esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Modeling  
The working principle of a standard cruise control system, one of the vehicle's longitudinal control systems, involves the driver setting a constant desired speed, denoted as \(v_{d}\), at which the vehicle is intended to travel. The cruise control system then automatically regulates the throttle to maintain this desired speed. In this process, the longitudinal controller determines the vehicle's desired acceleration. The engine output map is a critical tool in automotive engineering, as it provides detailed information about engine performance characteristics under various operating conditions. This map typically represents engine torque and power output as functions of engine speed and throttle position, serving as a foundation for optimizing control strategies in vehicle powertrain. A study in [31] explored the application of engine maps in predictive control for energy management in hybrid electric vehicles. Similarly, ref. [32] investigated Model-based predictive engine torque control for improved drivability. Their research highlighted the use of engine maps for optimizing combustion strategies, minimizing fuel consumption, and reducing engine wear.  
In performance specifications for the design of the longitudinal controller, it is necessary to specify that the steady state tracking error of the controller should be zero. Other desirable performance specifications might include zero overshoot and adequately fast rise time. The main focus of this work is designing the upper controller for longitudinal motion control of autonomous vehicles.  
The longitudinal controller is considered as the upper-level controller as shown in Figure 1, as long as the upper controller is our concern; therefore, the plant model used for control design is the same as [1], which is given in (1),  
\[\tau\,\dddot{x}+\bar{x}=\bar{x}_{d}, \tag{1}\]  
where \(x\) is the longitudinal position, \(x_{d}\) is the desired longitudinal position, and \(\tau\) is the time constant for the actual acceleration to reach the desired acceleration. From the system (1), the state variables could be defined as a vectorized form,  
\[X=\begin{bmatrix}x&\dot{x}&\bar{x}\end{bmatrix}^{T}=\begin{bmatrix}x_{1}&x_{2} &x_{3}\end{bmatrix}^{T}. \tag{2}\]  
Thus, by considering (1) and (2), the dynamics of the system could be rewritten as,  
\[\dot{x}_{1} =x_{2}, \tag{3}\] \[\dot{x}_{2} =x_{3},\] \[\dot{x}_{3} =-\frac{1}{\tau}x_{3}+\frac{1}{\tau}u,\]  
where \(u\) is the system control input which is the desired acceleration of the vehicle. The main objective of the control input is to make the vehicle track the reference speed set by the user. In this case \(u=\bar{x}_{d}\).
## 3 Experiments and Analysis  
### Datasets, Experiment Setup and Performance Evaluation  
Two datasets are used in our experiment, one from the largest publicly released thermal infrared video dataset TIV [36] and the other from the dataset taken by ourselves. Three videos of bats with different densities taken from three viewpoints are selected from TIV [36], and they were called Davis08-sparse, Davis08-dense, and Da-vis13-medium, respectively, which describes real world scenarios of bats emerging from their caves in large numbers with a resolution of 640 \(\times\) 512. In addition, we also provided two 5-min  
Figure 5: The target features and their features obtained by max-pooling and maximum-no-pooling, respectively: (**a**) target original features; (**b**) features by max-pooling; (**c**) features by maximum-no-pooling.  
Figure 6: The processing procedures with and without \(1\times 1\) convolution, (**a**) without \(1\times 1\) convolution; (**b**) with \(1\times 1\) convolution.  
infrared videos of a flock of birds with unknown names taken near the airport at different time periods, with the number of birds ranging from 50 to 200.  
In the training phase, we used the VGG-16 [35] pretrained weights on ImageNet as initial weights in the FE block of GHPNet. The initial learning rate was set to 0.01 with a decay equal to 0.1 for every 160 epochs. We used the Adam algorithm with mini-batch size four for optimization.  
In this paper, the precision rate P, the recall rate R, the weighted harmonic average value F1 of P and R in common target detection algorithms are used as the bird detection evaluation indicator [37], which are calculated as follows.  
\[\mathrm{P}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}} \tag{7}\]  
\[\mathrm{R}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}} \tag{8}\]  
\[\mathrm{F1}=\frac{2\times\mathrm{P}\times\mathrm{R}}{\mathrm{P}+\mathrm{R}} \tag{9}\]  
where TP (True Position) represents the number of positive samples that are correctly identified as positive samples, TN (True Negative) represents the number of negative samples that are correctly identified as negative samples, FP (False Position) represents that negative samples are incorrectly identified as the number of positive samples, and FN (False Negative) represents the number of false positive samples identified as negative samples.
