with Equations (8) and (9), it is possible to obtain the Euler angles that will be the reference inputs for the control law to move the unmanned aerial vehicle toward the desired trajectory. The equations to obtain desired angles are:  
\[\phi_{c} = \frac{1}{c}(\ddot{x}_{c}\sin\psi^{d}-\ddot{y}_{c}\cos\psi^{d}) \tag{10}\] \[\theta_{c} = \frac{1}{c}(\ddot{x}_{c}\cos\psi^{d}-\ddot{y}_{c}\sin\psi^{d})\] (11) \[\psi_{c} = \psi^{d} \tag{12}\]  
with \(\phi_{c}\) as roll angle reference, \(\theta_{c}\) and \(\psi_{c}\) are the pitch and yaw angle references, respectively.
# Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous Driving
## 4 Experiment  
In this section, the obstacle avoidance path planning algorithm is experimented with the mobile robot. We conducted an experiment using the parameter value of the ODG MPC algorithm as the value that obtains the best experimental result in the paper [38]. Section 4.1 describes the three main evaluation indicators for the experimental results. The first is the average computational time evaluation metrics. The second is the safety metric, and the third is an indicator related to comfort. Section 4.2 describes the configuration of the experimental environment. Section 4.3 reveals the results of the actual experiment. The ODG MPC algorithm was optimized using the MATLAB optimization routine _quadprog_.
## 2 Context of the Experiments
### Control Algorithm
#### 2.7.4 Available Power  
Figure 2 illustrates how the power considered as available in the future for the charge of the EVs is computed. It shows the solar production data of 1 November 2021. First, the PV production forecasts, based on weather forecasts, are retrieved every two hours from the web server of the National Oceanic and Atmospheric Administration in the USA. These data are called \"rawPowerForecast\" and are reported as orange dots in Figure 2. Second, the power production of the PV plant considering that the sky is clear, is computed through a homemade software based on the public software library PV_LIB [38]. These data are called \"clearskyPower\" and are reported as a dark blue line. This curve is bell-shaped because solar production increases during the morning until it reaches solar noon (time when solar production is at its peak), then decreases at the same rate as it increased a few hours earlier. Third, a \"bell-shaped\" interpolation of the \"rawPowerForecast\" is computed. These values are called \"powerForecast\" and are reported as a black line. The power measured on the PV plant is collected with a System Control And Data Acquisition (SCADA) and is transmitted over the 3G network. These data are called \"Power\" and are reported as a blue line. From all these data, SIGE computes the \"correctedPowerForecast\" represented by a green line. Roughly speaking, these last data are equal to the \"powerForecast\", unless the previous values (i.e., in the near past) of \"powerForecast\" are too far from the previous values of \"Power\". In that case, \"correctedPowerForecast\" is computed through \"Power\" and through the persistence method (i.e., the weather in the near future is considered the same as the weather in the near past). Finally, the power considered in the near future as available for the charge of the EVs is the \"correctedPowerForecast\".