esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
Three electronic databases (PubMed, Web of Science, Scopus) were chosen to search relevant studies. Google Scholar and backward search (i.e., assessing the reference lists of the included articles) were used for additional searches. The terms \"cognitive functions\", \"cognitive performance\", or \"agility\" were combined with \"fatigue\" and \"sport\" to find a title, abstract, or keywords related to our topic. The main inclusion criterium was that studies had to examine the impact of any form of acute fatigue on (i) cognitive functions only, (ii) reactive agility, (iii) sport-specific skills with reactive components included. Other criteria were a publication date of 2010-2021 and article full-text availability. Studies that failed to meet our criteria were excluded from this review.  
The PRISMA flow diagram (Figure 1) adapted from [PERSON] et al. [35] illustrates the search and selection stages. Initially, we identified 243 articles from the main databases and another 27 articles which provided additional Google Scholar search and backward search of relevant studies reference lists. After the exclusion of duplicates, we eliminated studies that focused on cognitive changes related to sleep deprivation or nutrition interventions, sport-specific skills, agility without cognitive components, or did not examine team sport players. Twelve relevant articles are included in Table 1. Pre-post differences were calculated from the mean values and expressed as percentages. If the study did not provide the required data, we used the abbreviation \"n.d.\" (no data) or the significance level. The last column shows available additional indicators such as: (a) standard deviation (SD), (b) 90% confidence interval (90% CI), (c) 95% confidence interval (95% CI), (d) effect size--Cohens d (ES [d]), and (e) effect size--Partial eta squared (ES [\(\eta_{\text{p}}\)\({}^{2}\)]). These indicators serve to enhance the understanding of collected data.  
In summary, although the number of studies included in our review is restricted, we consider that current research on this topic is focused rather on the general or trained population than athletes in their specific sports.  
Figure 1: Search methodology.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Author**} & **Level and** & \multirow{2}{*}{**Participants**} & **Fatigue Inducing** & \multirow{2}{*}{**Cognitive Abilities**} & \multirow{2}{*}{**Type of Assessment**} & \multicolumn{2}{c}{**Pre-Post**} & \multicolumn{1}{c}{**SD [\({}^{\circ}\)1\(
## 5 Simulations Results and Discussions
### Case Study 2: Home Is Equipped with DGs
#### 5.2.2 PADR  
The PADR for case study 2, where RES and ESS are equipped with the home in addition to the EUC, is shown in Figure 10. The results show that the proposed algorithm reduces PADR by 40.1%, whereas PSO, GA, ACO, BFA, WDO, GBFA, GPSO, and GACO limit the PADR by 27%, 11.8%, 32.3%, 16.3%, 23.7%, 19.26%, 5.3%, and 36.8%, respectively, and the GWDO algorithm-based ECS consistently scheduled the consumer's appliances to attain the objective of reducing the PADR.
