esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Discussion  
Machine learning approaches are employed to elucidate the correlation between depression and the quality of life (QoL) characteristics that contribute to the onset of depression. XGBoost utilizes many strategies to effectively manage overfitting, identify optimal splits, and handle missing values during the training phase. The variables responsible for causing depression are the results of expectations of the feature importance. The importance ranking (Figure 5) provided in this study primarily includes estimates of sleeping difficulties, lack of exercise and physical activities, overweight, perception of weight loss, and eating behavior. Prior research provides evidence of an association between mental problems and the usage of nicotine [42].  
This study identified a link between lower monthly family income and the occurrence of depression, establishing that lower income is a contributing factor to the development of mood disorders. The study's primary findings indicate that individuals' regular sleep and waking timelines, as well as difficulties in both falling asleep and excessive sleep duration, were key factors associated with sleeping problems [43]. According to previous studies [44; 45; 46], it has been demonstrated that sleep disturbances are a significant contributing factor to the development of depression. The inclusion of extended periods of active video gameplay was also identified as one of the outcomes. It is hypothesized that an absence of vigorous physical activity and engagement in idle pursuits may contribute to depression. The perception of weight loss was also anticipated to be a contributing factor to the development of depression. Conversely, empirical evidence from a study demonstrates that those who adhere to a nutritious diet exhibit a significantly lower likelihood of experiencing depression [47]. Consuming nutritionally deficient meals can potentially be regarded as an indication of depressive symptoms. Among individuals diagnosed with alcohol dependency, 29% experienced at least one mood disorder within a year. Notably, the most prevalent affective disease among these individuals was significant depression, reported by 28% of the respondents. Depression is an important issue that holds considerable importance in understanding the gender disparity in depression, particularly among women. The investigation's findings confirm a significant correlation between financial hardship, encompassing factors such as monthly income, wages, and other financial resources, and the presence of depression. A consistent association exists between depression and an elevated risk of developing coronary heart disease. Depression is almost twice as high in women as in males, and it has demonstrated strong links with coronary heart disease.
# Beyond Correlation: Mutual Information to Detect Damage in Nonlinear Systems
## 5 Example Application
### Vibration Data  
Experimental data from a three-story aluminum frame structure equipped with an adjustable bumper and a suspended column was used. Figure 4 shows the experimental setup. Details about the experimental setup and damage states can be found in the report ([PERSON] et al., 2018).  
In this SHM testbed, the definition of the non-linear damage to be detected is 'crack' formation in a suspended central column that occasionally hits an adjustable aluminum bumper (Figure 4). This mechanism simulates how a crack opens and closes; a crack-like nonlinear damage mode. When the crack and buffer gap stay open, the system behaves linearly. Impact events cause local yielding, joint slip, and clear nonlinear signatures in response. The operational and environmental variability is simulated in several ways. Thermal effects are simulated by using columns with reduced-thickness specimens to change their effective Young's modulus. Live-load changes are simulated by adding weights at the base or on the first floor. Varying excitation levels (\(\pm\) drive voltage to the shaker) are simulated to mimic changing operational loads. Data acquisition limits reflect real-world challenges. Economic limitations allow for only one accelerometer per floor. They are mounted on the centerline without any formal optimal placement studies. There is no direct measurement of the input force, just a single base load cell, meaning we monitor output only. All channels are sampled at 320 Hz, and a dynamic range of \(\pm\)4 g is set by the sensor and the limits of the A/D converter.  
The dataset contains 170 sets of acceleration signals from 17 structural states, each state with 10 measurements. Table 1 describes these states and groups them into four categories. State #1, representing the baseline state (Category 0), is the reference state from which deviations will be considered in damage detection. Category I (State #2-#9) contains the linear damage states obtained by changing the stiffness of one or two columns or adding a mass to the base or the first floor. Category II (State #10-#14) contains the damage states where the damage is simulated by introducing nonlinear behavior via adjusting the gap. A small bumper (clearance gap) is situated between a suspended central column and an adjustable block. When the gap is small enough, the column intermittently hits the bumper, producing joint slip, bracket loosening, and local effects, yielding true nonlinear damage. Category III (State #15-#17) represents damage states obtained by simulating nonlinear behavior combined with a change in mass.  
