esa-sceva/satcom-chunk-collection huggingface dataset:
# Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting
## 2 Materials and Methods
### Datasets
#### 2.1.2 Electric Vehicles Power Consumption  
The second dataset gathers information about power consumption in charging stations for electric vehicles (EV) in Spain. This data was also obtained from Red Electrica de Espana and can be found at [30]. In the near future, governments will have to build infrastructures that can fulfil the demands of the increasing EV fleet. Given their limited autonomy, the prediction of EV consumption seems crucial to efficiently manage the power supply. In this article, we followed the same steps to generate the EV demand time series as in [31]. The data was collected hourly and ranges from 2015-03-02T00:00 to 2016-05-31T23:00. For each geographical area, we obtained a single value of power consumption for every hour. Later, in order to obtain a single time series, the different zones were aggregated giving the total energy consumption of the Spanish EV. The forecasting horizon was set to be 48 h, which involves a prediction of 48 time-steps. The dataset is divided into two sets: the training set contains 8759 samples corresponding to the period from 2015-03-02T00:00 to 2016-02-29T23:00; and the test set contains 2207 samples comprising the period from 2016-03-01T00:00 to 2016-05-31T23:00.  
As can be seen in Figure 2, the time series presents weekly and daily patterns. Since electric vehicles are most commonly charged at night, the time series presents peak values of demand during the first six hours of each day. Only Sundays and Mondays present a slightly different pattern, as it is displayed in Figure 1(c).  
Figure 1: Line plots illustrating the electric demand time series data at different scales.
# Automated Docking System for LNG Loading Arm Based on Machine Vision and Multi-Sensor Fusion
## 3 Design of a Vision-Based Deep Learning Loading-Arm Positioning System
### Monocular Camera Positioning Model  
The imaging model of a monocular camera can establish a correspondence between the center of an image and the center of space, but complete implementation of this relationship requires depth information at that point. However, since monocular cameras cannot directly obtain depth information, this study uses the method of extracting the features of the circular flange surface to calculate the 3D coordinates of any point in the circular plane from the circular feature information. Under close-range shooting conditions, the feature information of the circular flange surface in the image may be incomplete due to limited viewing angle or image blur, resulting in a deviation between the fitted circular plane and the actual plane, which affects the positioning accuracy. To compensate for this deficiency, an ultrasonic sensor is introduced to provide accurate depth compensation in combination with visual information, so as to further improve the reliability and accuracy of close-range positioning.
# FLGQM: Robust Federated Learning Based on Geometric and Qualitative Metrics
## 2 Materials and Methods  
### Related Work  
#### 2.1.1 Federated Learning  
There is a set of nodes/clients \(C=c_{1},\ldots,c_{k},\ldots,c_{K}\) in federated learning, where each client has its own data \(\mathbb{D}_{k}=\{(a_{1},b_{1}),\ldots,(a_{i},b_{i}),\ldots,(a_{n_{k}},b_{n_{k}})\}\), where \(a_{i}\) is the \(i\)th data, and \(b_{i}\) represents the true label. \(|\mathbb{D}_{k}|=n_{k}\). \(n_{k}\) is the number of training data owned by \(c_{k}\). \(n=\sum_{k\in K}\ n_{k}\) represents the cumulative number of samples owned by all clients, while the server is denoted as \(S\). Specifically, all clients train a classifier \(f(w)\), where \(w\) is the global model obtained by aggregation, and this model is expected to have good results on the test set \(D_{test}\). The objective of the server is as follows.  
\[\min_{w}l(w)=\sum_{k=1}^{K}\frac{n_{k}}{n}l_{k}(w) \tag{1}\]  
where \(l_{k}(w)=\frac{1}{n_{k}}\sum_{i\in\mathbb{D}_{k}}l_{i}(w)\). \(l_{i}(\theta)=l(b_{i},f_{w}(a_{i})\) is the loss predicted with model parameter \(w\) for data \((a_{i},b_{i})\).
