esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Adaptive Robust Control Design
### Fuzzy Dynamic Modeling of Delta Robot
Due to the uncertainty, the determination of the matrix \(M(\cdot)\), \(C(\cdot)\), \(G(\cdot)\), \(F_{e}(\cdot)\) and \(M_{f}(\cdot)\) are associated with not only the generalized coordinate \(\delta\) and the generalized velocity \(\dot{\delta}\) but also the uncertainty parameter \(\varsigma\)._  
**Assumption 1**. : _The matrix \(M(\delta,\varsigma,t)\) is uniformly positive definite, which leads to_  
\[M(\delta,\varsigma,t)>\sigma I \tag{5}\]  
_for all \(\delta\in\mathbf{R}^{n}\). Here, \(\sigma>0\) is a scalar constant._  
**Remark 3**. : _In the past, Assumption 1 was generally considered to be a fact rather than an assumption [33]. However, in some mechanical systems, the matrix \(M(\delta,\varsigma,t)\) is proved to be a semi-positive definite matrix under some circumstances. Some examples are listed in [34]._  
**Assumption 2**. : _For all \(\delta\in\mathbf{R}^{n}\), \(\varsigma\in\Sigma\), the matrix \(M(\delta,\varsigma,t)\) is bounded as_  
\[\|M(\delta,\varsigma,t)\|\leq\kappa_{0}+\kappa_{1}\|\delta\|+\kappa_{2}\| \delta\|^{2}. \tag{6}\]  
_Here, \(\kappa_{x}\) are scalar constants, \(x=0,1,2\), \(\kappa_{0}>0\), \(\kappa_{1,2}\geq 0\)._  
**Remark 4**. : _For any rigid type robots with revolute and slide joints, \(M(\delta,\varsigma,t)\) is related to the parameters of the inertia matrix and the positions of the joints, that is, there exists a set of constants \(\kappa_{x}\), the euclidean norm of the inertia matrix satisfies (6)._  
There exist no slide joints but only revolute joints in Delta robot, which leads to \(\kappa_{1}=\kappa_{2}=0\) such that  
\[\|M(\delta,\varsigma,t)\|\leq\kappa_{0}. \tag{7}\]  
**Assumption 3**. : _(1) Suppose the initial state of Delta robot, which is uncertain, be expressed as \(\delta_{0}=[\delta(t_{0})^{T},\dot{\delta}(t_{0})^{T}]^{T}\), where \(t_{0}\) is the initial time. For any element in \(\delta_{0}\), namely \(\delta_{0i}\), \(i=1,2,\cdots,2n\), there exists a fuzzy set \(U_{0i}\) in a universe of discourse \(\Lambda_{i}\in\mathbf{R}\) represented as_  
\[U_{0i}=\left\{(\delta_{0i},\mu_{\Lambda_{i}}(\delta_{0i}))|\delta_{0i}\in \Lambda_{i}\right\}. \tag{8}\]  
_Here, \(\mu_{\Lambda_{i}}:\Lambda_{i}\rightarrow[0,1]\) is the membership function, \(\Lambda_{i}\) is known and compact._  
_(2) For any element in \(\varsigma\), namely \(\varsigma_{i},i=1,2,\cdots,m\), the element \(\varsigma_{i}(\cdot)\) is Lebesgue measurable._  
_(3) For each \(\varsigma_{i}\), there exists a fuzzy set \(U_{1i}\) in the universe of discourse \(\Sigma_{i}\in\mathbf{R}\) represented as_  
\[U_{1i}=\{(\varsigma_{i},\mu_{\Sigma_{i}}(\varsigma_{i}))|\varsigma_{i}\in \Sigma_{i}\}. \tag{9}\]  
_Here, \(\mu_{\Sigma_{i}}:\Sigma_{i}\rightarrow[0,1]\) is the membership function, \(\Sigma_{i}\) is known and compact, \(\Sigma=\Sigma_{1}\times\Sigma_{2}\times\cdots\times\Sigma_{m}\)._
# A Roadside and Cloud-Based Vehicular Communications Framework for the Provision of C-ITS Services
## 3 Roadside and Cloud Architecture
### Cloud Architecture
#### 3.2.4 National Access Point (NAP)  
As part of a European Commission initiative to improve the interoperability of mobility-related services and facilitate the travel experience in the European Union, each member state should have a NAP to which users and road operators can connect to exchange transport-related data and/or metadata. Access to data is provided in a non-discriminatory basis, and related implementations must follow standards defined by the European Commission. NAPs can also provide Application Programming Interfaces (APIs) for programmatic data exchange.  
