esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Method for Identification of Critical Scenario Components
#### 3.1.2 Driving Database  
In recent years, data on daily driving behavior have been collected on a large scale by many naturalistic driving studies (NDSs) [40; 41; 42]. In an NDS, vehicle signals, such as speed and acceleration, and images from cameras that show the front view of the vehicle and driver are constantly recorded. Therefore, when a driver causes an accident or in the case of a near-miss incident, it is possible to understand the behavior of the driver and surrounding environment leading to the event. However, although the constantly recorded data collected in the NDS contain long-term records of daily driving, accidents and near-miss incidents are rare.  
In contrast to databases of constantly recorded data in NDSs, some databases contain driving data recorded in response to events, such as emergency braking and steering using an event recorder. Unlike the database collected in an NDS, this database contains many accidents and near-miss incidents. Similarly to the data recorded in the NDS, the vehicle signals and camera images are recorded. However, the data are recorded only for a limited time before and after the event. Both databases have advantages and disadvantages in identifying critical scenarios. In this study, the latter type was adopted to understand critical scenario components from accident and near-miss incident data.  
The near-miss incident database managed by the Smart Mobility Research Center at TUAT was adopted as the driving database collected with an event recorder. This database contains accident and near-miss incident data of taxis recorded by an event-type drive recorder. Recording starts automatically when the deceleration value in the front/rear direction exceeds 0.45 G. Data from 10 s before and 5 s after the start trigger are recorded. The database contains vehicle information, such as speed and acceleration, and camera images of the vehicle's front view and driver's face. In addition, experienced evaluators annotated the data with information about the driver who caused the accident/near-miss incident and the surrounding environment. A near-miss incident is not an accident, but a case in which a vehicle almost collided with another traffic participant. Thus, the critical factors leading to accidents can be observed. Previous studies have shown that the ratio of accident types collected in the database and traffic accidents in Japan are identical,  
Figure 1: Framework of the proposed method for identification of critical scenario components using a driving database and a driver error analysis method.  
and the near-miss incident data in the database are effective for factor analysis of traffic accidents [43; 44].
## 5 Discussion  
The UAV-SfM approach can generate orthoimages in a short time, with good quality and accuracy, and they are simple in nature, such that non-expert users can also use them. In this study, it was confirmed that SfM performances from UAV imageries could be implemented for modeling 3D cultural heritage. The UAV-SfM's application for pagoda patterns in Thailand provides archaeologists and local authorities with high-resolution geoinformation that can be used to support tools for rapid mapping of cultural heritage buildings.  
The quality of GCPs for verifying and checking accuracy-based georeferencing depends on the number of GCPs and their distribution over the study area [30; 31; 32]. Our results showed that the calculated RMSE values of 12 GCPs were 0.028 m in the horizontal direction and 0.230 m in the vertical direction, which are acceptable results for georeferencing.  
The accuracy comparison between the point clouds produced from UAV imagery and the terrestrial laser scanning data uses the mean error of 20 check points. The output of UAV on the pagoda compared with the TLS point clouds found that the calculated RMSE value of TLS and UAV point clouds were almost the same, owing to the comparable size of both the Prang and Chedi patterns. In addition, the distribution of checkpoints were only on the bottom base of the pagoda.  
Figure 11: Comparison of the terrestrial and UAVs point cloud of the Chedi structure: (**a**) Base of the Chedi from TLS. (**b**) Top of the Chedi from TLS. (**c**) Base of the Chedi from UAV. (**d**) Top of the Chedi from UAV.  
Figure 10: Comparison of the terrestrial and UAVs point cloud of the Prang structure: (**a**) Base of the Prang from TLS. (**b**) Top of the Prang from TLS. (**c**) Base of the Prang from UAV. (**d**) Top of the Prang from UAV.
