esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background  
### Floating-Point Representation  
The IEEE-754 [29] standard specifies the format for representing floating-point numbers, as well as rounding modes and arithmetic operations such as addition \(\oplus\), subtraction \(\ominus\), multiplication \(\otimes\), division \(\oslash\) and square root \(sqrt\). Floating-point numbers are written as \(x\cdot 2^{E}\), where the mantissa \(x\in[1,2)\) is a number of \(m\) binary digits (bits) and \(E\), \(E_{min}\leq E\leq E_{max}\), is an integer called the exponent. The format specifies \(m\), \(E_{min}\) and \(E_{max}\). Representable numbers are those that can be expressed in this notation. If the solution of an operation is not representable with one setup, then the result is rounded to a representable number. The round-to-nearest, round-towards-zero, round-towards-positive-infinity, and round-towards-negative-infinity rounding modes are selectable by modifying the internal state of the floating-point unit (FPU). Every rounding mode has a rounding function \(fl(x)\) that converts a real number \(x\) into a representable number. Every arithmetic operation is defined as the rounding of an abstract arithmetic operation's exact outcome. For example, \(fl(a+b)\) will become the result of the sum of two integers \(a\) and \(b\). All intermediate values are rounded in computations that involve more than one operation. As a result, the operators \(\oplus\) and \(\ominus\) are not associative. When \(a=10^{20}\), \(b=-10^{20}\), and \(c=1\) with a 64-bit representation, the expression \((a+b)+c\) yields 1 while \(a+(b+c)\) produces 0. We have to note here that the differently accumulated roundoff error in most cases should not change the validity of an application [30]. Changing the execution order of an algorithm may still produce valid results, independently of being reproducible.  
We can observe this effect in Figure 1, using a more realistic finite element method example with a conjugate-gradient solver, with calculations in double precision (Aero [20]--detailed in Section 3.8). On this histogram, we counted the number of different values of the end results in relative differences for several magnitudes between running the application using eight processes and 16 processes. From the 6.5M elements, there were only 3599, which had a bitwise identical result, the rest had a difference between \(10^{-12}\) and \(10^{-4}\), and most of them were around the magnitude of \(10^{-8}\).
## 3 Results  
### Failure Situations in All Cases  
Figure 7 shows the failure situations for different cases during rainfall. No runoff on the surface of the slope was observed in all cases, as the infiltration capacity of the soil layer was higher than the rainfall intensities used in this study. Two failure modes were observed in this study. One was soil piping, which occurred in the multi-layered slope. Slight soil piping occurred at the toe of the slope, finer materials mixed with amounts of infiltrate water flowed out from the piping as the seepage surface, and the seepage surface grew gradually and increased until cracks appeared (Figure 7c). As Figure 7a shows, the piping phenomenon occurred slightly, and as the tilt angle increased, the soil pipe size developed rapidly and more seriously (see Figure 7b,c).  
Another failure mode was surface sliding from the toe of the slope in a single-layer slope. Small slide failure occurred at the toe at first, and then relatively larger slide failure followed. This type of failure mode was clearly observed for cases IV, V, and VI, as shown in Figure 7d.  
The failure modes and initial failure time \(t\) for all eight cases are summarized in Table 4. The initial slope failure occurred at the toe of the slope in case I and case IV at about 1.18 and 0.95 h after the rainfall has been applied, respectively. This means that failure in a multi-layer slope occurs later than that in a single layer under the same condition in the horizontal group. In Figure 7b,e, in case II and case V, failure occurred at around 0.84 and 0.89 h, respectively. In case III and case VI, more rapid  
Figure 7: Comparison of failure situations of the different model experiment cases (each case was performed three times to ensure the repeatability of the results, and typical results of individual experiments were chosen to show the failure situation in each case). (**a**) case I (tilt angle \(\alpha=0^{\circ}\)); (**b**) case II (tilt angle \(\alpha=7^{\circ}\)); (**c**) case III (tilt angle \(\alpha=15^{\circ}\)); (**d**) case V (tilt angle \(\alpha=0^{\circ}\)); (**e**) case VI (tilt angle \(\alpha=7^{\circ}\)); (**f**) case VII (tilt angle \(\alpha=15^{\circ}\)).  
movement of slope failure occurred in an almost fully saturated condition and they thus had a lower strength. The failure times of the 15 degrees inclined group were 0.78 and 0.85 h. It should be noted that failure occurred earlier in the multi-layer slope than in the single-layer slope in the inclined group, which is different from the result obtained for the horizontal group.
