esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Program
#### 2.3.2 Data Collection  
We made the impedance remain below 10 k\(\Omega\) in the EEG equipment and set the sampling rate at 500 Hz. According to [PERSON] et al. [55], the connection between hearing and language processing is found in the Broca area (Cz and Fz) in the human brain by fMRI during the non-word Shadowing Tasks. The work of [PERSON] et al. [56] gained distinct P300 data from electrode Pz. Further, T3 and T4 of the 10/20 location system lie in the chief auditory cortex [49]. Thus, in this study, the EEG device obtains the user's brainwaves via electrodes T3, T4, Pz, Cz, and Fz on their scalp [16], as shown in Figure 5. Electrode FP2 is grounded, and the reference potential gains from electrodes A1 and A2. Every electrode is Ag/AgCl wet electrode, and the electrode locations refer to the International 10-20 Location System [57; 58; 59].  
The ISO-1032 CE in EEG equipment amplifies the brainwave signal and records the EEG potentials. The control unit, CONTROL-1132, uses a 0.3-15 Hz band-pass filter to filter the signal. Then, the converter card, PCI-1713, shifts the data from analog to digital, and finally, the aBCI system receives all the EEG signals to find the stimulus focused by the user.  
For noise processing, there are two parts. The first is to filter the blink noise. So, there is electrode Fp2 around the eye. If the EMG signal is detected, the system will discard the signal. The second is the AC signal of the power supply. EEG hardware equipment has filtering and voltage stabilization functions to filter. For heartbeat noise, we remove it with relative potential between the sampling electrodes and the reference electrodes (electrodes A1 and A2 around the ears). Therefore, the system can remove most of the noise. Finally, based on the principle of event-related potentials: the potentials obtained by multiple stimulations and then averaged, the system can deal with the remaining small part of the noise and gain a stable and reliable electroencephalogram.  
Figure 4: Average waveforms at Cz of the second stage of the 4 th test run from participant N04: The red circles mark the locations of N200 (marks 1 and 2), while the green circles mark the locations of P300 (marks 3 and 4).
## 3 Proposed Method  
One of the popular feature extraction toolkits for speech and audio analysis and classification is Technical University of Munich's (TUM) open-source feature extractor (openSMILE) [12]. The toolkit first calculates low-level descriptors (LLDs) using a short-time window with the frame length of 30 ms. The LLDs include the calculations of short-time energy, filter-bank energy, cepstral coefficients, and voicing related parameters.  
Once the LLDs are calculated, supra-level features are calculated and used for training classifiers and regression models. The supra-level features are characterized by regression and statistical measures to model the trajectories of the LLDs over time whose duration is much longer than the length of the window used for the LLD calculations [12].  
In previous work [2], it was shown that the choice of a duration for supra-level feature extraction is important and impacts the classification results significantly. When emotional dimensions were automatically classified into three classes per dimension, it was shown that a longer duration generally resulted in a better classification accuracy. In this work, the supra-level features extracted by the openSMILE toolkit are used as baseline features to be compared with the proposed spectral features, and the supra-level features are extracted at the duration of 8 s. The determination on the choice of the duration is discussed in Section 3.2 along with the description on the proposed spectral feature extraction method.  
Deep CNNs could also act as a feature extractor for MER. The embedding vector is extracted by CNNs and a fully connected layer on top of the model is used for predicting different classes. In this study, the deep CNNs are the pre-trained models and transfer learning is utilized for our target task.
# URSIM: Unique Regions for Sketch Map Interpretation and Matching
## 8 Applications  
To the best of our knowledge, URSIM is the first method addressing the problem of map matching between a hand drawn sketch map of an indoor environment and a metric map with a high matching accuracy. Several possible applications of URSIM are described below.  
A sketch interface can be used to control one or more service robots in a building such as an airport or a train station. Such service robots can be provided with metric prior maps, e.g., floor plans or emergency maps. Furthermore, since the robots perform their tasks in the same environment over multiple days, they can also build fairly accurate sensor maps. Using a sketch application on their phones, users could direct the robots to specific places by drawing the environment and sketching the robots' trajectories. The robots could infer their trajectories in the environment by matching the user provided sketch map to their own prior or sensor maps using URSIM.  