# A Specification-Based IDS for Detecting Attacks on RPL-Based Network Topology
## 3 A specification-Based IDS for RPL to Detect the Topology Attacks
### Profiling RPL
It only adds to the FinalSpe the states and transitions that it does not have yet. It also compares the statistic pattern and only record the pattern with significant different trend. At the end of Algorithm 2, we obtain a Specification-based module as shown in Figure 2 below. The generated RPL specification module consists of 8 states: The _sending DIS, sending DIO, receiving DIO (DIOr), sending no-change DIO, processing DIOs, sending new (changed) DIO, Sending DAO, repair_, and _new node joining_. From the statistical data recorded, we obtain the following observations:  
1. Nodes only move to the _Sending DIS_ state when it first starts, joins, or involves in a link repair procedure. As a result, nodes only visit the _Sending DIS_ state few times during the network performance. 2. Nodes in the center tend to have more transitions to nodes in the border. The reasons are that center nodes have more neighbors than border nodes, while their neighbors are also more likely to update the routing information than the border nodes' neighbors. 3. In the _processing DIOs_ state, nodes have to strictly follow the rank rule. 4. After a long enough time of running, when the network topology becomes stable, the node will visit mostly the _Sending no-change DIO_ state. However, such visit is not too often, because the DIO trickle time is always extended in a stable network. 5. The five states _sending DIS, sending new DIO, sending DAO, repair_, and _new node joining_ indicate the instability of the network topology. When the node is in one of these states, the transitions are expected to happen more often, because the DIO trickle time is set to minimum. Figure 2: Specification-based IDS for RPL through trace file analysis. In the next section, we will use the knowledge obtained from this section to design and implement a Specification-based IDS in the RPL network.
## 2 Method
### Motion Classification Method  
Conventional myoelectric prosthetic hands are typically controlled by EMG signals from the residual muscles of upper limb amputees. However, because residual muscles generate very few EMG signals, upper limb amputees cannot perform various hand gestures and grip motions with these hands. By contrast, the developed hand can perform eight hand gestures and grip motions using only two EMG signals. Four motion states are used to perform these eight hand gestures and grip motions: a grip motion state and a gesture motion state classified by the EMG signal, and an adduction state and an abduction state respectively classified into the adduction/abduction motions of the thumb. To achieve this, as shown in Table 2, two EMG signals are divided into three control signals: a co-contraction signal (CC) that is used to classify the grip motion state and the gesture motion state of the prosthetic hand, and an open signal (OS) and an EMG close signal (CS) that are used to classify the eight hand gestures and grip motions.  
The most important hand function for upper limb amputees is the grip motion. Therefore, the normal motion state of the developed hand is the grip motion state. To perform hand gestures in the grip motion state, the developed hand should permit EMG CC signals. Additionally, to perform four grip motions in the basic grip motion state, the thumb must be classified into the adduction and the abduction states. These states are classified based on the value of the Hall Sensor (HS) mounted inside the palm of the developed hand. As shown in Figure 5, the thumb rotation angle can be classified into the adduction state and the 90\({}^{\circ}\) abduction state.  
\begin{table}
\begin{tabular}{c c} \hline
**Command** & **Type of Function** \\ \hline Co-Contraction (CC) & Mode change \\ \hline Open Signal (OS) & Motion (thumb extension state) \\ \hline Close Signal (CS) & Motion (thumb flexion state) \\ \hline \end{tabular}
\end{table}
Table 2: EMG control signal classification.  
Figure 5: Adduction and abduction motion states of the thumb. The blue line shows the HS 1 value, and the red line shows the HS2 value.
## 7 Future Research Directions
### Integration of Real-Time Data and Dynamic Routing Updates  
One promising area for future research in multi-objective message routing for EnFVs is the integration of real-time data and dynamic routing updates [24; 80]. Real-time data, such as traffic conditions, weather information, vehicle availability, and charging station status, can significantly impact the performance and efficiency of routing decisions. Incorporating such dynamic factors into the GA-based optimization process can enable adaptive and responsive routing strategies that better account for the current operating conditions of the vehicles and the transportation network. To achieve this integration, researchers can explore various avenues. Firstly, the development of efficient data collection mechanisms and sensors can provide real-time information on key parameters, such as traffic congestion, weather patterns, and charging station availability [20]. These sensors could be integrated into the vehicles or the surrounding infrastructure to gather relevant data continuously.  