# Hub-and-Spoke Network Design Considering Congestion and Flow-Based Cost Function
## 2 Literature Review  
The problem of finding the optimum location for hub facilities in a given network is well-studied in the literature and these are generally named hub location problems (HLPs), in which it is assumed that the hub network is a complete graph [3]. Hub location problems receive great attention in the literature, and many aspects of this set of problems have been studied. There are several review articles in this field for interested readers [4; 5; 6; 7; 8].  
There is another version of HLPs, in which the assumption of having a complete network is relaxed. Thus, the model should identify the links to establish in the hub network and determine where to locate the hub facilities. These types of problems are known in the literature as Hub Arc Location Problems (HALPs) [9].  
Several transportation modes in the literature of hub location/transportation research are studied, such as sea-based/maritime [10; 11; 12; 13; 14], railroad [15; 16], air [17; 18; 19; 20], multi-modal [21; 22; 23; 24]), etc.
## 3 Methodology
### Problem Definition
Taking an incoming vessel as an example, after entering the channel, the vessel will proceed towards the berth at a certain heading and speed until it reaches a certain distance before the berth. At this point, the vessel's speed will decrease, and its heading may change until docking is completed. The position where the vessel decelerates and the rate of speed change will vary depending on the size and type of the vessel. The paper aims to solve the problem of obtaining a vessel behavior model within the channel using machine learning methods, and then predict the vessel's behavior within the channel at different times based on known conditions. The study focuses on the following key aspects:  
* Extraction of Waterway Data: Since the research is concentrated on vessel behavior within the channel, it is crucial to confine the range of AIS data to the boundaries of the channel;
* Preprocessing of Historical AIS Data: This involves filtering out anomalous AIS data to ensure that the training process is not adversely affected;
* Organization of Training Data: This step includes reformatting AIS data to create labeled training datasets suitable for analysis;  
Figure 2: Diagram of ship behavior in Fairways. * Learning Vessel Behavior Patterns: The study employs Decision Tree, Random Forest, and Gradient Boosting Regression methods to analyze and model vessel behavior patterns within the Waterway.
## 3 Selected Results of Optical Metrology Techniques
### Techniques Based on Geometrical Optics Suitable for In-Process and In Situ Applications  
Deflectometry, formerly often denoted as Fringe Reflection (FR) and Fringe Projection (FP), have proven their suitability for precise form measurement [14]. Deflectometry is suitable for reflective surfaces and has demonstrated its usefulness not only for the detection of surface and surface-near defects [15], but also for form measurement in optical manufacturing and qualification [39], achieves high precision [40] and--as a gradient-based method--is precise and robust without using a laser light source [20] that may introduce concerns with respect to eye safety. Although the measurement principle is simple, adjusting the setup and proper evaluation is involved in order to avoid systematic errors. Recent results for simpler and more effective calibration procedures applied to FP [41] established recently at our institute may also foster a breakthrough for in-line measurements using deflectometry.  
Fringe Projection is suited for diffuse scattering surfaces and has demonstrated its ability for precise, robust, in-process measurement of complex geometries [42]. It is well suited for many technical surfaces and is intrinsically benign with respect to complex surfaces. Data capturing can be comparatively fast. Using a 120 Hz fringe projection in combination with a camera that only requires 8 ms for one picture one just needs 32 ms to capture a phase distribution. Although further measurements are needed in the commonly employed hierarchical phase shifting approach, the measurement accuracy only depends on the first measurement that uses the highest spatial frequency. In the following two sub-chapters, we present one example for measuring large objects and one for small objects.