In another scenario, sketch maps can be used to enhance existing prior maps, e.g., emergency maps. Working with Dortmund's firemen, we identified that they have access to emergency maps that they use to direct their operations [39]. However, while firemen are proficient with emergency maps, other people may not be, especially in situations of stress. On the other hand, a sketch map is easy and quick to draw and, if using URSIM to find the correspondences, the sketch can be drawn by hand and without major drawing constraints. Once the sketch map of the environment is matched onto the emergency map using URSIM, civilians can give information concerning the position of elements such as gas bottles, the position of victims, or where the fire started. Civilians can also help by sketching the safest or fastest trajectories to points of interest. Being able to quickly relate sketched information to a known map type leads to increased situation awareness and safer operations for both the firemen and the persons they are trying to rescue.  
Furthermore, we previously developed the Auto-Complete Graph (ACG) method [39] to integrate an emergency map into the robot's Simultaneous Localization and Mapping (SLAM). By first matching a sketch map to an emergency map using URSIM and then integrating the emergency map in the SLAM using the ACG method, the information of the sketch map could be included in the robot's map during exploration and used to navigate to points of interest, both by sketching a trajectory or autonomous navigation.
## 4 Lessons Learned from Norwegian Case Studies
### Influence of Emission Factors
[59] argued that lower emission factors can lead to yielding more weight on embodied emission reduction measures and lower weight on operational energy reductions, and vice versa for higher emission factors. It is important to note that even if both case studies (Statens Hus Vadse and Bergen city hall) claim to have used the EU28 + NO factor, and that the NO factor was calculated based on NS3720, the emission factors used in the two projects were different. Providing additional information about the calculation method would have increased the transparency of the scenarios. However, such detailed information was not available. The lessons learnt from the Norwegian ZEB pilots identified the importance of implementation of energy efficiency measures, use of renewable resources, considerations of efficient materials, area reduction measures, prioritizing the use of reused and recycled materials, and the use of long-lasting, low-embodied carbon, and locally available materials to reduce potential embodied GHG emissions [42, 60]. Similar findings were obtained through the analysis of IEA EBC Annex 57 case study collections [40]. This is also in line with the three core elements of circular economy and the 10R circular economy framework, which make up the core elements: (1) prioritize regenerative resources (refuse, reduce, rethink), (2) service life extension (reuse, repair, refurbish, remanufacture), and (3) use waste as a resource (repurpose, recycle, recover) [61].
## 2 Methods and Details of the Technical Assessment
### The Aspects of the Analysis  
Our study deals with the deviation between day-ahead and intraday PV power generation forecasts compared to the real production. The aim is to determine the balancing requirement through real data relative to the forecasts related to 1000 MWp PV system. This research also focused on Li-ion-based and VRFB energy storage systems in connection with the PV grid balancing reduction potential, because these technologies allow an effective, fast, and better integration of VRE sources into the power system, avoiding PV or wind generation curtailment in the case of exceeding generation compared to grid transport capacities [20; 52; 79].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Entso-E Project Number** & **Country** & **Expected Commission Date** & **Current Status** \\ \hline
1011 & Spain & 2020 & in permitting \\
1000 & Austria & 2022 & under construction \\
1002 & Belgium & 2022 & planned but not \\
1006 & Greece & 2023 & permitted yet \\
1012 & Spain & 2023 & in permitting \\
1009 & Lithuania & 2024 & under construction \\
1030 & Ireland & 2024 & planned but not \\
1003 & Bulgaria & 2025 & permitted yet \\
1015 & UK & 2025 & under construction \\
1026 & Austria & 2025 & in permitting \\
1025 & Ireland & 2026 & under construction \\
1014 & UK & 2027 & in permitting \\
1019 & Spain & 2027 & in permitting \\
1029 & Slovenia & 2027 & in permitting \\
1004 & Estonia & 2028 & in permitting \\
1027 & Spain & 2028 & in permitting \\
1001 & Austria & 2034 & in permitting \\ \hline \hline \end{tabular}
\end{table}
Table 2: New pumped hydro storage (PHS) storage projects in the EU based on [20].