In the designed architecture, a NAP serves as a bridge between the various road managers to exchange traffic-related data. Through the central C-ITS platform, the road manager can disseminate NAP-originated events, such as accidents, to the road users through the devices connected to the cloud MQTT broker, such as RSUs and user smartphones (through the designed application). User-generated events can also be published in the NAP, after due verification by the road manager.
# A Glider-Assisted Link Disruption Restoration Mechanism in Underwater Acoustic Sensor Networks
## 2 Related Work
In order to improve the flexibility and generality of network of link disruption restoration and save the energy of common sensor nodes to reinforce the connectivity of the network, many researchers have begun to study the use of movable and rechargeable underwater vehicles to assist in underwater monitoring tasks. Based on the motion characteristics of underwater vehicles, Ref. [9; 23] have made a related study on the location prediction of dynamic nodes and the communication among nodes. The authors in [24] propose an AUV-aided underwater routing protocol (AURP) for UASNs. AURP uses AUV as a relay node to assist common sensor nodes collecting information and gives full play to the initiative of the mobile node and reduces the load of the network static nodes. Learn from this idea, taking into account the strong initiative and the low energy consumption of gliders, we propose a glider-assisted link-disruption restoration mechanism to restore interrupted links in UASNs.
## 4 Leak Detection Technologies
### Software-Based Methods  
The software-based approaches collect relevant data from water distribution systems, such as water flow, temperature, and pressure. They can be classified into six groups: flow variation, pressure point examination, water balance, and numerical methods, as well as artificial intelligence and pattern recognition.
# Terahertz Time-Domain Spectroscopy of Glioma Patient Blood Plasma: Diagnosis and Treatment
## 2 Materials and Methods
### Machine Learning Methods  
The analysis of the experimental THz data was performed according to the ML pipeline, as presented in Figure 2. In the first stage, data were split into two groups: patients with glioma versus healthy donors (Case 1), and patients with glioma versus patients with SCD (Case 2). A computational processing pipeline was implemented on Python (3.9; here and below, version of the software placed in round brackets) with scikit-learn (1.2.1), scipy (1.9.1), and catboost (1.0.6) libraries. It included THz spectra smoothing using a Savitzky-Golay filter, dimensionality reduction and visualization using PCA and t-SNE methods, and data separability tests using SVM, RF, and XGBoost. The choice of the latter was restricted by the requirement of interpretability of the results. PCA allows the reduction of the dimensionality of the original data by projecting it onto the new coordinate axes (principal components, PCs) by maximizing explained variance. The major difference between PCA and t-SNE methods is that PCA can be trained and then reapplied on the new, unseen data, whereas t-SNE needs to relearn. The advantage of t-SNE is the consideration of distances in the original data. This means that if spectra are close in some metrics in the input data, then after projecting the data to a lower dimension space, points will be close too. It should be mentioned that t-SNE Python implementation (sklearn.manifold.TSNE) has crucial parameters: perplexity and metrics. RF and XGBoost methods are both tree-based ensemble classifiers [57]. The difference between them lies in the training process. XGBoost adds a random decision tree classifier to correct the erroneously classified samples, thus improving the accuracy of data separation. The RF method used on each iteration step of the training process found the best random subset of features for the given classifier to construct the ensemble one [58]. These methods explicitly find most informative features during the training process, so the confidence in the constructed data models is high [59].  
ML models' performance was evaluated through a k-fold cross validation technique in terms of ROC-AUC, sensitivity, and specificity metrics. This pipeline has already been applied in our previous works for the analyses of experimental THz and Raman data of mouse blood serum [41; 46].  
Figure 2: Experimental THz data processing pipeline.
