esa-sceva/satcom-chunk-collection huggingface dataset:
# Computational Aspects of \(L_{0}\) Linking in the Rasch Model
## 2 \(L_{0}\) Linking in the Rasch Model
### \(L_{0}\) Linking as a Robust Mean-Mean Linking in the Rasch Model  
MM linking can be modified by using the \(L_{0}\) loss function instead of the \(L_{2}\) loss function. In the former case, the loss is defined by counting the number of non-vanishing deviations \(\widehat{b}_{i2}\) from \(\widehat{b}_{i1}+\mu\), which corresponds to the number of non-vanishing DIF effects. To obtain a stable linking parameter estimate in the presence of sampling errors, the \(L_{0}\) loss function is replaced by the differentiable approximation \(\rho_{\epsilon}\), and the estimated mean \(\hat{\mu}\) is obtained as  
\[\hat{\mu}=\underset{\mu}{\arg\min}\,H(\mu)\text{ with }H(\mu)=\frac{1}{I}\sum_{ i=1}^{I}\rho_{\epsilon}\Big{(}\widehat{b}_{i2}-\widehat{b}_{i1}+\mu\Big{)}. \tag{10}\]  
This approach can also be referred to as robust MM linking or \(L_{0}\) linking in the Rasch model, where \"robust\" refers to the linking parameter estimate being resilient to the presence of fixed DIF effects.  
Equivalently to (10), the linking parameter estimate \(\hat{\mu}\) solves the estimating equation  
Figure 1: Ratio loss function and Gaussian loss function \(\rho_{\epsilon}\) for \(\epsilon=0.001\) and \(\epsilon=0.01\) as differentiable approximations of the \(L_{0}\) loss function.  
\[\frac{\partial H}{\partial\mu}=\frac{1}{I}\sum_{i=1}^{I}\rho_{\varepsilon}^{\prime} \big{(}\widehat{b}_{i2}-\widehat{b}_{i1}+\hat{\mu}\big{)}=0. \tag{11}\]  
Equation (10) represents a one-dimensional optimization problem that can be numerically solved using standard optimizers implemented in statistical software.
## 3 Conceptual Design
### Finite Element Analyses and Optimization Results
#### 3.5.1 C1 FEA and Results
This confirms the design's structural integrity under actuation-driven morphing, with stress concentrations confined to expected high-stiffness junctions. To evaluate the structural behavior of the MF using a different material, a second finite element analysis (FEA) loop, referred to as Loop 2, was conducted by applying the same methodology as in Loop 1. A 10-ply carbon-fiber lay-up was chosen, featuring a consistent skin thickness of 2.4 mm. Figure 18 presents the FEA results for the structure under aerodynamic loads in high-lift configurations. The displacement distribution (shown on the left) differs from that depicted in Figure 15; however, the MF still demonstrates rigidity, with displacement magnitudes around \(10^{-4}\) m when subjected solely to aerodynamic loads. Due to the composite material properties, the distribution of top ply failure indices is illustrated on the right in the same figure, revealing peaks exceeding 1 at the connections between ribs and skin. Additional sensitivity analysis was conducted by evaluating the variations in displacements and the distribution of failure indices through the actuation of the lower skin and the lower edges of the ribs. The findings are illustrated in Figure 19, which indicates the following:  
* When the lower skin is displaced by 0.062 m, the vertical deflection at the downward tip is approximately 1.4 \(\times\) 10\({}^{-1}\) m, which is very close to the target Tz value. The failure indices for the top ply again reveal peaks at the junctions between the skin and the ribs. * Actuating the lower edges of the ribs by 0.054 m yields Tz results of around \(1.5\times 10^{-1}\) m, with a similar distribution of top ply failure indices as observed in the previous scenario. * In both cases analyzed, the ribs emerge as the most stressed structural components, highlighting the necessity for optimization in their design. Final Finite Element Analyses (FEA) were conducted solely under the influence of actuation loads applied to the lower skin, aiming to gather data on displacements and the distribution of top ply failure indices. The findings are illustrated in Figure 20: while the vertical deflection at the downward tip closely aligns with the target value, the resulting morphed shape appears to be discontinuous. All preliminary results for the C1 configuration indicate that this architecture can meet the Tz requirement with appropriate actuation on the lower skin and lower rib attachments. However, an optimized design is necessary to achieve the desired continuous morphing shape.