# CubeSat-Based Observations of Lunar Ice Water Using a 183 GHz Horn Antenna: Design and Optimization
## 3 Simulation and Results
The smooth-walled antenna's enhanced impedance matching, and reduced insertion losses make it a more efficient and reliable choice for operating at the targeted frequency range. Figure 6 depicts the VSWR measurements of both the corrugated horn antenna and the smooth-walled horn antenna in the same frequency range. For the corrugated horn antenna,  
Figure 5: \(S_{11}\) Simulation of 183 GHz Corrugated Horn Antenna and Smooth-Walled Horn Antenna. the VSWR values range between 2.2 to 0.4, indicating some degree of impedance mismatch and signal reflections. However, the smooth-walled antenna maintains VSWR values close to 1:1 throughout the same frequency range, suggesting excellent impedance matching and minimal signal reflections. The smooth-walled antenna's stable VSWR values make it a highly efficient and reliable option for transmitting and receiving signals in the specified frequency range, which is crucial for space exploration and remote sensing applications. In Figure 7, we observe the simulated gain results of both the corrugated horn antenna and the smooth-walled horn antenna. The corrugated horn antenna exhibits a gain of 1 dB, which may pose challenges in satellite remote sensing applications due to its relatively low performance. However, by applying the smooth-walled MATLAB algorithm to the corrugated antenna, we achieve a remarkable increase in gain, reaching 18 dB. This significant enhancement in gain makes the smooth-walled antenna an ideal choice for satellite remote sensing applications, as it can overcome the challenges posed by the original corrugated design. Figure 7 clearly demonstrates the improved gain performance of the smooth-walled antenna compared to the corrugated horn antenna. This result validates the effectiveness of our proposed method in transforming the corrugated design into a smooth-walled configuration. Importantly, this optimization process does not compromise the antenna's functionality; instead, it enhances its magnetic specifications, resulting in better performance. In Figure 8, we present the gain phi and gain theta results for both antennas, and it is evident that there are no unconventional or unexpected outcomes. The results are consistent and fall within acceptable ranges, demonstrating the appropriateness and effectiveness of our antenna design and optimization process. Based on appropriate simulation results, we are now ready to proceed with the fabrication of the antenna. The successful fabrication and subsequent evaluation of the antenna's functionality will be a crucial step in validating the performance improvements achieved through the smooth-walled design. Figure 6: VSWR (Voltage Standing Wave Ratio) comparison of corrugated horn antenna and smooth-walled horn antenna.
# A Survey of Information Extraction Based on Deep Learning
## 3 Event Extraction Based on Deep Learning  
Anything occurring in a real event can be considered as an event, and people understand the world by understanding the relationship between events. An event occurs at a specific time and in a particular place, involving one or more participants, and can often be described as a change in state. Event extraction is a sub-task of IE that plays a very important role in the field of knowledge mining. It aims to extract events of interest from unstructured information, identify specific types of events, and present the elements of the established role in a structured form. Event extraction can be further decomposed into four sub-tasks: trigger word recognition, event type classification, meta-identification, and role-classification tasks.  
Event extraction is a deep research topic in the field of IE, which is based on research on entity recognition and relationship extraction. In recent years, to construct event extraction system-related research developments, the traditional event extraction method, namely, based on the mode-matching method, required field qualification to be excessively strong, the effect greatly depended on the data dimension quality and annotation scale factors, artificially built templates were difficult to create, the connection between multiple events was difficult to model, and event authenticity detection was not performed. Building an effective event extraction system from scratch suffers from several difficulties and obstacles. In contrast to traditional methods based on pattern matching, deep learning methods based on neural network models have attracted increasing attention from researchers. Its general logic is to represent the text sequence as a computable multi-dimensional tensor and to realize the classification of event-triggered words and event elements by constructing end-to-end deep learning models. A deep learning method based on the neural network model uses word embedding to represent text, which does not require manual specification of the features and greatly improves the effect of the event extraction task [70]. Embedding represents hard matching of feature engineering with soft matching, and features are automatically extracted using linear and non-linear functions in the network. Neural network models have strong combinatoriality. Transfer learning can combine different models reasonably to learn from each other, which mitigates problems, such as error propagation and implicit feature loss in feature learning. The model based on multi-task learning can predict event trigger words and event elements simultaneously, realize parameter sharing between tasks, use the correlation of the two tasks to improve each other, and finally improve the event extraction effect overall. This chapter summarizes event extraction from two aspects according to the different learning modes and scopes of the model.
# Covert Channel Communication as an Emerging Security Threat in 2.5D/3D Integrated Systems
###### Abstract  
In this paper, first, a broad overview of existing covert channel communication-based security attacks is provided. Such covert channels establish a communication link between two entities that are not authorized to share data. The secret data is encoded into different forms of signals, such as delay, temperature, or hard drive location. These signals and information are then decoded by the receiver to retrieve the secret data, thereby mitigating some of the existing security measures. The important steps of covert channel attacks are described, such as data encoding, communication protocol, data decoding, and models to estimate communication bandwidth and bit error rate. Countermeasures against covert channels and existing covert channel detection techniques are also summarized. In the second part of the paper, the implications of such attacks for emerging packaging technologies, such as 2.5D/3D integration are discussed. Several covert channel threat models for 2.5D/3D ICs are also proposed.  
covert channel; interposer integration; 3D integration 2023
## 3 Adaptive Tracking Control in the Task Space  
In this section, we introduce the adaptive tracking control for robot manipulators. First, we define the estimation errors and the filtered variables for tracking and adaptation. Second, we proposed a controller for tracking control of robot manipulators and an adaptation law for dynamic and kinematic parameters.  