## 2 Nyquist Analysis for Ideal FPR Controller on Voltage Applications
### Stability Analysis Based on a Conceptual Nyquist Trajectory
#### 2.2.5 Nyquist Curve Drawing Considerations  
This section describes the assumptions or considerations made for all the non-analytically attainable results from previous sections.  
* In terms of the slope near the resonance frequency. Thus, assuming reasonable \(r_{C}\) and \(C\) values, i.e., \(r_{C}C\approx 10^{-6}-10^{-9}\), it can be analysed that from \(\alpha=0\) to \(1\), the slope starts from being huge and negative, to decrease its slope until the negative vicinity side of \(\alpha=1\). There is a discontinuity at that point, and the slope abruptly goes from negative to \(\omega_{0}r_{C}C\). From \(\alpha=1\) to \(2\), the slopes increase again to the extreme case (\(\alpha=2\)) where another discontinuity goes to \(-1/(\omega_{0}r_{C}C)\).
* In terms of crossing points. When \(\alpha\) moves from \(0\) to \(1\) (not included), the cross point is moving from (0,0I) to \((-x\),0I), with \(x\) being the exact real part of the crossing point. Then, at \(\alpha\) equal to \(1\), there is an abrupt change, and the cross-point is positive and equal to \((\frac{r_{C}^{2}C^{2}k_{p}\omega_{0}^{2}+r_{C}Ck_{i}\omega_{0}+k_{p}}{r_{C}C^{ 2}\omega_{0}^{2}},0I)\). Now, from \(\alpha\) equal to \(1\) to \(2\) (not included), the cross-point is (\(x\),0I), where \(x\) reduced its value until \(0\) when \(\alpha\) equals exactly \(2\).
###### Abstract
By fine-tuning gate execution, noise interference can be minimized, leading to improved computational accuracy and fidelity. In existing NISQ devices, quantum programs are typically composed of single-qubit and two-qubit gates. Studies have shown that the error rate of two-qubit gates is usually 10 times higher than that of single-qubit gates [15]. As a result, two-qubit gates in quantum circuits face a significantly higher risk of errors during execution. To improve the fidelity and efficiency of quantum computing, various strategies have been explored, including optimizing quantum circuit design [16], implementing quantum error mitigation techniques [17], and developing more robust quantum algorithms [18]. The primary goal of these strategies is to reduce quantum gate error rates and improve qubit stability, thereby enhancing the overall performance and reliability of quantum computers. To address the challenges of excessive SWAP gate insertion and insufficient parallelism in quantum circuit scheduling, we propose a new scheduling method. By integrating SWAP gate conflict avoidance with quantum gate parallelism optimization, a Quantum Exchange Lock Parallel Scheduler is designed, and a Full-level Joint Optimization SWAP Algorithm is implemented. Our contributions focus on the following aspects:  
* Introducing SWAP conflict optimization of traditional greedy algorithms: Reduce the conflict of SWAP gates through deep optimization, while balancing the relationship between local suboptimal solutions and global optimization. Using the idea of greedy algorithms, flexibly handle conflicts between quantum gates and avoid frequent insertion of SWAP gates. * Quantum lock parallel time operation: Utilize the parallelism of quantum gates to reduce the execution time and depth of the circuit. It is based on the results of the first layering and further optimizes the execution order of SWAP gates and other quantum gates by dynamically inserting nonconflicting quantum gates. Full-level joint optimization algorithm: Through cross-level optimization, the FJOSA algorithm can more effectively reduce the insertion of SWAP gates and optimize the execution order of quantum gates. * Cost function: The algorithm dynamically selects the optimal number of optimization layers through the cost function, balancing the relationship between reducing SWAP gate insertion and controlling algorithm complexity. * Comprehensive experimental verification and algorithm comparison: The effectiveness and superiority of the QELPS algorithm and the FJOSA algorithm are experimentally verified and compared with the benchmark circuit and the 2 QAN algorithm, providing a reliable solution for the quantum circuit scheduling problem.
# IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data
## 3 Methods
### Prediction Models-MLT Models and Naive Approach
#### 3.2.1 Decision Tree (DT)  
DT was originally designed for categorical data, but it may be also applied to continuous data, such as in this study. DT is constructed through recursive partitioning, where nodes are split based on input feature values to minimize the overall variance or another specified metric considering all output variables. The recursive process continues until a stopping criterion is met, such as a specified tree depth or a minimum number of samples in a leaf node [35]. Each leaf contains a constant value, usually an average value of the target attribute, replaced in the model tree by a linear or nonlinear regression function. When making predictions for a new data point, the tree is traversed from the root to a leaf, and the predicted values for all the output variables are derived from the values associated with that leaf [36]. Building a predictive model using DT has two goals: to accurately predict the response variable from the input data vector and to uncover the structural relationships between the response and measured variables. These dual objectives emphasize the role of the model in precise forecasting and uncovering underlying dependencies in the observed data [37].
# Durability Issues and Corrosion of Structural Materials and Systems in Farm Environment
## 3 Corrosion and Degradation of Structural Materials in Farm Environment  
Farm structures are constructed using concrete, steel reinforcement, timber, and concrete substitutes [10; 11; 12]. However, the durability of concrete is the most pertinent factor; timber is used in limited applications because it is susceptible to fungal and insect attacks, discoloration, decay, swelling, and shrinkage [10]. Similarly, metallic structures are more susceptible to corrosion compared to concrete structures [13; 14; 15; 2; 11]. Concrete structures in farm environments are exposed to different forms of corrosive agents such as meal-water mixtures, acids, and bacteria, which suppress the pH and contribute to the accumulation of organic acids such as lactic acid, development of soluble calcium salts and leaching [12]. The cumulative effect is the degradation of the structural integrity of the material. However, the ease with which the concrete agricultural structures are degraded is dependent on the material properties of the concrete including water absorption, density, and compressive strength [12] The data in Table 4 shows the variation in material properties depending on the composition--cement content, gravel, water to cement ratio, sand, and other additions. Apart from the environmental factors, the durability of the structures is determined by metal reinforceents such as galvanized \(Z\)-girders, which are common in crop storage silos [11]. Beyond concrete and metal, the durability of timber support structures is dependent on the joint reinforceents, environmental factors such as exposure to harsh environmental conditions, and treatment [10].
## 1 Introduction
proposed a homotopy-based approach to design an expected path by deforming specific Dubins paths for curvature-bounded vehicles [32]. [PERSON] et al. presented a predictive-control-based algorithm to generate and track the path based on the requirements for the security and the path length for an unmanned surface vehicle [33]. In more general cases, [PERSON] proposed a smooth algorithm to connect a series of waypoints and generate a feasible path for UAVs in reconnaissance missions [34]. [PERSON] et al. proposed a path planning method based on the particle swarm optimization algorithm on three dimensional UAV formation tasks [35]. Moreover, [PERSON] et al. solved the collision avoidance problem based on the combination of geometrical constraints and kinematics equations during the path planning process [36; 37]. [PERSON] et al. proposed a formation control algorithm based on the backstepping design method and a finite-time attitude tracking law to deal with the collision avoidance problem in path planning [38]. Although the existing works are effective in different situations, the aerial recovery problem of a UAV swarm has, to the authors' knowledge, not yet been researched. In this problem, the coupling mechanism between the scheduling and the path planning is bidirectional and it has not been elaborated. For recovery scheduling, an appropriate method should be proposed. Moreover, an effective path planning approach needs to be proposed and adopted to design the flight paths for the UAV swarm. To address the above issues, we studied the aerial recovery problem of a UAV swarm by one mother aircraft. To describe the coupling mechanism between the scheduling process and the path planning process, a recovery planning framework is proposed. In the recovery planning framework, the outputs of the scheduling are delivered to the path planning to design the paths. Conversely, the intermediate results in path planning are treated as the feedback input for scheduling. A GA is employed to resolve the scheduling problem and generate the recovery position series and the recovery path lengths owing to its convenience. Moreover, a homotopic approach is proposed for the path planning problem based on two different path homotopies. To validate the performance of the homotopic path planning approach and the recovery planning framework, a sufficient number of simulations for stochastic scenarios were conducted. This paper is structured as follows. The preliminaries and the models of the aerial recovery problem are presented in Section 2. The recovery planning framework is proposed and introduced in Section 3. Section 4 presents the details of the genetic algorithm and the homotopic path planning approach in the recovery planning framework. Section 5 examines the proposed approaches and presents the overall performance of the recovery planning framework. Finally, Section 6 delivers the conclusions from this investigation and it describes our future research.