## 1 Introduction
[30] combined traditional and adaptive ANCF methods to introduce an enhanced Adaptive Absolute Nodal Coordinate Formulation (AANCF). [PERSON] et al. [31] concentrated on the dynamic response of elastic slender structures under significant axial stretching and extensive deformation. Through simulations based on existing experimental data, they demonstrated that their ANCF model could offer precise and dependable predictions for the response of line structures under extensive deformation. [PERSON] et al. [32] further utilized the ANCF method to intricately model ROV umbilical cables with radially multilayered circular sections, validating the proposed cable element model through comprehensive numerical studies and experiments. [PERSON] et al. [33] integrated the ANCF with arbitrary Lagrangian-Eulerian descriptions to develop a dynamic model of the J-lay pipeline deployment process, uncovering the dynamic characteristics of the pipeline during deployment and its correlation with laying responses. They discovered that this method markedly enhances accuracy and achieves convergence through static analysis. Currently, the Absolute Nodal Coordinate Formulation (ANCF) serves as a pivotal computational tool for addressing issues related to flexible large deformation structures and is widely used in fields such as mechanical engineering, multi-body dynamics, and aerospace. However, the potential of the ANCF remains largely untapped in marine engineering, especially in underwater operations. Although the ANCF has been utilized to analyze flexible cables in marine environments, research on its application in pendant deployments remains scant. This study aims to employ the ANCF to investigate the nonlinear time-domain dynamic coupling processes in the Pendant Installation Method (PIM) theory. This study specifically includes the following sections. Section 2 primarily introducesthe ANCF theory and the numerical solution of the kinematic equations, and thoroughly discusses the mechanical concepts and relationships used in model development, including elastic internal forces and external loading forces. Section 3 validates the applicability of the ANCF model in large deformation scenarios and presents key application cases. In Section 4, the response of the PIM model in a deep-sea environment at a depth of 1700 m is simulated using the catenary method, and the results are compared with those obtained from the ANCF method. Subsequently, the sensitivity factors affecting system performance during the deployment phase are analyzed based on the proposed theoretical model. Section 5 provides a summary and discussion of the research findings.
## 4 Simulation Results and Discussions
### User's Strategy Selection Probability and Jammer's Regret  
Figure 4 shows the user's power selection probability in one epoch, and it converges to a stationary mixed strategy after about 70 time slots. All power strategies have possibilities to be selected which can increase the randomness of strategy and fool jammer effectively. It is easy to find that the user tends to choose power strategy \(P_{3}\) and tends not to choose \(P_{1}\) and \(P_{2}\), that is because a higher transmission power can guarantee the SINR at BS. However, an exorbitant power causes the increase of power cost, so the possibility of \(P_{3}\) is higher than \(P_{4}\) and \(P_{5}\), which realizes the tradeoff of anti-jamming effect and power cost at the same time.  
Figure 5 shows the user's relay selection probability in one epoch, and it converges to a stationary mixed strategy after about 50 time slots. We can find the selection probabilities of \(R_{1},R_{2},R_{3},R_{4}\) are  
Figure 4: User power selection probability.  
Figure 3: Location of base station (BS), the user, jammer and relay group.  
about 0.247, 0.355, 0.204, 0.194 respectively when they have achieved convergence. That is because though \(R_{3}\) is closer to BS and the user, but the distance between \(R_{3}\) and the jammer is also shorter. So in order to minimize the jamming influence of the jammer, the user tends to choose \(R_{2}\) and \(R_{1}\) even they are further from BS and the user compared to \(R_{3}\) and \(R_{4}\). Considering the influence of distance on channel fading, the user tends to select \(R_{2}\) compared to \(R_{1}\).  
Figure 6 shows the jammer's regret in 1000 epochs, which denotes the loss of payoff due to the fact that the optimal strategy is not always chosen during the decision-making process. We can find the jammer's regret grows nearly logarithmically, i.e., it grows quickly at the start and grows slowly subsequently, which means the loss caused by the wrong choice of the optimal strategy is smaller and smaller. However, regret still grows because jammer selects other non-optimal strategies occasionally to avoid missing a future potential optimal strategy, which realizes the exploration and exploitation simultaneously.