Before deriving the tracking control, we will first define the estimation error  
\[\widetilde{x}\equiv x-\hat{x}\ (4) \tag{4}\]  
and  
\[e_{x}=\hat{x}-x_{d} \tag{5}\]  
\[e_{v}=\hat{x}-\dot{x}_{d} \tag{6}\]  
where \(\hat{x}\) is the estimated position, \(x_{d}\) is the desired position, \(\hat{x}\) is the estimated velocity from the observer defined later, and \(\dot{x}_{d}\) is the desired velocity.  
Denote \(\hat{J}\big{(}q,\hat{\theta}_{k}\big{)}\) as the estimate of Jacobian \(J(q,\theta_{k})\).  
Assuming \(\hat{J}^{-1}\big{(}q,\hat{\theta}_{k}\big{)}\) is non-singular, we can define the reference signal \(\dot{q}_{r}\) and \(\ddot{q}_{r}\):  
\[\dot{q}_{r}=\hat{J}^{-1}\big{(}\dot{x}_{d}-\alpha e_{x}\big{)} \tag{7}\]  
\[\ddot{q}_{r}=\hat{J}^{-1}\big{(}\ddot{x}_{d}-\alpha e_{y}\big{)}+\hat{J}^{-1} \big{(}\dot{x}_{d}-\alpha e_{x}\big{)} \tag{8}\]  
where \(\alpha\) is a constant related to the convergence of the tracking error.  
Define the sliding vector as  
\[s\equiv\dot{q}-\dot{q}_{r} \tag{9}\]  
Notice that the dynamic regressor \(Y_{r}\) can be expressed as  
\[Y_{r}\theta_{d}=M(q)\ddot{q}_{r}+C\big{(}q,\dot{q}\big{)}\dot{q}_{r}+G(q) \tag{10}\]  
We propose the following adaptive tracking control:  
\[\tau=\ -\hat{J}^{T}\big{(}k_{v}e_{v}+k_{p}\,e_{x}\big{)}+Y_{r}\hat{\theta}_{d} \tag{11}\]  
where \(k_{v}\) and \(k_{p}\) are positive constants and \(\hat{\theta}_{d}\) is the estimate of \(\theta_{d}\).  
The update laws for dynamic and kinematic parameters are  
\[\dot{\theta}_{d}=-\Gamma_{\rm d}\ Y_{r}^{T}s \tag{12}\]  
and  
\[\dot{\theta}_{k}=\ \Gamma_{\rm k}Y_{k}^{T}\overline{x} \tag{13}\]  
where \(\Gamma_{d}\) and \(\Gamma_{k}\) are diagonal and positive matrices.  
The observer for the velocity in task space is  
\[\dot{\dot{x}}=\ \dot{J}\dot{q}+\lambda\overline{x} \tag{14}\]  
where \(\lambda\) is a constant related to the convergence of the estimation error.  
The proposed adaptive control scheme is shown in Figure 1.
## 2 Material and Methods
### Test Species: Danio rerio and Enchytraeus cryptic  
Two biological model organisms were used for the bioassays.  