## 3 Hardware Development
### Power Supply
#### 3.4.1 Battery Lifetime Estimation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Device** & **Time Duration [s]** & **Voltage [V]** & **Current [mA]** \\ \hline Sensor & 0.3 & 4.0 & 4 \(\cdot\) (2  sensors) \\ MCU\({}_{WuMCU}\) & 0.1 & 4.0 & 0.01 \\ MCU\({}_{Measure}\) & 0.3 & 4.0 & 0.01 \\ MCU\({}_{ProData}\) & 0.5 & 4.0 & 0.01 \\ Wi-Fi\({}_{WuWiFi}\) & 2.0 & 3.3 & 16 \\ Wi-Fi\({}_{DataTrans}\) & 1.0 & 3.3 & 120 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Power consumption in active mode. factors such as temperature and humidity can have an impact on the battery capacity and leakage current of the electronic components used. So, considering all the practical difficulties, it could be reduced to about 1.5 years. Alternatively, it is possible to double the battery life by taking only one measurement per day. From Equation (6) and counting the number of measurements and data transmissions, it is possible to estimate the remaining percentage of the battery charge and display the data graphically in the app.
## 4 Control Packet Collision
### Collision Avoidance with the Reactive Interval Setting  
SREQ collisions are caused by two factors, one of which is the disagreement between the transmission capacity and the load of a node. The maximum number of packets that a node can receive per unit time corresponds to the number of IDs the node sends per unit time. Therefore, as the intermittent interval of a node is shortened, the amount of data that a node can receive increases. When the load exceeds the processing performance of the node, multiple SREQ packets are sent, and collision occurs. Accordingly, in the reactive method, each node sets its ID transmission interval dynamically. Nodes determine that their loads are high when collisions are detected while they are waiting for an SREQ packet. In this case, they set their own intermittent intervals to \(T_{min}\). If an SREQ collision is not detected, the nodes gradually increase their intermittent interval to \(T_{max}\) at increments of \(T_{i}\) after every transmission of an ID in order to reduce the duty cycle (Figure 7). Regarding \(T_{max}\) and \(T_{min}\), although a longer \(T_{max}\) decreases the duty cycle of the node, it affects its neighbors by increasing the interval of waiting for an ID. In contrast, while a shorter \(T_{min}\) improves the transfer performance, it interferes with communication between other nodes.  
The other factor is the priority of forward nodes as receivers. As described in Section 3.2, when a sender node receives an ID packet from its forward node, it transmits an SREQ packet. Therefore, when more than one hidden node is ready to send data to the same receiver, whenever the receiver transmits an ID, an SREQ collision occurs. In addition, even if there are no hidden nodes, SREQ packets will collide if they are transmitted simultaneously. At nodes which are the only forward nodes for a large number of sender nodes, such as the sink node, SREQ collisions occur repeatedly, as mentioned before in the section regarding recurring SREQ collisions. In order to solve this problem, it is necessary for sender nodes to ignore the ID packet of their forward nodes in a random fashion. Therefore, if a node fails to transmit a packet to all its forward nodes, which is a situation described as 'communication failure' in Section 3.2, it ignores IDs from the forward nodes with a fixed probability denoted by \(P_{f}\).  
Figure 7: Dynamic control of the intermittent interval as proposed in [15].  
As \(P_{f}\) becomes larger, sender nodes tend to transmit data packets to sideward nodes. Thus, a large \(P_{f}\) leads to an increase in both the number of data relays and the period of waiting for ID packets from sender nodes. We utilize the concept of disregarding ID packets with a certain probability for selecting the appropriate \(P_{f}\). Although this additional process cannot prevent initial collisions, once a collision occurs, each sender node autonomously avoids further collisions.
# LAMDA Controller Applied to the Trajectory Tracking of an Aerial Manipulator
###### Abstract
LSMC has been tested in the field of control of chemical processes, demonstrating that it is stable in the control of systems with model uncertainties and under disturbances. The results obtained by this LAMDA controller have been good in non-robotic aerial applications. * The originality of this article lies in the validation of the robust LAMDA controller in aerial robotic systems whose dynamics are partially known or inaccurate. The main features of the proposed method are the following:
* The controller is based on concepts derived from LAMDA theory: Classes or functional states have a number of fixed layers (intrinsic feature). Therefore, the design is more straightforward than methods where the number of internal layers must be calibrated. * The proposed controller uses the class criteria, which allows a quick convergence on the desired output without requiring a learning method that increases computational time. * The design of a controller where the continuous and discontinuous control actions of SMC schemes are computed using the LAMDA method. * A comparative analysis between the LAMDA method that does not use the aerial manipulator robot model is carried out with other well-known controller, which depend on model. Thus, obtaining a good performance of the controller without considering of the dynamics system. Based on the abovementioned features, the contribution of this article is the design of an intelligent controller based on the LAMDA fuzzy algorithm for the trajectory tracking of an aerial manipulator robot and validating the approach in this field of study. Four control algorithms will be designed: (i) Kinematic, (ii) Inverse Dynamics, (iii) SMC Sliding Mode, and (iv) LAMDA. Finally, experimental tests will be carried out for the four controllers on a real aerial manipulator robot with three degrees of freedom for trajectory-tracking purposes. The paper is organized as follows: Section 2 presents the kinematic and dynamic robot model, whereas Section 3 describes the design of the four controllers as well as their stability. Experimental tests and analysis of the results are given in Section 4. Finally, discussion and main conclusions are presented in Section 5.