The dataset contains 10 sets of measurements from each state. Each acceleration signal contains 8192 time-domain samples with a sampling frequency of 320 Hz. To compare the performance of the proposed \(M1\) index to \(ATC\) and \(TMAC\) we will use the same signals used in [PERSON] et al. (2018), namely the acceleration signals from Channel 2 (base), Channel 3 (first floor), and Channel 5 (third floor).
## 4 Methodology
### Causal Configuration Analysis Using fsQCA
#### Logical Minimization  
Logical minimization is a core procedure in fsQCA that simplifies the results obtained from the truth table. This step aims to identify the essential combinations of conditions that are sufficient for the outcome to occur [52]. It involves applying Boolean algebra rules and logical reduction techniques, such as the Quine-McCluskey algorithm, to simplify complex logical expressions [45]. This simplification process helps to:* Enhance interpretability: By reducing the number of conditions and configurations, logical minimization makes the results more manageable and easier to understand [46].
* Identify core causal conditions: It highlights the most critical conditions that are consistently associated with the outcome [53].
* Improve generalizability: Simpler solutions may be more generalizable to other contexts and datasets [54].  
_Solution Assessment_  
FsQCA uses measures such as consistency and coverage to evaluate the relationships between causal conditions and outcomes [37]. Consistency, in the context of sufficiency, measures the degree to which a causal condition or configuration is consistently associated with the outcome [55]. It quantifies the degree to which cases sharing a particular configuration of conditions also exhibit the outcome.  
Coverage, on the other hand, indicates the degree to which a configuration accounts for the instances of an outcome. It reflects the empirical relevance of the configuration or its ability to explain the outcome. A higher coverage value suggests a greater importance of the configuration in producing the outcome. In an extreme case where a configuration covers 100% of the outcome, it would be both sufficient and necessary. In [37], the authors suggest that consistency is analogous to Pearson's correlation coefficient (r), while coverage is analogous to the coefficient of determination (R?).  
Unlike traditional statistical methods that focus on isolated effects of conditions or independent variables, fsQCA identifies \"causal recipes\"--combinations of conditions associated with a specific outcome or dependent variable [49]. This approach offers a more comprehensive understanding of causality by considering the interaction of multiple conditions. In [56], the authors explain that fsQCA identifies patterns by grouping observations based on potential causal relationships that led to a specific outcome. These relationships are interpreted using Boolean algebra, a mathematical framework for analyzing logical relationships.  
Business failure is rarely a consequence of a single isolated cause; rather, it typically arises from the complex interaction of multiple factors, including financial strain, managerial decisions, and environmental pressures. Moreover, the relationship between these factors and the outcome of failure is often nonlinear [57].
# Electro-Aero-Mechanical Model of Piezoelectric Direct-Driven Flapping-Wing Actuator
## 3 Experiment
### Measurement  
Figure 6 shows the experimental setup for the wing motion, lift force, and power consumption. We captured the wing motion using an experimental setup with a high-speed camera. The average lift force and consumed power were measured using a precise electrical balance (HR-100A, A&D Co., Tokyo, Japan) and a power analyzer (PA1000, Tektronix, Beaverton, United States), respectively. To block the wind produced by the wing, an acrylic plate was placed between the wing and the electric balance.  
Figure 7 shows a typical image of the actuated wing. We tracked the tip and root and the tip position of the leading edge, \(\mathbf{p_{\text{tip}}}\) and \(\mathbf{p_{\text{root}}}\), respectively, and a position on the lower edge of the wing, \(\mathbf{p_{\text{edge}}}\), based on the images to estimate \(\phi\) and \(\psi\). \(\phi\) was simply calculated as the angle between \(\mathbf{p_{\text{tip}}}-\mathbf{p_{\text{root}}}\) and the x-axis. \(\psi\) was estimated by  
\[\psi=\text{arcsin}\frac{d}{c_{\text{edge}}}, \tag{42}\]  
where \(d\) and \(c_{\text{edge}}\) are the distance between the leading edge and \(\mathbf{p_{\text{edge}}}\) in the image and the chord length at the measured point, respectively. We applied unipolar sinusoidal driving signals with amplitude of \(V_{0}\): \(V(t)=V_{0}\sin(\omega_{r}t)+V_{0}\). In this experiment, the maximum voltages for the small and large types were 30 and 50 V, respectively, to prevent fracture of the piezoelectric material. The frequency of the voltage signal \(\omega_{r}\) was matched to the resonance frequency of the system.  