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 4 Floods
### Reviewed Use Cases for Flood EW
There is a vast literature on Neural Networks and DNN used for flash flood prediction, and the approaches rely on the knowledge of previous data and flood maps and new data coming from the chosen WSN solution, for which good communication is essential, alongside the right choice of measured parameters and positioning of the network. As an example, [PERSON] et al. [55] propose a generic block diagram for flood prediction using Convolutional DNN that follows the aforementioned approach of data sensing, communication, and prediction. The developed data-processing algorithm and Convolutional Neural Networks (CNN) showed better results than systems that use Artificial Neural Networks (ANN) or Deep Neural Networks (DNN) when validated using doppler radar data. [PERSON] et al. [51] realized in Kuwait a flood EW system that uses Machine Learning on data obtained from a sensor network to issue warnings. They deployed 24 sensors and 6 coordinators/receivers, dividing the monitored area into 6 sectors. The sensors used are water level, temperature, humidity and wind speed sensors, and the coordinator nodes also use the Google Weather API to get additional data. LoraWAN is used for communication technology. Since not many sensors were available, the number of reading from each sensor had to be raised. The data collected over a year was used to form a training set which was used to train a Support Vector Machine (SVM) model which was then implemented on each coordinator node, and validations showed a 98% accuracy for detecting flood. The thresholds for the sensors must be calculated every time a new solution is deployed in a new location (requiring a period to collect data if previous data was not available), and there is an ambiguity for data coming from overlapping endpoints, i.e., from locations that intersect, and better clustering and locations separation is needed. As already mentioned, camera based systems and Computer Vision can also be used for flood Early Warning. [PERSON] et al. [52] developed an IoT platform using simple feature extraction and image comparing algorithms that use data obtained from CMOS cameras and images from a database to produce curves that give a rank of the danger of flooding calamity. The compressed images are collected from the cameras network using Zigbee, forwarded to a GSM gateway and then sent to a processing server for analysis, with the advantage of being low-cost and economic.
# Epoxy/Glass Fiber Nanostructured p- and n-Type Thermoelectric Enabled Model Composite Interphases
###### Abstract  
This experimental study is associated with the modification of glass fibers with efficient, organic, functional, thermoelectrically enabled coatings. The thermoelectric (TE) behavior of the coated glass fiber tows with either inherent p semiconductor type single wall carbon nanotubes (SWCNTs) or the n-type molecular doped SWCNTs were examined within epoxy resin matrix in detail. The corresponding morphological, thermogravimetric, spectroscopic, and thermoelectric measurements were assessed in order to characterize the produced functional interphases. For the p-type model composites, the Seebeck coefficient was +16.2 \(\mu\)V/K which corresponds to a power factor of 0.02 \(\mu\)W/m-K\({}^{2}\) and for the n-type \(-\)28.4 \(\mu\)V/K which corresponds to power factor of 0.12 \(\mu\)W/m-K\({}^{2}\). The p-n junction between the model composites allowed for the fabrication of a single pair thermoelectric element generator (TEG) demonstrator. Furthermore, the stress transfer at the interphase of the coated glass fibers was studied by tow pull-out tests. The reference glass fiber tows presented the highest interfacial shear stress (IFSS) of 42.8 MPa in comparison to the p- and n-type SWCNT coated GF model composites that exhibited reduced IFSS values by 10.1% and 28.1%, respectively.  
Seebeck effect; model composites; SWCNTs; organic thermoelectrics; functional interphases; thermal energy harvesting +
Footnote †: journal: Applied Physics Letters
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 4 Intrusion Detection and Prevention Systems
### Knowledge-Based Techniques  
Knowledge-based techniques [30] for intrusion detection are expert systems whose knowledge base models reasonable traffic profiles. This takes into consideration not only data, but also a set of rules that can characterize the expected shape of normal traffic. As a rule, the need to encode manually requires expertise and can be time-consuming. One major drawback is that these rules should be adapted if the traffic shapes change because of technology and internet services evolution. If the set of rules is well-calibrated, the false positives can be maintained at a low level.
## 2 Current Status of GNSS Integrity Service  
This section will review the current status of the integrity services of the four GNSS constellations, including BeiDou, the GPS, GLONASS, and Galileo. Both fundamental integrity services and SBAS integrity services will be considered. The fundamental integrity service of GNSSs represents the integrity information in the broadcast messages, and the SBAS integrity service denotes the integrity information provided by the SBAS messages.