# Precoded Generalized Spatial Modulation for Downlink MIMO Transmissions in Beyond 5G Networks
## 3 Transmitter and Receiver Structure
### Receiver Design  
Considering the system model and the BD precoder described in the previous sections, each receiver will have to apply simple single user GSM detection. This can be seen as an attempt to solve the MLD problem related to receiver \(k\), which is formulated as  
\[\min_{\mathbf{s}}f(\mathbf{s}_{k})\triangleq\|\mathbf{y}_{k}-\hat{ \mathbf{H}}_{k}\mathbf{s}_{k}\|_{2}^{2} \tag{8}\] \[\text{subject to }\mathbf{s}_{k}\in\mathcal{A}_{0}{}^{N_{s}}\] (9) \[\text{supp}(\mathbf{s}_{k})\in\mathbb{S}, \tag{10}\]  
where \(\mathcal{A}_{0}\overset{def}{=}\mathcal{A}\cup\{0\}\) and \(\mathbb{S}\) denotes the set of valid AICs, which has a size of \(N_{comb}\). Solving this non-convex problem directly would require excessive or even unfeasible computational complexity for moderate to large problem settings. To tackle the problem, we adopt instead a similar approach to the one we applied in [7], which is based on the idea of using ADMM as an heuristic for splitting a complex problem into a sequence of simpler ones (as addressed in [32]). Being an heuristic based approach, there will be no guarantee that the resulting algorithm will converge to the solution of the original MLD problem. While this means that the detector will be suboptimal, it will require a much lower computational cost. Following a similar derivation to the one provided in [7], we can arrive at the iterative detection algorithm shown in Table 2 which can be used in each GSM receiver. In this table, \(\mathbf{u},\mathbf{w}\in\mathbb{C}^{N_{x}/N_{u}\times 1}\) are scaled dual variables and \(\rho_{x}\) and \(\rho_{z}\) are penalty parameters associated to constraints (9) and (10). A careful tuning of these parameters will ensure that the algorithm reaches a good performance during its execution. In the algorithm, \(Q\) is the maximum number of iterations, \(\prod_{D}\left(.\right)\) denotes the projection onto set \(\mathcal{D}=\{\mathbf{s}:\text{supp}(\mathbf{s})\in\mathbb{S}\}\), and \(\prod_{\mathcal{A}_{0}{}^{N_{s}}}\left(.\right)\) is the projection over \(\mathcal{A}_{0}{}^{N_{s}}\). The projection over set \(\mathcal{D}\) can be accomplished by keeping the \(N_{a}\) largest
## 3 Results and Discussion
### Degradation Kinetics and Chain Scission Mechanisms
Similarly, random chain scission was also observed in previous studies for US degradation of KGM [22], pectin [43], and chitosan [40]. Figure 2: Kinetics of ultrasonic degradation of KGM: (**a**) time courses of molecular weight (MW) change during US treatment; (**b**) time courses of intrinsic viscosity [\(\eta\)] change during US treatment; (**c**) MW time courses fitted to the random chain scission model Equation (1); (**d**) MW time courses fitted to the midpoint chain scission model Equation (2); H represents the value of \((M_{\rm e}-M_{\rm i})/(Mt-M_{\rm i})\).
## 4 Experimental Results and Discussion
### Experimental Results and Analysis
#### 4.3.2 Experimental Comparison of Optimized CCA Mechanism  
On the basis of the proposed CCA mechanism, the validity and rationality of the CCA mechanism are verified in two cases. In the first case, the CCA mechanism was used to replace the last C3 module in the backbone for the experiment, and in the second case, the CCA mechanism was added after the SPP module for the experiment. Meanwhile, to further verify its effectiveness in defect detection in nitrile medical gloves, this article compared the proposed CCA mechanism with the mainstream attention mechanisms: SE, CBAM, original CA, and ECA. As shown in Table 2, the CCA mechanism added after SPP exhibits the best recall value and mAP and meets the requirements of defect detection in nitrile medical gloves with a small increase in the values of the parameters.