Secondly, novel algorithms and techniques need to be devised to effectively process and utilize the real-time data [1,82]. Machine learning approaches, such as reinforcement learning, deep learning, or online learning, can be explored to develop models that can learn and adapt to changing conditions in real-time. These models can be integrated into the GA framework to dynamically update the routing decisions based on the latest available data. Furthermore, the scalability of the GA approach needs to be considered when incorporating real-time data [38]. Efficient techniques for handling large-scale and complex routing problems in real-time must be explored to ensure the practicality and feasibility of the proposed solutions.  
In conclusion, integrating real-time data and dynamic routing updates into the GA-based optimization framework is a promising direction for future research. This integration can enhance the adaptability, responsiveness, and efficiency of multi-objective message routing in EnFVs. By leveraging real-time information on traffic, weather, and vehicle availability, researchers can develop intelligent routing strategies that effectively balance multiple objectives in dynamic transportation systems.
## 5 Conclusions and Future Works  
The dramatic increase of EVs exerts significant pressures to the power system operators, and an accurate load forecasting model is a key solution to this problem. In this paper, four deep learning methods were used to predict the short-term charging load of real-world EV charging stations. The results show that the models have demonstrated effectiveness on the dataset, and the one hidden layer GRU model has the best performance compared with the other three models. However, these results cannot prove which model has an absolute advantage in the application. The major reason is that only limited data of one EV charging station was used in the model training, and the number of EVs only accounts for a small part of the actual vehicle numbers. In the future, with the increasing number of EVs and on-line charging behaviors, the amount of data obtained will increase accordingly. Therefore, more influencing factors and an increased amount of data will be considered to increase the dimensions and quality of the features and the quantity of the inputs. New models are promising to improve the prediction speed as well as the forecasting accuracy.  
[PERSON]. ([PERSON]) and [PERSON] proposes the algorithms and draft the paper. [PERSON] and [PERSON]. ([PERSON]) prepared the data and modified the paper, [PERSON] provided the data and setup the experimental environment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Hidden Layers** & **Train-NRMSE (\%)** & **Test-NRMSE (\%)** & **Train-NMAE (\%)** & **Test-NMAE (\%)** \\ \hline \multirow{4}{*}{DNN} & 2 & 2.05 & 3.69 & 1.06 & 1.32 \\  & 3 & 1.97 & 3.79 & 0.50 & 0.94 \\  & 4 & 1.98 & 3.81 & 0.46 & 0.92 \\  & 1 & 1.50 & 2.91 & 0.62 & 0.91 \\ RNN & 2 & 1.52 & 2.91 & 0.70 & 1.01 \\  & 3 & 1.61 & 2.96 & 0.84 & 1.19 \\  & 1 & 1.71 & 3.36 & 0.59 & 0.90 \\ LSTM & 2 & 1.71 & 3.36 & 0.79 & 1.11 \\  & 3 & 1.74 & 3.39 & 0.68 & 1.01 \\  & 1 & 1.48 & 2.89 & 0.47 & 0.77 \\ GRU & 2 & 1.56 & 2.92 & 0.48 & 0.78 \\  & 3 & 1.52 & 2.91 & 0.51 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of deep learning based methods. NRMSE: normalized root mean squared error, NMAE: normalized mean absolute error, DNN: deep neural networks, RNN: recurrent neural networks, LSTM: long short term memory; GRU: gated recurrent units.  
Figure 10: Predictions and REP of the winter scenario.  
**Funding:** This research is financially supported by China NSFC under grants 51607177 and 61571401, Natural Science Foundation of Guangdong Province under grants 2018A030310671, China Post-doctoral Science Foundation (2018M631005), Scientific and technological innovation talents in Henan Universities (18 HASTIT021).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Impact of Latency on QoE, Performance, and Collaboration in Interactive Multi-User Virtual Reality
## 3 Materials and Methods
### Experimental Methodology  
This section describes the experimental methodology followed in this study. First, a description of the adopted use case is provided. Next, the independent variables are discussed, followed by an overview of the objective and subjective evaluation methodology. Finally, the experimental flow is presented.