Figure 5: Photograph of prototype actuators: (**a**) small type and (**b**) large type.  
Figure 6: Setup for wing motion, lift force, and power consumption measurement.
# Consensus Tracking Control of Multiple Unmanned Aerial Vehicles Subject to Distinct Unknown Delays
## 3 Control Strategy
### Altitude and Attitude Control  
This section proposes using simple linear control laws for the altitude and attitude subsystems, to ensure the stability of the flying attitude of the quadrotor multi-agent system. Thus, the control inputs \(F_{i}(t)\), \(\tau_{2}(t)\), \(\tau_{3}(t)\), \(\tau_{4}(t)\) for each of the agents are denoted as  
\[F_{i}(t)= mg+k_{p2}e_{zi}(t)+k_{iz}\int e_{zi}(t)dt+k_{dz}\dot{e}_{zi}(t) \tag{6}\] \[\tau_{2}(t)= k_{p\theta}e_{\phi_{i}}(t)+k_{d\phi}\dot{e}_{\phi_{i}}(t)\] (7) \[\tau_{3}(t)= k_{p\theta}e_{\phi_{i}}(t)+k_{d\theta}\dot{e}_{\theta_{i}}(t)\] (8) \[\tau_{4}(t)= k_{p\psi}e_{\psi_{i}}(t)+k_{d\psi}\dot{e}_{\psi_{i}}(t) \tag{9}\]  
where  
\[e_{zi}(t)=z_{d}(t)-z_{i}(t) e_{\phi_{i}}(t)=\phi_{di}(t)-\phi_{i}(t) e_{\theta_{i}}(t)=\theta_{di}(t)-\theta_{i}(t) e_{\phi_{i}}(t)=\psi_{di}(t)-\psi_{i}(t)\] \[\dot{e}_{zi}(t)=\dot{z}_{d}(t)-\dot{z}_{i}(t) e_{\phi_{i}}(t)=\dot{\phi}_{di}(t)-\dot{\phi}_{i}(t) e_{\theta_{i}}(t)=\dot{\theta}_{di}(t)-\dot{\theta}_{i}(t) e_{\phi_{i}}(t)=\dot{\psi}_{di}(t)-\dot{\psi}_{i}(t)\]  
The control inputs are substituted in the equations of the linear multi-agent system (5); thus, the following closed-loop expressions are obtained:  
\[\ddot{\phi}_{i}(t)= \frac{1}{I_{xx}}\Big{[}k_{p\theta}e_{\phi_{i}}(t)+k_{d\phi}e_{ \phi_{i}}(t)\Big{]}\] \[\ddot{e}_{i}(t)= \frac{1}{I_{yy}}\Big{[}k_{p\theta}e_{\theta_{i}}(t)+k_{d\theta}e _{\theta_{i}}(t)\Big{]}\] \[\ddot{\psi}_{i}(t)= \frac{1}{I_{zz}}\Big{[}k_{p\psi}e_{\phi_{i}}(t)+k_{d\psi}e_{\psi_{ i}}(t)\Big{]}\]  
Figure 1: Control scheme for the multi-UAV system.
## 4 Classification of Routing Protocols
### Postural-Movement-Based Routing Protocols
#### 4.4.5 Energy Efficient Thermal and Power Aware Routing (ETPA)  
In [66] [PERSON] and [PERSON] proposed an energy efficient, thermal and power aware routing protocol, based on the posture-movement of the human body. ETPA aims to decrease the temperature rise and power consumption. ETPA uses the same network architecture used by PRPLC [22]. In this protocol the frames are divided into time slots using a Time Division Multiple Access (TDMA) scheme, where each node transmits in its own time slot. During each cycle (after every four frames), all nodes broadcast their hello messages to their neighbors containing the temperature and residual energy and then every node estimates the received power from neighbors nodes. ETPA uses temperature, residual energy and transmission power of the node to calculate the cost function. When a node has a packet to send, it searches for an efficient route with minimum cost. If it finds nodes with an efficient route, it forwards the packet, otherwise it buffers the packet. A packet is dropped if it has been buffered for more than two frames. In order to decrease the delay, each packet can only pass through a pre-defined number of hops (max_hop_count), otherwise it will be dropped.