## 3 Results and Discussion
### User-Calculated Sensitivity Analysis of a Subroutine  
The following algorithm is proposed to calculate the sensitivity analysis of the subroutine within the subroutine: record the perturbation steps of all measurements in the subroutine during the first Jacobian evaluation. Let the partial derivatives for the first eight measurements (those not related to the deformation) be calculated by the MBD solver using the default serial finite forward difference method. Let the rest of the partial derivatives of the sensitivity analysis be calculated by the subroutine using the same serial finite forward difference method with the use of the perturbation steps estimated during the first Jacobian evaluation. If the solution struggles to converge, update the perturbation steps. The scheme of the algorithm is depicted in Figure 5, and it is used by the user finite forward difference (_User_) method.  
The _User_ method was applied to the MBD model of the piston-liner interaction (20 mode shapes of the flexible piston only--first six modes are depicted in Figure 6) and compared with the default finite forward difference (_Default_) method. For numerical solution, the HHT integrator was employed with the following settings: integrator tolerance \(10^{-6}\), step size \(8.3\times 10^{-5}\) s, a maximum number of iterations of 10 and the original corrector. The Intel(r) Core(tm) i7-4770S 3.1 GHz processor with four physical cores and eight virtual cores was used.  
A comparison of the results using the _Default_ method and the _User_ method is indicated in Table 1. Both simulations led to very similar elapsed times. The _Default_ method required 25 min and 10.4 s while the _User_ method was slower with elapsed time of 25 min and 20.2 s. The _Default_ method also needed fewer iterations to converge of 7877 compared to 8311 of the _User_ method. According to these results, the Jacobian quality is not degraded by the user serial finite forward difference method (for the measurements related to the deformation) and it merits further investigation. From the perspective of maximum CPU usage, the complexity of a particular MBD simulation (only 48 measurements in the subroutine) and a serial sensitivity analysis solution cannot sufficiently utilize the hardware capabilities.  
Figure 5: Scheme of sensitivity analysis of the subroutine using the User finite forward difference method for deformation-related measurements in Fortran. The modal force LOADVEC is calculated using function MFORCE_EHD based on measurements INPUT and parameters PARAM. If the multibody dynamics solver is calculating the Jacobian for the first time, the subroutine records the perturbation steps DELTA. Otherwise, the subroutine calculates the partial derivatives of Equation (29), stores them in DER, and passes them to the multibody dynamics solver.  
Figure 6: First six mode shapes of flexible piston: (**a**) 5,4883 Hz; (**b**) 6320 Hz; (**c**) 9597 Hz; (**d**) 10,434 Hz; (**e**) 11,370 Hz; (**f**) 13,458 Hz.
# A Review on Distribution System State Estimation Algorithms
## 2 DSSE Special Attributes
An additional factor that adds complexity to the DSSE is the configuration of the system. First and foremost, a modern, universal DSSE needs to be adaptable and equally efficient in all sorts of configurations-not just radial (which is the conventional use case) [21]. For example, the integration of RES and various innovative architectures in the MV and LV levels has promoted the research and development on ring and/or meshed configurations. Moreover, for reasons related to the increase of the system's reliability, interconnected configurations (with more than one feeder) are studied. Other than the type of configuration, the status of the switches might sometimes be vague to the DSO, as they are not always monitored. In distribution networks, switches are mostly used in order to isolate a fault or mitigate congestion issues in the distribution system. A close switch of phase ph between nodes i and j is modeled as presented in (6)-(7), and an open switch is modeled as presented in (8)-(9) [17]. \[V_{i}^{ph}-V_{j}^{ph}=0 \tag{6}\]  
\[\mathrm{g}_{i}^{ph}-\mathrm{g}_{j}^{ph}=0 \tag{7}\]  
\[P_{i,j}^{ph}=0 \tag{8}\]  
\[Q_{i,j}^{ph}=0 \tag{9}\]  
Figure 3: Unbalanced phases in the distribution system. Taking the aforementioned attributes into account, it is evident that the DSSE constitutes a challenging, complex task with multiple proposed approaches, which will be presented in the following Sections.