## 4 Evaluation
### Performance Comparison with Various Parameters
In our experiment for supervised learning, the performance was better when adding noise only in the period of \(n\geq n_{act\_ems}\), instead of adding noise throughout the training process. As shown in Table 7, when noise is added throughout the training process to both models, the F\({}_{1}\)-score is improved, but the log loss is slightly degraded. Both performance indices, the F\({}_{1}\)-score and the log loss, improve when noise is applied only to the student model, and there is a greater improvement when the noise application interval is adjusted to \(n\geq n_{act\_ems}\).
## 3 Earth Surface Modelling Advances and Links with EO Datasets
### Land-Atmosphere Coupling
The plot shows a composite of the so-called Relative Warming Rate (RWR) as a function of the amount of precipitation during the preceding event for March-April-May (MAM, **a**) and June-July-August (JJA, **b**). RWR quantifies the increase in dry spell land surface temperature relative to air temperature, and is a measure for the evaporation regime of the land surface. errors depend on the vegetation type, or more precisely on the momentum roughness length of each vegetation type. The overestimation of the near-surface wind speed for most of the vegetation types suggests that the values used for the momentum roughness length were too low. This motivated the revision of these values based on theoretical considerations and SYNOP observations of wind speed at 10 m. The basic idea was to search, for each vegetation type, for a new value of the momentum roughness length for which the mean 10-m wind speed forecast error with respect to SYNOP observations drops to zero in near-neutral conditions. This calibration showed that the momentum roughness length values should be increased for nine and decreased for one of the 18 vegetation types characterizing land areas. The newly derived values were introduced in IFS cycle 37r3 (November 2011). As the roughness length for momentum was on average increased, the roughness length for heat was decreased in order to account for terrain heterogeneity. The use of the new momentum roughness length values reduced significantly the 10-m wind speed forecast errors, especially for the predominant vegetation types, but also on average over continental areas. The improvement for Europe was clear both in the pre-operational testing and in the operational verification afterwards. The reduction of the heat roughness length had also a positive impact on the 2-m temperatures, leading to a small warming during night-time and cooling during daytime over the continental regions for both winter and summer, which reduced the forecast biases over Europe (2016). Near surface temperature and humidity biases in weather are handled by assimilating SYNOP temperature and relative humidity and therefore optimising the 2 m forecast performance on given weather conditions (Kumar et al., 2012).
# Multi-Resolution and Semantic-Aware Bidirectional Adapter for Multi-Scale Object Detection
## 3 The Proposed Method
### Bidirectional Fine-Grained Interaction
The detailed process can be articulated as:  
\[R_{i}=Tanh\{Tanh[\Phi(avg(P_{i}))]+Tanh[\Phi(max(P_{i}))]\}, \tag{12}\]  
where \(R_{i}\) denotes the semantic prompt map of high-level features, and \(avg\) and \(max\) represent the average pooling and max pooling operation block. Then, \(P_{i-1}^{\prime}\) learns the semantic knowledge according to the prompt map. The process can be written as:  
\[P_{i-1}=\mathcal{G}(\Phi(P_{i-1}^{\prime}),F_{mul}(P_{i-1}^{\prime},R_{i})). \tag{13}\]  
The proposed bidirectional fine-grained interaction takes full advantage of multi-scale features. During the bidirectional interaction process, both semantic and spatial information can be effectively completed among different levels. The low-level layers, which possess high-resolution information, effectively capture salient location information via pixel-level filtering at the pixel level. This information is then utilized to establish a bottom-up information flow. This aids in enhancing the essential location information of objects within high-level layers. Conversely, the high-level layers, abundant in semantic information, contribute significant semantic prompts when subjected to channel-wise prompting at the channel level. The prominent semantic prompt can be effectively transmitted to the low-level layers with minimal loss. BGI promotes adequate interaction among different levels with abundant multi-scale information.