# Decorrelation-Based Deep Learning for Bias Mitigation
## 3 Materials and Methods
### Decorrelation in Objective Function  
In our study, we use the squared distance correlation as a decorrelation function. This function is minimized to decorrelate features learned by the networks from the biases. This means that we want to find parameters of the network, such as \(F\) features, have a minimal distance correlation with the \(B\) bias variable. We added the decorrelation function term to the standard objective function. The objective function is given as:  
\[J(\theta)=\min_{\theta}(1-\lambda)L(Y,\hat{Y})+\lambda\mathcal{D}\mathcal{C}^ {2}(B,F) \tag{2}\]  
The regular loss function (\(L\)) in Equation (2) could be binary cross-entropy, softmax loss, or mean-squared error depending on the nature of the tasks. The \(\lambda\) in the objective function is a hyperparameter that controls the relative importance of the decorrelation function in relation to the loss function. \(Y\) and \(\hat{Y}\) are true and classifier outputs, respectively, whereas \(B\) is bias variable and \(F\) is features extracted from the model. Optimizing the combination of these two losses not only helps to mitigate bias but also tries to achieve higher classification accuracy. Depending on the size of the dataset and the overfitting issue, one can also add a regularizer L2 loss function in the objective function for weight decay purposes.
# Dental Microstructural Imaging: From Conventional Radiology to In Vivo Confocal Microscopy
## 3 In Vitro Imaging Techniques
### In Vitro Raman Spectroscopy  
_Raman spectroscopy_ (RS) is a non-invasive and non-destructive optical scattering technique used to explore the chemical arrangement of materials. RS is based on the principle that the matter irradiated by light produces molecular vibrations (the so-called \"Raman effect\") specific to each compound. In detail, the Raman effect ensues when a beam of light plugs the surface of a specimen, and some of the dispersed photons alter energy. Each molecule possesses distinctive vibrational modes, constituting the fingerprint of a molecule. Raman spectroscopy recognizes these vibrational differences and allows the identification of each specific molecule or tissue based on its vibration characteristics. [61].  
Based on this principle, [PERSON] and colleagues used micro-Raman spectroscopy to estimate dental fluorosis severity [62]. The authors collected 400 micro-Raman spectra images from 40 extracted teeth (30 suffering from dental fluorosis and 10 controls). Their data underwent accurate statistical analysis, which indicated a significant difference in fluorosis severity categories, such as endorsing micro-Raman in vitro techniques.  
Recently, [PERSON] et al. conducted RS analysis to observe orthodontic bonding composites [63]. Their in vitro investigation was performed on enamel discs, and the data obtained by the Raman device reveal unique variations in the spectrum among the examined materials.  
Furthermore, RS revealed the intriguing prospect of being used for human gender recognition; in 2021, [PERSON] et al. conducted an ex vivo investigation to detect the teeth's sexual dimorphism by Raman spectroscopy [64]. The authors observed good spectra classification accuracy, generating promising results for forensic scenario purposes.  
The potential role of RS in dental tissue characterization comes from a study by [PERSON] et al., who aimed to standardize carious detection in clinical practice based on the subjective hardness operative perception of dentine [65]. This in vitro study employed high-resolution RS to determine the correlation between biochemical composition and the Knoop microhardness test of 20 curious dentine lesion samples. The authors found clear correlations between the results from the Knoop microhardness test and the Raman peak values of amide and phosphate moieties, supporting the potential of RS in identifying in vitro and in vivo carious lesions.