# Applications of Wireless Sensor Networks: An Up-to-Date Survey
## 2 Main WSN Applications
### Tsunami Detection  
Another natural disaster is the tsunami waves. It is crucial for coastal regions to be informed early of such a catastrophe. WSNs can be utilized for real time monitoring enabling the authorities to act proactively. Below a Tsunami detection WSN application is presented.  
In [26], a tsunami detection system which utilizes a WSN with underwater sensor nodes deployed in coastal regions is proposed. In order to inform proactively the authorities, the system utilizes a lexical resource messaging system, called SentiWordNet, which is able to provide information extracted from the sensor messages.
# Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator
## 4 Experimental Results
### Determining the Optimal Parameters of a Kalman Filter  
KF parameters, such as the process noise \(Q\) and measurement noise \(R\), can be adjusted according to the environment. A value of 0.005 is applied for the Kalman parameter \(Q\), and experiments are conducted to select \(R\). The experiment collected 1000 signals in 1 m increments from 1 m to 15 m in LOS and NLOS environments. The sampling interval of the transmitter is set to 100 ms. To obtain the optimal \(R\), we experimented with KF with \(R\) set to 1.25, 2.5, and 20. \(R\) is selected by comparing the distance MAE of the results of applying various KF configurations to each distance. Experimental results indicate that in both LOS and NLOS environments, the lowest distance estimation error is observed when \(R\) is set to 20, as shown in Table 4. Therefore, the KF parameter \(R\) is selected as 20.
## 3 Results
### Pre-Filtering Analysis of Simulator Outputs
If we look at the signal to noise ratio (SNR), we find that below 50 km wavelength the energy of the noise is significant with respect to that of the signal (SNR values below 15 dB at wavelengths smaller than 47.6 km, i.e., the energy of the noise accounts for more than 15% of the energy of the signal for these scales). Such low SNR values make particularly challenging the denoising issue for scales below 50 km [36]. Consequently, we expect that the best filter parametrization will be one corresponding to \(\lambda_{c}\) between 47.6 and 60 km.
# Neural Network System for Predicting Anomalous Data in Applied Sensor Systems
## 3 Results
### Evaluation of the Effectiveness of the Results Obtained  
To evaluate the effectiveness [56; 57; 58; 59; 60] of the proposed neural network system for predicting anomalous data in sensor systems (Figure 1), various metrics need to be considered, such as mean squared error (\(MSE\)) and mean absolute error (\(MAE\)), which reflect the accuracy in predicting sensor values. The determination coefficient (\(R^{2}\)) shows how well the model explains data variations, while the correctly replaced outlier percentage and the false positive rate demonstrate the effectiveness of the reconstructor and anomaly detector. Additionally, the missed outlier rate and anomaly detection accuracy provide insight into the system's ability to correctly identify and classify anomalies, while mean response time to anomalies assesses the system's operational speed. In the context of a system for predicting anomalous data, _Precision_ reflects the system's ability to avoid false positives. This is crucial to prevent the operator from being overwhelmed by unnecessary notifications about anomalies that are actually normal data. _Recall_ indicates how well the system can identify all existing anomalies. The _F1-score_ is a combined metric that considers both _Precision_ and _Recall_. The ROC curve displays the true positive rate (\(TPR\)) against the false positive rate (\(FPR\)) across various threshold values. These metrics enable a comprehensive evaluation, ensuring the accurate prediction and reliable detection of anomalies in sensor data. The results evaluating the effectiveness of the proposed neural network system for predicting anomalous data in sensor systems are presented in Table 2. Accordingly, the confusion matrix is given in Table 3.  
True positive (\(TP\)) represents the number of cases where the system correctly identified an anomaly, that is, the system classified an event as an anomaly, and it was indeed an anomaly. True negative (\(TN\)) represents the number of cases where the system correctly identified data as normal (not an anomaly), that is, the system classified an event as normal, and it was indeed normal. False positive (\(FP\)) represents the number of false alarms where the system mistakenly classified normal data as anomalies, that is, the system identified an event as an anomaly, but it was actually not an anomaly. False negative (\(FN\)) represents the number of missed anomalies where the system did not recognize an anomaly, that is, the system classified an event as normal, but it was actually an anomaly.