## 1 Introduction
CNTs are not used for the purpose of changing the physical properties of concrete, but CNTs affect the properties of concrete such as hydration, strength, and shrinkage. Therefore, the effect of CNTs on the properties of concrete should be investigated to use CNTs for making multifunctional concrete. A literature review revealed that CNTs accelerate the hydration reaction of cement particles and reinforce matrices such as steel fibers and synthetic fibers when used as fillers for cement materials. This indicates that CNTs have a significant influence on the volumetric properties of cement, such as autogenous shrinkage as well as its mechanical properties. These must be examined to effectively use CNTs as construction materials, but the related research is insufficient. Several studies have investigated the mechanism of CNTs on the mechanical properties and hydration of cement composites. However, studies investigating the mechanism of autogenous shrinkage of CNT-reinforced cement composites are still lacking. The purpose of this study is to investigate the mechanism by which autogenous shrinkage is changed by CNTs. In this study, the effect of the CNT content on the hydration and autogenous shrinkage of cement composites was investigated. To this end, CNT-reinforced cement composites were fabricated using ordinary Portland cement (OPC) and fly ash (FA) as binders and adding 0.0, 0.1, 0.3, and 0.5% multi-walled CNTs (MWCNTs). The hydration characteristics of CNT-reinforced cement composites were investigated by analyzing the setting, heat of hydration, and scanning electron microscopy (SEM) images. In addition, experiments were performed to investigate the autogenous shrinkage and internal relative humidity (IRH). Through the experiments, the effect of CNTs on the autogenous shrinkage of cement composites was investigated.
## 2 Materials and Methods  
Figure 1 shows a block diagram overview of the modelling approach to predict AN firing rates for different hearing prosthesis options in a personalized way, as well as to obtain for each prosthesis option the LAP scores. First, the model of [PERSON] et al. of acoustical stimulation was used three times with different hearing loss parameters \(\Psi\) and input stimuli [8]. The AN firing rates of the NH reference \(\mathrm{AN_{nh}(n,t)}\) were obtained with hearing parameters reflecting NH (\(\Psi_{\mathrm{nh}}\)). The nerve signals of impaired (unaided) hearing \(\mathrm{AN_{ua}(n,t)}\) were obtained with hearing parameters reflecting impaired hearing (\(\Psi_{\mathrm{imp}}\)). The input signal \(s(t)\) was pre-amplified, owing to the amplification parameters \(L_{f}\) and \(L_{s}\) to obtain the firing rates of hearing with acoustical amplification \(\mathrm{AN_{ac}(n,t)}\). Second, the model of [PERSON] was used to obtain firing rates triggered by electrical  
stimulation \(\text{AN}_{\text{el}}(\text{n},\text{t})\)[13]. The fitting parameter was the CI insertion depth D. The output was added to the low-frequency outputs of the models of acoustical stimulation \(\text{AN}_{\text{ua}}(\text{n},\text{t})\) to obtain firing rates including residual low-frequency hearing [17]. The residual hearing was simulated with stimuli that were attenuated by 20 dB [18]. To obtain firing rates for electrical stimulation combined with acoustic amplification, i.e., \(\text{AN}_{\text{co}}(\text{n},\text{t})\), the electrical stimulation model's output was added to the low-frequency part of \(\text{AN}_{\text{ac}}(\text{n},\text{t})\). All nerve signals were compared to the NH reference, and their LAP scores were obtained. Details regarding the models of the firing rates, the acoustic amplification, the used stimuli, and the automatic fitting of the auditory prostheses are disclosed hereafter.  
Figure 1: Overview of the modelling approach to predict AN firing rates for different hearing prosthesis options in a personalized way, as well as to obtain the LAP scores for each prosthesis option. The stimulus s(t) is put into models of acoustical stimulation, acoustical amplification, and electrical stimulation to obtain place–time representations of AN firing rates. Firing rates are obtained for the normal hearing reference (nh), unaided hearing (ua), electrical stimulation including residual acoustical hearing (el), sole acoustical amplification (ac), and electrical stimulation combined with acoustical amplification (co). The loss of action potentials (LAP) is obtained for ua, el, ac, and co, by comparing the corresponding AN firing rates with nh. * For modelling low-frequency residual hearing with a cochlear implant, a 20 dB attenuation is applied to the stimulus.
# Smart Antenna for Application in UAVs
## 5 Construction and Implementation of the Smart Antenna  
### Smart Antenna Construction  
The implementation of the communication system was carried out in two phases: antenna construction, and the construction of the control system.  
The P-ESPAR antenna was built in the laboratories of the Instituto Superior Tecnico. In Figure 18, the top of the antenna is shown and, in Figure 19, its bottom part.  
Figure 17: Flowchart of the process.  
The circuit was printed in the IST at Taguspark. The circuit uses three cable terminal board ports with three outputs that are used as terminals of the digital potentiometer.