Dario rerio_ (zebrafish--Cyprinidae, Teleoseti) eggs were obtained from a culture maintained at the Department of Biology, University of Aveiro (Aveiro, Portugal). The Zebrafish Facility complies with the Portuguese law (Portaria 1005/02 and Portaria 1131/97), which follows the European Guideline 86/609/EC and conforms with the Council Regulation (EC) No. 2005/01 EC regarding animal transit. It also follows the FELASA (Federation of European Laboratory Animal Science Associations) guidelines and recommendations concerning animal welfare in the care and use of laboratory animals, and in the design and conduct of research projects in which animals are used (including the proper education and training of the people involved in all animal work). Zebrafish adults were kept in a recirculating system with reverse osmosis and activated carbon-filtered tap water, supplemented with instant ocean synthetic salt automatically adjusted for pH and conductivity. The fish were maintained at 27 \(\pm\) 1 \({}^{\circ}\)C, under a 12:12 h h light:dark photoperiod, with conductivity at 750 \(\pm\) 50 \(\upmu\)S/cm, pH at 7.5 \(\pm\) 0.5, salinity of 0.35, and dissolved oxygen at 95% saturation. Adult fish were fed daily with a commercial, artificial diet Gemma Micro 500 (Skretting(r), Burgos, Spain). Reproduction groups were placed in a sloping breeding tank on the day before the test (at 4 pm). In the next morning, two hours after the opening of the illumination, the eggs were collected, counted and cleaned of residues. Zebrafish eggs with normal development (3 h post-fertilization (hpf)) were chosen for the bioassay, using a Stereoscopic Zoom Microscope--SMZ 1500 (Nikon, Amsterdam, The Netherlands). Unfertilized, irregular, or damaged eggs were rejected.  
Enchytraeus crypticus (potworms--Enchytraeidae, Oligochaeta) was also used for the bioassays. _E. crypticus_ adults were obtained from laboratorial cultures maintained at CESAM, University of Aveiro (Aveiro, Portugal). The organisms were kept in agar, consisting of Baci-Agar medium (Agar No. 1, Lab M Limited, Lancashire, UK) and a mixture of four salt solutions (2 mM CaCl\({}_{2}\cdot\) 2H\({}_{2}\)O, 1 mM MgSO\({}_{4}\), 0.08 mM KCl, and 0.75 mM NaHCO\({}_{3}\)) at 20 \(\pm\) 1 \({}^{\circ}\)C, under a 16:8 h light:dark photoperiod. Organisms were fed ground, autoclaved oats twice per week. For the bioassay, synchronized-age cultures of _E. crypticus_ were set by transferring, from the laboratorial cultures, adults with well-developed clitellum into fresh agar plates to lay cocoons. The number of adults to transfer should be 2 \(\frac{1}{2}\) times the number of cocoons necessary. After 2 d, cocoons were relocated to fresh agar plates. Juveniles of 17-19 d were used. According to Directive 2010/63/EU of the European Parliament and of the Council of 22 September 2010, invertebrates (like _E. crypticus_) are allowed biological models for scientific experimentation and are free of an Ethical Statement.
# Robotic System for Post Office Package Handling
## 3 Architecture  
The proposed robotic system for post office package handling, as illustrated in Figure 2, builds upon the TRINITY use-case demonstration of the artificial-intelligence-based stereo vision system [30], consisting of multiple modules. The whole system is based on the robot operating system (ROS) [31] framework, which is used for communicating between modules and controlling the system. The system includes a 6-DOF robot arm with a gripper, an RGB-Depth sensor, and two processing units for computer-vision-based and robotic-motion-based computations.  
The workflow of the system can be described as follows: first, upon request, a 3D image of the workspace is captured with the RGB-Depth sensor and sent to a grasp quality convolutional neural network (GQ-CNN) Dex-Net 4.0 [23] for finding a grasp pose.  
Second, when a pose is found, it is sent to a robot control state machine in a format of X, Y, Z coordinates and a W, X, Y, Z quaternion orientation. The robot control state machine is a set of states that describes the whole process in an overseeable and defined way with predictable outcomes. It consists of multiple robot functions, such as moving to a desired pose or picking and releasing a desired object. It is implemented using the State Machine Asynchronous C++ (SMACC) [32] package.  