## 2 Orbital Simulator  
The orbital simulator includes the six dof spacecraft dynamics, model of the actuation system (Reaction Control Thrusters (RCS) and reaction wheels (RWs)), models of sensors, and model of external disturbances. Actuators and sensors errors and nonlinearities are also taken into account.  
The spacecraft dynamics includes both position dynamics and attitude dynamics. The orbital dynamics is based on propagation of equations of relative dynamics, usually known as Hill's or Clohessy-Wiltshire equations. Attitude dynamics is formulated in the quaternion notation and it is propagated relative to the inertial Earth Centered Inertial reference frame (ECI).  
As briefly said in the introduction, the proposed case study is a complete rendezvous maneuver depicted in Figure 1. The Hill frame will be described later in the paper, and it will be better depicted in Figure 2. The maneuver starts at the waypoint S0, which is the initial condition for the simulation. This waypoint corresponds to a lower orbit with respect to the Target one and it is located at a sufficient distance from the Target in order to successfully complete the maneuver. The second part of the complete maneuver is for reaching the Target orbit and it starts at waypoint S1, the position of which depends on the difference in altitude between the two spacecraft. If the maneuver is an impulsive (or quasi-impulsive) one, the resultant trajectory corresponds to the ideal Hohmann transfer. The next maneuver (from waypoint S2 to waypoint S3) is necessary for getting closer to the Target. The distance of S3 from the Target is usually of the order of hundreds of meters, and it is strongly dependent on safety approach issues. Finally, waypoint S4 corresponds to the docking mechanism of the Target. The S3-S4 maneuver is a forced motion of the Chaser to get the Target, generally similar to a straight line approach. The simulation stops when the Chaser is few meters away from the Target. The docking of the spacecraft is not considered in the simulator, because a multibody dynamics is usually considered to clearly understand the loads of contact.
# Analysis of Urban Residents' Travelling Characteristics and Hotspots Based on Taxi Trajectory Data
## 3 Experiments, Results, and Analysis
### Analysis of Residents' Travel Time Characteristics
#### 3.2.1 Weekday Travel Demand Analysis  
The study started by evaluating the total number of taxi operations on weekdays. Using the processed trajectory data, the total numbers of taxi trips were found to be 6597 and 6846 on the two weekdays, with passenger counts of 56,096 and 58,462, respectively. Next, the analysis focused on hourly travel patterns during weekdays, excluding the 24:00 to 6:00 night period due to low passenger numbers. The analysis included calculating the number of taxi trajectories per day, enabling the determination of passenger pick-up and drop-off trends across different times. These statistics are represented in Figure 10, providing visual insights into the fluctuating demand for taxi services throughout the day.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Attribute Fields** & **Data Example** & **Data Types** & **Data Description** \\ \hline cab\_id & 3949 & Long Int & Taxi collection terminal number \\ latitude & 30.628614 & Float & Latitude of data collected (GCJ-02 coordinate system) \\ longitude & 104.02519 & Float & Longitude of data collected (GCJ-02 coordinate system) \\ status & 0 & Int & Passenger loading status (0 means empty; 1 means loaded) \\ timestamp & 3 August 2014 22:50:54 & Timestamp & Timestamp of recorded data \\ \hline \end{tabular}
\end{table}
Table 2: Data’s attribute structure.  
According to the data in Figure 10, a clear pattern in the frequency of taxi pick-up and drop-off points during different times on weekdays is observed, with noticeable peaks at around 7:00-9:00, 11:00-13:00, and 19:00-21:00. The specific analyses for these time periods are as follows:  
1. Travel volume experiences a marked surge after 6:00, reaching its zenith at around 9:00. This surge is attributed to residents commuting to work or school, creating a pronounced morning peak in taxi usage.
2. There is a minor increase in the number of taxi passengers after 11:00, reaching its highest point at around noon, and then gradually decreasing after 13:00. This pattern corresponds to lunch breaks at workplaces and schools, leading to increased taxi usage as residents travel to dine and rest.
3. There is a notable decrease in taxi passenger numbers during the evening, particularly between 14:00 and 17:00. This reduction is likely due to evening rush hour congestion, impacting the efficiency of taxi services.
4. Another peak in taxi usage is observed between 19:00 and 21:00, coinciding with residents engaging in social and entertainment activities after work or school.
5. Taxi passenger volume gradually decreases after 23:00, aligning with residents' typical bedtime routines on weekdays.