## 3 DTN Solutions for IoT
### Taxonomy of DTN Solutions for IoT
#### 3.2.2 Routing-Based  
IoT and WSN characteristics made DTN routing more challenging. Consideration of energy constraints, limited bandwidth, scalability, and network heterogeneity improves and makes DTN  
Figure 3: Classification of DTN solutions for IoT.  
routing more efficient for IoT. Most routing protocols focus on the route election and the number of messages replicated in the network, but, in IoT context, selecting the good relay nodes to forward the message to the destination affects routing performance [15]. A buffer management algorithm [27] is also critical to help routing in terms of loss rate and delivery delay.
## 3 Experimental Results
The particle temperature \(T_{p}\) refers to the temperature of particles inside the mass-flow cone, and \(T_{ins,s}\) is the temperature of the outer surface of the thermal insulation. The heat loss from the heat exchanger to the surrounding through the thermal insulation can be estimated using the thermal resistance concept by knowing the temperatures and thermal resistance across the thermal insulation. The inner temperature of the insulation is equal to the temperature of the outer surface of the heat exchanger's shell if the thermal contact resistance is neglected. The shell's outer surface temperature is assumed to  
Figure 5: Variation of particle flow rate with elevator VFD frequency (Hz). be the average of the maximum temperatures of both media across the heat exchanger, i.e., the average of air inlet and particles outlet temperatures, as shown in Equation (1). This approach represents the upper limit for the shell's outer surface temperature; hence, the worst-case scenario for the heat loss. \[T_{shell,\rho}=\frac{T_{a,i}+T_{p,\rho}}{2} \tag{1}\]  
\[\dot{Q}_{loss}=\frac{\Delta T}{R_{tot}} \tag{2}\]  
where \(T_{a,i}\) and \(T_{p,\rho}\) are the air inlet and particle outlet temperatures, respectively, \(\Delta T\) is the temperature difference between the inner and outer surfaces of the insulation, and \(R_{tot}\) is the total thermal resistance of the insulation. The heat losses associated with the conducted steady-state tests are listed in Table 5. Those tests will be presented shortly in detail. It can be noticed that the loss represents a small percentage of the total heat supplied by the air, less than 3%. This implies that the heat exchanger is well insulated. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test** & \(T_{a,i}\) & \(T_{p,\rho}\) & \(T_{shell,\rho}\) & \(T_{installation,\rho}\) & \(\dot{Q}_{loss}\) & \(\dot{Q}_{loss}\) \\  & [\({}^{\circ}\)C] & [\({}^{\circ}\)C] & [\({}^{\circ}\)C] & [\({}^{\circ}\)C] & [W] & [\%] \\ \hline \(1\) & 555.9 & 523.3 & 539.6 & 34.5 & 780.0 & 2.4 \\ \(2\) & 556.0 & 523.7 & 539.9 & 29.1 & 788.6 & 2.5 \\ \(3\) & 556.1 & 522.7 & 539.4 & 33.6 & 781.0 & 2.5 \\ \(4\) & 556.3 & 521.1 & 538.7 & 35.5 & 777.1 & 2.5 \\ \(5\) & 555.4 & 517.0 & 536.2 & 38.5 & 768.5 & 2.4 \\ \(6\) & 556.3 & 519.9 & 538.1 & 43.9 & 763.1 & 2.6 \\ \(7\) & 553.9 & 517.2 & 535.6 & 30.9 & 779.2 & 2.7 \\ \(8\) & 553.8 & 517.3 & 535.5 & 28.4 & 783.1 & 2.8 \\ \(9\) & 555.4 & 517.1 & 536.3 & 43.7 & 760.6 & 2.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Heat loss through thermal insulation. Figure 6: Temperature decay inside the heat exchanger when T100 unit is off. The air pressure at the inlet (\(P_{a,i}\)) and outlet (\(P_{a,o}\)) of the heat exchanger is continuously measured. It was found that the pressure drop (\(\Delta P=P_{a,i}-P_{a,o}\)) across the heat exchanger is around 5 kPa. Figure 7 shows the air pressure measurements and the pressure drop.