## 3 Basic Theory
### Asm  
ASM [30] determines a potential trend of feature points according to the law of distribution present in the training image, searching for its target position in the most likely direction. It calculates the average texture \(\overline{G_{ij}}\) and covariance \(S_{G_{ij}}\) corresponding to the calibration point through the global shape model, which is the local texture model of that point. When using this model to search for the best candidate point for a feature point in an unknown image, m grayscale information is sampled along the normal direction. In this way, the search for the best matching position is transformed into finding the matching position between the sampled grayscale vector and the model vector. The corresponding Mahalanobis distance is calculated with Equation (3).  
\[d\Big{(}G^{{}^{\prime}}_{ij}\Big{)}=\Big{(}G^{{}^{\prime}}_{ij}-\overline{G_{ij }}\Big{)}^{T}S^{-1}_{G_{ij}}\Big{(}G^{{}^{\prime}}_{ij}-\overline{G_{ij}}\Big{)} \tag{3}\]  
Among them, \(G^{{}^{\prime}}_{ij}\) is a normalized texture vector obtained by sampling near the unknown image point \(j\), with a superscript of \(-1\) representing the inverse operation. The point corresponding to the minimum value of \(d(G^{{}^{\prime}}_{ij})\) is the best candidate point.  
It first uses marked feature points for training to generate a grayscale model. As shown in Figure 2, the grayscale model (outer line) is used to find the target feature points in the target image (image object), and the suggested points are calculated iteratively in the specified direction (normalized to the model boundary). ASM is simple, fast, and requires very little training data. However, this method still has several drawbacks, including an over-reliance on initial values and a tendency to converge to local optimality. In this paper, ASM is applied to an image segmentation postprocessing task in order to highlight its advantages and find ways of avoiding its shortcomings.  
Figure 1: The structure of a convolutional neural network [27].
# Gap Analysis of Ambient Electromagnetic Noise Measurements Stored in the ITU Data Banks
## 1 Introduction  
Ambient electromagnetic noise or 'radio noise' is the main limiting factor for advanced sensor systems that observe weak signals below 20 MHz, even in quiet rural locations. At these frequencies, at least when using an efficient transducer (antenna) of the sensor, the power produced by the external noise field exceeds that of the internal noise generated by the first amplification stages [1] (p. 766). In densely populated areas, the dominance of radio noise may extend beyond 500 MHz [2]. Examples of such sensor systems are radio equipment for security, health and safety, navigation [3] radio interception and direction finding [4]. Scientific instruments for radio astronomy [5], and for upper atmospheric and space weather observations--such as ionosondes, RIO-meters, polarimetric sensors [6] and incoherent scatter radars--may also be affected.
## 3 Photon Beamlines
### Photon Diagnostics
#### 3.3.3 Spectral Diagnostics  
As with the beam position, intensity, and temporal properties, the spectral content of the SASE beam changes on a shot-to-shot basis. Since both experimental data analysis and accelerator operation often benefit from high-resolution spectral information about the FEL beam, the SwissFEL photonics team designed a photon single-shot spectrometer (PSSS) [110]. The PSSS is meant to non-destructively measure the hard X-ray photon spectrum on a shot-to-shot basis.  
The basic operating principle of the PSSS is simple: the FEL beam passes through a thin diamond transmission grating that splits off the first order light and illuminates a bent crystal spectrometer downstream. The spectrometer is meant to function at photon energies above 4 keV, covering a 0.5% bandwidth with a \(\Delta E/E\) resolving power of \(10^{-5}\) to \(10^{-4}\). The device has been successfully tested in its prototype phase at LCLS [111], and it will be fully operational at SwissFEL. Further concepts for spectral measurements at longer wavelengths are being developed with similar properties, and will be implemented as they are commissioned and fully developed.