## 3 Materials and Methods  
### Assumptions and Setup  
In order to validate our concept hypothesis regarding its user acceptance, an empirical survey was conducted in the form of a questionnaire distributed online to 100 people. Data were collected within one month (February-March 2021). Typical questions concerning breaking news capturing, reporting, and sharing were answered, retrieving vital feedback. Hence, background- and general-interest-related questions were structured in a categorical form of potential answers, with 5-point Likert scales (1-5, from \"Totally Disagree\" to \"Totally Agree\"). Binary values (i.e., gender) and higher-dimension lists were also involved. The items were divided into three subsets, with the former involving questions regarding the current state of/trends in (breaking) news reporting (Q1-Q4), the second implicating questions on the envisaged modalities and usability characteristics of the proposed service (Q5-Q9), and the latter containing basic characteristics/demographics of the users (Q10-Q17). The survey formation was validated after discussions and focus groups with representative users. Specifically, both professional and citizen journalists were involved, as well as people from the broader journalistic and news reporting field. The survey was updated based on the received feedback, investigating the audience interest in a system that incorporates mechanisms for automatic breaking news detection and their intention to contribute with their content. The gathered information was used for the estimation of the anticipated dynamics of the proposed architectural approach. An overview of the chosen inquiries is presented here, aiming to justify the adoption and configuration of the formed questionnaire. Detailed information regarding this survey is provided in the associated Results section, along with the assessment outcomes.  
During the survey preparation, all ethical approval procedures and rules suggested by the \"Committee on Research Ethics and Conduct\" of the Aristotle University of Thessaloniki were followed. The respective guidelines and information are available online at [[https://www.rc.auth.gr/ed/](https://www.rc.auth.gr/ed/)]([https://www.rc.auth.gr/ed/](https://www.rc.auth.gr/ed/)) (accessed on 9 July 2021). Moreover, the Declaration of Helsinki and the MDPI directions for the case of pure observatory studies were also taken into account. Specifically, the formed questionnaire was fully anonymized, and the potential participants were informed that they agreed to the stated terms upon sending their final answers. All participants had the option of quitting any time without submitting any data to the system.
# Imaging Flow Velocimetry with Laser Mie Scattering
## 3 Developed Measurement Techniques and Their Imaging Evolution
### Measurement Techniques Using the Doppler Principle
#### 3.2.1 Amplitude-Based Signal Evaluation
For this reason, the development of PDV systems with a two-ray interferometer as the interferometric filter and with high-speed cameras seems worth being studied in the future.
## 4 Discussion
Our second key finding is that the points of interest we identified--based on their TF image's UAE--presented a 7-12 mm accuracy in the localization of the EZ (see Figure 6), with the most accurate findings (7 mm) obtained when using a machine learning algorithm (SVM) to combine information from all the layers of the deep learning network (VGGnet). According to the Desikan-Killiany atlas map, the smallest area in the brain, such as the temporal pole, has an estimated radius of 6 mm, and a large area, such as the superior frontal gyrus, has an estimated radius of 30 mm. In addition, the typical gyral width of the para-hippocampal gyrus is between 11 and 21 mm [26; 47; 48]. Thus, an average localization error of 7 mm is very unlikely to cause a mis-localization at the anatomical level of the brain. Even though several studies have proposed novel methods to localize the EZ for epilepsy surgery planning, only a few of them have reported actual distance from the EZ (or resection) of the proposed methods. Recent works on localizing the EZ using electrical source imaging and/or iEEG resulted in errors of 5 mm [48] and 9 mm [49] or reported distances within 10 mm as concordant [50]. Our data thus suggest that the points of interest we identified on the patients' MRI are concordant with the EZ and are comparable to the most recent results in epilepsy surgery. The presented methodology shows great potential in the field of epilepsy care and the strategic planning for neurosurgery in individuals with drug-resistant epilepsy. The development of a computerized tool that uses deep learning-based visual complexity metrics to analyze interictal iEEG TF images represents a possible breakthrough (if further validatedon larger cohorts) with the potential to significantly enhance the pre-surgical decision-making process and the effectiveness of surgeries in patients with drug-resistant epilepsy. The proposed methods and algorithms overcome the longstanding challenges of relying on ictal data for EZ localization (where the challenge is due to the unpredictability of seizures). By introducing a multi-step approach to analyze interictal iEEG data, we propose a comprehensive automated solution for identifying iEEG electrodes with low TF visual complexity indicative of underlying brain epileptogenicity. This methodology has the potential to improve the pre-surgical planning when seizures are not captured and thus reduce the reliance on long-term ictal recordings. In conclusion, the study's innovative approach has the potential to improve drug-resistant epilepsy treatment, offering an accurate and automated means to localize the EZ in each patient's MRI via brief iEEG data with an average localization accuracy of 7 mm. Further validation of the proposed method on a larger cohort could ultimately improve the pre-surgical evaluation of patients with drug-resistant epilepsy and enhance epilepsy surgery planning.