# A Quality Control Method for High Frequency Radar Data Based on Machine Learning Neural Networks
## 2 Materials and Methods
### Prediction-Based Anomaly Detector
#### 2.2.2 Bi LSTM Neural Network
In this research, the Bi-LSTM layer consists of 512 neurons and is followed by the stacking of multiple fully connected layers, introducing non-linear transformations and dimensionality changes to the output. During the model training process, to ensure that the predictive model can learn the features of normal ocean current data effectively, the training data undergo meticulous curation, excluding anomalous data points. This ensures that the model exhibits a certain level of sensitivity when dealing with abnormal data, thereby ensuring efficiency and accuracy.
# Multi-Objective Ship Route Optimisation Using Estimation of Distribution Algorithm
## 2 Related Research
### Methods for Ship Routing Problem
In contrast to most of the research works on ship weather routing optimisation, the proposed approach modified both the engine power and heading settings during searching for an optimal path [2]. A similar approach, based on modifying both the ship heading and speed, was introduced in [4]. The proposed method was based on an improved 3D dynamic programming algorithm for ship route optimisation. The approach proposed in this paper is distinguished by the use of an innovative adaptation of the estimation of distribution algorithm (EDA) for the multi-objective optimisation of a ship's route in a non-stationary environment (tidal waters). The key elements of the proposed approach are the adaptive Markov chain-based path generator and the dynamic programming-based local search algorithm. The proposed approach is characterised by a unique set of features, both in terms of the problem instance being solved (ship routing in a non-stationary environment) and the methods used (multi-objective estimation of distribution algorithm, adaptive Markov chain-based path generator, and the use of the dynamic programming-based local search algorithm), which significantly distinguishes it from the methods discussed above (see Table 1).
## 4 Simulation Results
### Experimental Results
#### 4.2.5 Number of Cluster Heads  
In this section, we test the number of selected CHs in every round in EDACP-CS, EECDA, and LEACH-Hetero protocols. As shown in Figure 12, EECDA and LEACH-Hetero has more uncertainties in CHs selection than EDACP-CS. This means that EDACP-CS is an efficient CHs selection protocol helps in better and constant data rate transmission to BS.  
Figure 11: Network stability in EDACP-CS,EECDA, and LEACH-Hetero.  
Figure 12: Number of cluster heads per round in EDACP-CS, EECDA, and LEACH-Hetero.
# Towards Integrated Digital Twins for Industrial Products: Case Study on an Overhead Crane
## 3 Results
### Information Framework: Digital Twin-Based Product Lifecycle Management  
The digital twin-based product lifecycle management (DT-PLM) framework is a way to categorize the engineering content created from the initial idea to the product use phase. The framework was presented in a project seminar by [PERSON] and [PERSON] [49]. The vision was used as a guiding principle during the project, although being an extensive framework, only selected parts of it could be implemented.  
DT-PLM contains three categories: DT data, DT intelligence, and the real world, as shown in Figure 4. The DT data contain static representations of the product, whereas the active features in the intelligent part, such as simulation, bring digital twins to life, i.e., the DT becomes an active agent in cyberspace. The real world joins the lifecycle as the first prototype tests are performed and mirrored during the use phase of the product.  
Figure 3: The layers of the DT core framework with update rate getting less frequent as data flows from physical twin to strategic decision-making level. Examples of use cases are given on the right.  
The original design data are referred to as \"product DNA\". Storing the DNA is the first step, the second is to use it in various tasks during the product lifecycle. For example, DNA can be used to predict problems by looking at the DNA shared by a product family. If the DNA is in a machine-readable format, it can be leveraged as an active part of the usage phase digital twin system. For example, a system simulation can be used to provide virtual sensor information when plugged into the real counterpart. As related work, the product DNA concept has also been used by [PERSON] [50] in similar meaning, although focusing especially on the uniqueness of DNA.  
DT-PLM framework assists in understanding the practical information contents of digital twins from a product development perspective. This is an important perspective as a lot of potentially useful qualitative data are created during the product design phase. A challenge is that a lot of the design data are confidential and cannot be given even to buyers of the product. Currently, most of the design data are also prepared only for internal use, making them potentially unusable for others. A very strong business case would be needed for transferring the data to outsiders, but when the manufacturer is also the maintenance provider, using the design data in later phases of the lifecycle can be a gradual process.