# A Novel Method for Spectrometry Based on Imaging Systems
## 2 Materials and Methods
### Spectrometric Characteristics  
A finer scanning on the CRY018 crystal surface has been previously carried out in [23; 24] with the aim to characterize this scintillator in terms of energy resolution, pulse height uniformity response, and detection efficiency. A pulse height uniformity response within \(\pm\)3% and an energy resolution less than 8.5% (\(\sim\)30 keV) at 356 keV was reported. CRY018 has shown a fairly good energy resolution at 122 keV (12.7%, \(\sim\)15.5 keV) and at 81 keV (13.5%, \(\sim\)11 keV), too. In a 6 mm thick CRY018 a detection efficiency of \(\sim\)80% for 81 keV, 45% for 122 keV, and an efficiency less than 5% for energies greater than 356 keV was evaluated, too. This value could be further improved by increasing the thickness, considering standard dimensions for \(\gamma\) spectrometer that are of the order of few centimeters.
# A Combinational Buffer Management Scheme in Mobile Opportunistic Network
## 3 Buffer Management  
The design of the buffer management strategy should be combined with the attributes of the node and the attributes of the message. Most studies only consider the properties of the message and ignore those of nodes. In addition, since the motion trajectory of the node depends on the mobility model, the attributes of the nodes are significantly different. We propose a combinational buffer management scheme by collecting and analyzing the state of the message and considering the node properties in mobile opportunistic network. We describe it from four aspects: preliminaries, queuing strategy, utility value calculation and evaluation method, respectively. We give the details in the following sections.
# Review of Advanced Medical Telerobots
## 1 Introduction  
### Teleobotics General Context  
Telerobotic systems have extended the sensorimotor capacity of humans beyond the natural competence to achieve an augmented sensorimotor ability, which allows humans to interact with objects and environments remotely [1]. During the last two decades, teleoperated robotic systems have attracted a great deal of interest due to the remarkable benefit of medical [2, 3], and non-medical applications [4]. Focusing on medical applications, two major categories can be identified, namely, teleoperated surgery [2, 5, 6], and teleoperated rehabilitation robotic systems [7, 8], which have shown significant potential to transform the delivery of healthcare services. In this regard, teleoperated surgery has been widely investigated and commercialized. Yet, telemedicine applications also include various new remote services (whose interest grows during pandemics), such as a simple consultation or an expertise with a specific healthcare professional, telemonitoring/diagonosis [9], or an assistance for a particular procedure or examination [10, 11, 12, 13, 14, 15, 16, 17, 18, 19], over telecommunication networks.  
Conventional telerobotic systems are composed of one leader robotic module (classically named as a master module) and one follower robotic module (classically namedas a slave module) [1]. The leader robot is to be operated by the human operator, and the follower robot is to replicate the motions of the leader robot for interaction with the environment. The potential advantages of robotic manipulation over human manipulation are numerous and one can mention:  
* agility; precision; repeatability;
* automatic trajectory tracking and no-fly-zone generation;
* ability to satisfy constraints in position, and speed domains;
* real-time fusion of multimodal exteroceptive information;
* automatic recording of gestures made.
# Integrating a Path Planner and an Adaptive Motion Controller for Navigation in Dynamic Environments
## 2 Related Work
### Sampling-Based and Velocity-Based Methods  
Autonomous navigation in dynamic environments is challenging, since the planner is forced to frequently adjust its planned results for handling dynamic objects such as moving obstacles. Velocity obstacle (VO) [5] can simply consider dynamic constraints to compute trajectories of robotsvia the concept of velocity obstacles that denote the robot's velocities causing a collision with obstacles soon. However, computing maneuvers by velocity obstacles has low efficiency for real-time applications. Sampling-based methods such as PRM and RRT are popular in robotics, benefitting from advantages regarding efficiency and robustness [15]. This kind of method can solve high-dimensional planning problems by approximating C-space. Nevertheless, in dynamic environments, PRM needs to recheck edge connection [16], and RRT is required to modify the pre-built exploring tree [15], which further decrease time efficiency when environments become more dynamic. Consequently, the above-mentioned model-based methods still suffer from essential disadvantages that cannot realize time-saving navigation in dynamic environments, so as to hinder the robot's navigation capabilities.