Third, after receiving a pose, a grasp path is planned and, if successful, the state machine goes on through a full pick and place cycle. The robot control is implemented using the MoveIt! Motion Planning Framework [33], which manages the planning of paths and communication with the hardware. From the hardware side, the communication is processed using a robot-specific ROS driver by ROS-I in conjunction with a low-level controller provided by the industrial robot. After the object is placed in a predefined location, the process starts anew.
# Study of Mobility Enhancements for RPL in Convergecast Scenarios
###### Abstract  
In recent years, mobility support has become an important requirement in various wireless sensor network (WSN) applications. However, due to the strict resource constraints of power, memory, and processing resources in WSNs, routing protocols are mainly designed without considering mobility. Low-Power and Lossy Networks (LLNs) are a special type of WSNs that tolerate data loss. The Routing Protocol for Low-Power and Lossy Networks (RPL) is a routing protocol for LLNs that adapts IPv6 (Internet Protocol version 6) and runs on top of the IEEE (Institute of Electrical and Electronics Engineers) 802.15.4 standard. RPL supports multipoint-to-point traffic and point-to-multipoint traffic. In this paper we propose a mobility enhancement mechanism in order to improve data collection applications in highly mobile scenarios. The enhancement is based on signal strength monitoring and depth updating in order to improve the routing protocol performance in mobile scenarios. This enhancement helps routing protocols to cope better with topology changes and makes proactive decisions on updating next-hop neighbours. We integrated this mechanism into the RPL and compared it with other existing RPL mobility support enhancements. Results obtained through simulation using Cooja show that our work outperforms other existing RPL mobility supports on different performance metrics. Results also prove the efficiency of our proposal in highly mobile scenarios.  
L 2017 9 86 (10.3390/f9040086) 10.3390/f9040086 17 November 2017
## 3 Simulation-Based System Architecture
### Main Components of the Simulation-Based System  
The architecture of the simulation-based system developed in this paper is presented in Figure 2. It consists of two main components: an intermodal dispatcher which, amongst  
Figure 1: Intermodal 3-leg trip combining ride-pooling (black lines) with public transport (black–white striped line).  
other functions, computes the combined routes, and an intermodal simulator that evaluates the new use case based on simulation. The intermodal dispatcher in turn, is constituted by the Outer Dispatcher, the MaaS Dispatcher (an operational ride-pooling dispatcher developed by the PTV Group and available for internal developments) and the Public Transport Journey Planner (referred to as the PT Journey Planner, which is implemented specifically for this research to cope with the public transport network). The former is the core component that determines the new intermodal routes based on the latter components. The information required to construct the journey legs is obtained through a simulation model with the full definition of the private transport network for the ride-pooling system and the operating timetables for public transport. The intermodal simulator has been developed based on the MaaS Simulator, an exclusive ride-pooling simulator, and adapted to the new intermodal scenario researched here. The same system idea is employed in [12] where the MaaS Simulator is adapted to an electric ride-pooling scenario.  
The overall methodology follows an event-scheduling and batch-processing approach that can be described as follows:  
Trip requests are defined by the origin-destination locations of the trip and the corresponding booking time requirements. As long as the system evolves, they are queued and grouped in batches by the Requests Queue Scheduler (inside the Outer Dispatcher) with previous requests still pending for a journey dispatch. As it will be shown, a batch of requests may consist of newly issued requests, booked requests whose processing time has come, and requests generated by the system itself, corresponding to the pick-up time of a client in the case of a three-leg journey. The task of the Batch Dispatching Scheduler is to control when the batch dispatching requests should be triggered so that the simulator sends a request to the Outer Dispatcher to compute a journey for all the trip requests in the batch. For the trips with a last leg of RP in their intermodal composition, the adopted approach is to \"break\" them, leaving the last leg as an RP request to be included on the events list for future processing with a simple anticipatory strategy. Further details are explained in the next Section 3.4. After that, the simulator updates the new tour plans and a new cycle begins. Regularly, vehicle location updates are sent to the MaaS Dispatcher by the MaaS Simulator according to the vehicle tour plans for the upcoming system cycles.  
Figure 2: Architecture of the intermodal system with the batch dispatching approach.