# Named Data Networking Based Disaster Response Support System over Edge Computing Infrastructure +
## 3 System Design
### NDN Device Management Lsing KubeEdge as Edge Computing Platform  
Effective disaster response task management requires the commander at the management center to have an overview picture of what is happening around disaster areas. Hence, we utilize KubeEdge device management feature to create an NDN responder device management system for the centralized commander on the cloud. Through this system, the commander can receive the reported mission status of all responders (current event, requirement, location) from their devices. Based on this information, the commander can directly update responders' missions, roles from cloud to responder devices. Moreover, we take advantage of the reliable cloud-edge transmission feature of KubeEdge so that no information (reports from devices and updates from cloud) between cloud and devices is dropped in intermittent network conditions.  
The information exchange mechanism between cloud and responder devices is demonstrated in Figure 8. Update information from the commander at cloud will be sent to Edgecore and publish to Mosquito topics here. Subscribed devices will receive updates. Meanwhile, reported information from responders will be processed by the device-mapper and published to Mosquito topic. Edgecore gets reported data from subscriptions and sends it to the cloud. Commander can get data from the Kubernetes API Server.
# Study on Share Rate of Support Structure for Super-Large Span Twin Tunnels with Small Interval
## 3 Theoretical Calculation of Surrounding Rock Pressure  
### Failure Mode and Assumptions  
In Chinese standard [1], the method of calculating surrounding rock pressure of normal tunnels (usually including two or three lanes) is given. This method can be generalized to super-large span twin tunnels (four lanes each hole, wider than a normal tunnel). The failure mode, necessary assumptions, and theoretical method are introduced below.  
The theoretical sliding failure mode of the surrounding rock of the twin tunnels is shown in Figure 8. \(W_{1}\), \(W_{2}\), and \(W_{3}\) are the gravity of the rock mass \(A^{\prime}C^{\prime}E^{\prime}\) (_ACE_), _IKOM_, and \(I^{\prime}KOM^{\prime}\), respectively. \(T_{1}\) and \(T_{2}\) are the frictional force caused by the soil on both sides of the tunnel to resist the settlement of the vault. Furthermore, \(\varphi_{c}\) is the simplified internal friction angle of the surrounding rock. According to the Chinese standard, \(\varphi_{c}\) is usually greater than the real internal friction angle (\(\varphi\)). When using \(\varphi_{c}\), it is valid regardless of the cohesion force). \(\theta\) is determined by referring to the Chinese standard. Here are some explanation and hypotheses for this sliding failure mode:  
1. Suppose that the ground was horizontal, the rock mass was homogenous, and isotropic and the twin tunnel was symmetry and parallel. Moreover, excavation of left and right holes is sequent and full section.
2. The excavation of the first hole is similar to an ordinary single-hole tunnel. It means that the fracture planes on both sides of the first hole, which are shown as \(A^{\prime}C^{\prime}\) and \(M^{\prime}J^{\prime}\) in Figure 8, are two inclined straight planes, and at an angle of \(\beta_{1}\) to the horizon. In addition, the pressure inside and outside (shown in Figure 8) is symmetric.
3. When the following hole is excavated, the fracture plane at the outside of the hole (_AC_) is at angle \(\beta_{1}\) to the horizon, while at the inside of the hole the angle of the fracture plane (_MO_) is assumed as \(\beta_{2}\). Focus on the triangle \(O|J^{\prime}\), when the following hole is excavated, it is inclined to slide down along the plane \(JM\). However, since the excavation of the first hole has induced a relative slippage at the plane \(OJ^{\prime}\) and undermines the cohesion force along the plane \(OJ^{\prime}\), usually the triangle \(O|J^{\prime}\) will not slide and fracture along the plane \(JO\). Instead, the tensile fracture plane will be formed in the triangle \(O|J^{\prime}\), which is assumed as a vertical plane (\(OK\)). In summary, the fracture plane at the inside of the following hole is assumed as _KOM_.
4. According to the sliding trend of the triangle \(O|J^{\prime}\) caused by the sequent excavation of the twin tunnel, and based on the theory of soil mechanics, the interactive force (\(N\)) in normal direction at the fracture plane \(OK\) must be less than the earth pressure at-rest. For safety, let \(N\) equal 0.