# Speech Emotion Recognition through Hybrid Features and Convolutional Neural Network
## 3 Materials and Methods
### Convolutional Neural Network Model  
The proposed methodology for SER utilized a CNN model to process the derived MFCCT features. The CNN model was employed due to its ability to generate a feature map of the time series data, which can achieve enhanced performance on MFCCT features. The derived features were used as input into the CL to produce local features from the input. The CNN model was comprised of three 1D CLs, each followed by a dropout, activation, and max-pool layers. The first (Conv1) layer received an array of features with a 1-pixel stride, 64 filters, and a 5-size kernel, which is a critical parameter to fine-tune the CNN model. The output of every neuron was activated using the activation function called Rectifier Linear Unit (ReLU), followed by a dropout rate of 0.2. The ReLU accelerates convergence and solves the issue of vanishing gradient, while the dropout layer reduces the overfitting issue. The output of all neurons after using ReLU was given to a 1D max-pool layer with a pooling size of 4, implementing batch normalization. The next CL was comprised of 128 filters with 5-size kernel size and 1-pixel stride, followed by an activation, 0.2 dropout rate, and max-pool layer of same size. The final CL was comprised of 256 filters with the same size of kernel and stride, followed by an activation, dropout, and flattening layer to convert the CLs output into a 1D feature vector, utilized as input to the FC layer.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Label** & **Time-Domain Features** \\ \hline MIN & Minimum value of each bin \\ MAX & Maximum value of each bin \\ Mn & Mean value of each bin \\ Md & Median of each bin \\ Mo & Mode of each bin \\ STD & Standard deviation of each bin \\ VAR & Variance of each bin \\ COV & Covariance of each bin \\ RMS & Root mean square of each bin \\ Q1 & 25 th percentile of each bin \\ Q2 & 50 th percentile of each bin \\ Q3 & 75 th percentile of each bin \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of time-domain features.  
The number of neurons in the FC layer were selected based on the number of emotion classes in the dataset, integrating the global feature obtained from the preceding layers, and generating a vector for SoftMax activation to predict emotions. The model used Adam optimizer with a learning rate of 0.0001, 100 epochs, and a batch size of 16. The proposed configuration of CNN with parameters is given in Table 2.
# Robust Clustering Routing Method for Wireless Sensor Networks Considering the Locust Search Scheme
## 5 Experiments and Simulation
### Total Residual Energy  
The total residual energy of the network is calculated by summing every sensor node's remaining energy in every round. The evolution over time of this calculation for each protocol is reported in Figure 5. A closer inspection of this statistic reveals that the proposed protocol reaches the highest residual energy throughout the network lifetime. From the figure, it can be observed that the level of energy in all protocols is 100% at the beginning of the simulation. However, the power consumption gradually reduces the total residual energy in every round until the residual energy percentage is zero. Nevertheless, the proposed approach uncovers the cluster structure that consumes the minimum energy in every single round. Consequently, it achieves the lowest energy expenditure and maximizes the network's residual energy, outperforming the other protocols in comparison.