# Qom--A New Hydroologic Prediction Model Enhanced with Multi-Objective Optimization
## 4 Hydrologic Calibration Strategy
### Multi-Objective Formulation  
To measure the quality of solutions obtained thorough the optimization process, a widely used statistic metric in hydrology is the Nash-Sutcliffe Efficiency coefficient (\(NSE\)) [33], which is computed by means of Equation (5). It is a normalized metric that determines the relative magnitude of the residual variance compared to the measured data variance, evaluating the model fit primarily during high-flow periods. \(NSE\) ranges between \(-\infty\) and 1.0, with \(NSE=1.0\) being the optimal value. Values between 0.0 and 1.0 are generally viewed as acceptable levels of performance, whereas values \(<0.0\) indicate that the mean observed value is a better predictor than the simulated one, hence leading to unacceptable performance.  
Based on this standard measure, our multi-objective approach is focused on minimizing two objective functions \(f_{1}\) and \(f_{2}\), by taking into account the time-series data from both, observed and estimated water flows in a given hydrologic scenario. The first objective function is based on \(NSE\), although using the formulation \(f_{1}=1-NSE\), to promote minimization (optimum in 0.0, jMetal assumes that all the objective functions are to be minimized.) The second objective function is based on the percent bias (\(PBIAS\)) [34], which is formulated as \(f_{2}=Abs(PBIAS)\) and measures the average tendency of the estimated data to be larger or smaller than their observed counterparts. \(PBIAS\) is calculated as shown in Equation (6), with optimum value in 0.0. Low-magnitude, positive, and negative values indicate, respectively, accurate model estimation, model underestimation bias and model overestimation bias.  
\[NSE=1-\frac{\sum_{i=1}^{N}(Qe_{i}-Qo_{i})^{2}}{\sum_{i=1}^{N}(Qo_{i}-Qm)^{2}} \tag{5}\]  
\[PBIAS=\frac{\sum_{i=1}^{N}(Qe_{i}-Qo_{i})}{\sum_{i=1}^{N}Qo_{i}} \tag{6}\]  
The problem formulation includes constraints based on the relative error in terms of the percentages \(RE\), with \(AE\) being the absolute error between observed and estimated values according to Equation (7). A constraint considers the mass balance function \(MB\) (water balance or soil moisture accounting) that measures the river basin drainage with Equation (8) [UNESCO, 1971] for bounding the relative error \(MBRE\). Other two constraints are defined for bounding the fitting errors in hyetograms as well as in losses and excesses volumes. In this work, solutions are constrained to \(f_{1}<=0.3\), \(f_{2}<=0.3\) and \(MBRE<=0.3\) (30%).  
\[RE=\frac{AE}{O}\;\;being\;\;AE=O-E \tag{7}\]  
\[MB=\frac{(P-EVT-R-S-D)}{\Delta t}+(Qs+Qd)-Qe\pm AE=0. \tag{8}\]
# Region Pixel Voting Network (RPVNet) for 6D Pose Estimation from Monocular Image
## 3 Results
### Evaluation Metrics  
The two most common evaluation metrics in the field of pose estimation are 2D projection error and average 3D distance (ADD).  
Figure 6: Local perception for keypoints.  
2D projection error metric is defined as follows. Given the object's model and camera's intrinsic parameters, the selected points on the model are projected onto the image according to the ground truth pose and estimated pose, respectively. If the average distance in image between corresponding 2D points is less than 5 pixels, then the estimated pose is considered as accurate.  
For ADD metric, the object model, usually referred to as the point cloud model, is rigidly transformed according to the ground truth and estimated pose, respectively. If the average distance between the corresponding points of the two transformed models is less than 10% of the model's diameter, then the estimated pose is considered to be accurate. The diameter of the model is defined as the distance between the two farthest points on the model. For symmetric objects, ADD-S metric [11] is used. In ADD-S, the average distance is calculated according to closest point distance.  
In our experiment, only ADD metric was selected for accuracy evaluation. The main reasons are threefold: First, according to Equation (2), as the point's depth \(z_{c}\) increases, the change in pixels caused by the same relative motion of the object becomes smaller and smaller. Second, the experimental results of many studies [10; 12; 17; 29] show that 2D projection error is more \"tolerant\" than ADD metric, i.e., accuracy is higher under the criterion of 2D projection error. Third, the purpose of estimating pose is often for other systems to better interact with the object in 3D space, which means that ADD metrics are more realistic.