###### Abstract  
Smart manufacturing systems aim to enhance the efficiency, adaptability, and reliability of industrial operations through advanced data-driven approaches. Achieving these objectives hinges on accurate fault detection and timely maintenance, especially in highly dynamic industrial environments. However, capturing nonlinear and temporal dependencies in dynamic nonlinear industrial processes poses significant challenges for traditional data-driven fault detection methods. To address these limitations, this study presents an Incremental Pyraformer-Deep Canonical Correlation Analysis (DCCA) framework that integrates the Pyramidal Attention Mechanism of the Pyraformer with the Broad Learning System for incremental learning in a DCCA basis. The Pyraformer model effectively captures multi-scale temporal features, while the BLS-based incremental learning mechanism adapts to evolving data without full retraining. The proposed framework enhances both spatial and temporal representation, enabling robust fault detection in high-dimensional and continuously changing industrial environments. Experimental validation on the Tennessee Eastman (TE) process, Continuous Stirred-Tank Reactor (CSTR) system, and injection molding process demonstrated superior detection performance. In the TE scenario, our framework achieved a 100% Fault Detection Rate with a 4.35% False Alarm Rate, surpassing DCCA variants. Similarly, in the CSTR case, the approach reached a perfect 100% Fault Detection Rate (FDR) and 3.48% False Alarm Rate (FAR), while in the injection molding process, it delivered a 97.02% FDR with 0% FAR. The findings underline the framework's effectiveness in handling complex and dynamic data streams, thereby providing a powerful approach for real-time monitoring and proactive maintenance.  
data driven; fault detection; DCCA; incremental learning; dynamic nonlinear process +
Footnote †: journal: _Algorithms_
## 2 Materials and Methods
### Study Design and Study Population  
Children less than 16 years old were recruited for the study. Informed consent was obtained from their parents. Recruited children were divided into two cohorts--asthmatic and normal-voluntary. The asthmatic cohort consisted of children with active cough that is related to a respiratory pathology (asthmatic in this case). The normal-voluntary cohort consisted of healthy children with no active cough. Exclusion criteria included severe behavioral conditions, an inability to cooperate, and children nursed in isolation.  
Children in the asthmatic cohort were recruited from the Emergency Department, Respiratory Ward, and Clinic and Day Surgery Unit of the Hospital. Children in the normal-voluntary cohort were recruited from the Day Surgery Unit. These children were referred by their physicians and nursing team to the investigating team.  
In both cohorts, children were instructed to give a series of voluntary coughs. These cough sounds were recorded at 44.1 kHz, using a dedicated Samsung 6 smartphone (Samsung Group, Seoul, Korea) in the ward, so as to collect the cough sounds in as ecological a manner as possible (i.e., with ambient background ward sounds which included crying, babbling, monitoring alarms, staff talking, etc.). For awarded children in the asthmatic cohort, their cough sounds were collected daily whenever possible, until they were discharged. These cough sounds were then used to construct an audio database representing asthmatic and healthy children.  
Demographic data such as age, weight, and medical history were referenced from the patient's medical record. For the asthmatic cohort, the duration of coughs, clinical diagnosis, and results of physical examinations and investigations at the time of presentation of the cough were noted. The respiratory diagnosis related to the cough in question was determined by the attending physician, based on the patient's history and examination, and the investigation of the chest radiograph, as required.  
In this study, coughs from children diagnosed with asthma, exacerbation of asthma, or bronchial reactivity were grouped together under the asthmatic cohort. As bronchial hyper-responsiveness [23] is a common feature in classical asthma and cough-variant asthma, we thus chose to include the bronchial hyperactivity diagnosis together with the asthmatic cohort.  
Further, children with asthmatic cough with concurrent respiratory conditions, such as respiratory tract infection, allergic rhinitis, or both, were excluded, as these conditions might confound the quality and characteristics of the asthmatic cough sound in question. Children re-admitted into the hospital within a week after discharge were also excluded from our analysis; this was to eliminate the cases of poor progression of disease or poor response to medication.