## 2 Multi-Scenario Generation Based on Wind Power Forecasting Errors
### Constraints  
(1) Energy Storage Constraints  
\[SOC(t+1)=SOC(t)+(p^{c}(t)\times\eta_{c}-\frac{p^{d}(t)}{\eta_{d}})\times\Delta t \tag{17}\]  
\[0\leq p^{c}(t)\leq P_{rated} \tag{18}\]  
\[0\leq p^{d}(t)\leq P_{rated} \tag{19}\]  
\[SOC_{\min}\leq SOC(t)\leq SOC_{\max} \tag{20}\]  
(2) CVaR Risk Constraints  
To incorporate risk control into the objective function and prevent revenue degradation under extreme scenarios, a CVaR constraint is introduced.  
\[\tau_{k}\geq\zeta-C_{k} \tag{21}\]  
\[\tau_{k}\geq 0 \tag{22}\]  
where \(\zeta\) is the baseline value representing the revenue quantile threshold. \(\tau_{k}\) is the deviation between the revenue in scenario \(k\) and the quantile baseline. \(C_{k}\) is the total revenue of the wind farm in scenario \(k\).  
(3) Linear Constraints  
In each scenario \(k\), the actual wind power output at time \(t\), denoted as \(p_{30}^{k}(t)\), is an uncertain quantity determined by the scenario generation process. Ideally, the scheduled wind power \(p_{j}(t)\) combined with the net energy storage dispatch \(p^{c}-p^{d}\) should match this actual output, leading to a power balance constraint of the following form:  
\[p_{j}(t)+p^{c}-p^{d}=p_{30}^{k}(t) \tag{23}\]  
However, since \(p_{30}^{k}(t)\) is not a decision variable but a scenario-dependent parameter, the above equality cannot be directly used in the MILP formulation. To ensure tractability and retain linearity, a non-negative deviation variable \(z_{k}(t)\) is introduced to quantify the scheduling shortfall under each scenario. The equality is then relaxed to a linear inequality:  
\[p_{j}(t)+p^{c}-p^{d}-z_{k}(t)\leq p_{30}^{k}(t) \tag{24}\]  
This constraint ensures that the total scheduled output and storage dispatch do not exceed the available wind power in each scenario. The deviation variable \(z_{k}(t)\) is penalized in the objective function, encouraging solutions that approximate the ideal balance while maintaining tractability in the MILP framework.
## 4 Application Example
### Summary of Benefits derived from the Improvement of the Sustainability of the Transformation House
Reducing only the copper losses, without replacing the transformer with a more efficient one, by optimizing the operation of the capacitor bank \(Q_{C2}\), the benefits obtained are as follows (Table 15):  
* Losses caused by reactive and distortion currents have been considerably reduced, achieving energy loss savings in the transformer of 2896.37 kWh/year and savings in carbon dioxide emissions of 698 kg/year;
* Savings in economic costs derived directly from this energy consumption are very modest and amount to only 206 EUR/year, but the savings in penalties for capacitive reactive energy are very high, at 44,158.83 EUR/year;  
In addition, if an efficient 1000 kVA transformer replaces the inefficient 1600 kVA transformer, the benefits derived from the full application of the procedure to improve the sustainability of the transformation house are as follows (Table 15):  
* Savings in energy losses of 19,037.95 kWh/year (64.4%) and savings in carbon dioxide emissions of 4588.14 kg/year (64.4%);
* Decrease indirect costs of energy losses by 1536.75 EUR/year (58.11%) and savings from penalties for capacitive consumption by 44,158.83 EUR/year (100%).
# A Security and Privacy Scoring System for Contact Tracing Apps
## 3 Results  
### Selected Applications  
The five apps chosen as representatives are Canada's Covid Alert, Singapore's Trace-together, Iceland's Rakning C-19, India's Aarogya Setu, and France's TousAntiCovid. Canada's Covid Alert is a decentralized Bluetooth system implemented using the GAEN framework [26]. Singapore's TraceTogether is a centralized Bluetooth system following the BlueTrace protocol [2]. Iceland's Rakning C-19 is a logged GPS system [27]. India's Aarogya Setu is a Bluetooth centralized system that has added GPS streaming functionalities [28]. France's TousAntiCovid is a centralized Bluetooth system that follows the ROBERT protocol [29].  
Singapore, India, and France all use a Bluetooth centralized system and were chosen to represent the differences in protocols and behaviors between the centralized Bluetooth apps. The majority of the decentralized Bluetooth apps used the GAEN protocol and were very similar in design. The Canadian app was chosen, as there is a significant amount of information released about it. Iceland's Rakning C-19 app was chosen to represent GPS systems because they released information about the design through source code available online [30].