## 4 Results and Discussion
### Frequency Response Curves (FRC)
Figure 8 illustrates the influence of the damping coefficient on the main system and the controller's frequency response curves. Figure 8 shows that as \(\alpha_{1}\) expands, the controller's efficiency in deleting primary resonance excitations somewhat decreases, while the peak amplitudes of the main system and the controller decrease. Figure 9 shows that the damping coefficient \(\alpha_{2}\) increases, and the controller's effectiveness in mitigating super harmonic resonance excitations diminishes slightly, when both the main system and controller peak amplitudes decrease. Figure 10 demonstrates that the impact of the nonlinear parameters leads to an enhancement in the effectiveness of parameter \(\beta\) whereas the vibration reduction frequency decreases. Figure 11 shows that when the effectiveness of parameter \(\mu\) becomes higher, the vibration reduction frequency is decreasing. For small values of natural frequency for \(\sigma_{2}=0\) i.e., (\(\omega_{1}=\omega_{2}\)), the peak amplitudes of both the main system and the PPF controller have shown an increase along with the expansion of the bandwidth for vibration reduction. Consequently, in scenarios where there is a low natural frequency, the PPF controller proves to be highly effective, as depicted in Figure 4:1 We select three distinct values of \(\sigma_{2}\), the amplitude of the main system achieves its minimal value, as seen in Figure 13, when \(\sigma_{1}=\sigma_{2}\), which means that the PPF controller is more efficient in the resonance case.
# Robust Text-to-Cypher Using Combination of BERT, GraphSAGE, and Transformer (CoBGT) Model
###### Abstract  
Graph databases have become essential for managing and analyzing complex data relationships, with Neo4j emerging as a leading player in this domain. Neo4j, a high-performance NoSQL graph database, excels in efficiently handling connected data, offering powerful querying capabilities through its Cypher query language. However, due to Cypher's complexities, making it more accessible for nonexpert users requires translating natural language queries into Cypher. Thus, in this paper, we propose a text-to-Cypher model to effectively translate natural language queries into Cypher. In our proposed model, we combine several methods to enable nonexpert users to interact with graph databases using the English language. Our approach includes three modules: key-value extraction, relation-properties prediction, and Cypher query generation. For key-value extraction and relation-properties prediction, we leverage BERT and GraphSAGE to extract features from natural language. Finally, we use a Transformer model to generate the Cypher query from these features. Additionally, due to the lack of text-to-Cypher datasets, we introduced a new dataset that contains English questions querying information within a graph database, paired with corresponding Cypher query ground truths. This dataset aids future model learning, validation, and comparison on text-to-Cypher task. Through experiments and evaluations, we demonstrate that our model achieves high accuracy and efficiency when comparing with some well-known seq2 seq model such as T5 and GPT2, with an 87.1% exact match score on the dataset.  
text-to-Cypher; natural language processing; GraphSAGE; sematic parsing; graph database; CQL; Cypher
## 4 Specification of Asynchronous and Reliable Broadcast Algorithm  
A finite computation \(E\in\delta\), where \(\delta\) is the set of all possible executions, is called an asynchronous and reliable broadcast algorithm if and only if all the following conditions are **true**:  
[_Reliable Broadcast 1_] Root process \(r\) receives message \(M\) in the computation step \(\gamma_{1}\mapsto\gamma_{2}\)  
Figure 1: Process computational model.  
[_Reliable Broadcast 2_] Let process \(i\) be a process that received message \(M\) directly or indirectly from root \(r\). Any neighbor \(j\) of \(i\) where \(j\) has not received \(M\) in the boundary broadcast is in a state indicating that it has not received a broadcast and remains in that state until receiving the broadcast directly or indirectly from the root.  
[_Reliable Broadcast 3_] For any process \(i\) with spurious message \(M\), \(i\) can propagate \(M\) to neighboring process \(j\); however, \(j\) cannot propagate to any process.  
[_Reliable Broadcast 4_] For every process \(i\in V\), there exists a unique \(x\in[1,k-1]\) such that process \(i\) receives \(M\) in \(\gamma_{x}\mapsto\gamma_{x+1}\).  
[_Reliable Broadcast 5_] In \(\gamma_{k}\), root \(r\) receives an acknowledgment of the receipt of \(M\) from every process \(i\
eq r\).  
Note that the specification of boundary broadcast is given in the message passing model, while the rest of the paper assumes the shared memory model given in the previous section for the sake of brevity. It is easy to see that receipt of a message by a process in the message passing model corresponds to a state change by the process after the same by a neighboring process in the shared memory model.
###### Abstract  
In data-driven methods for prognostics, the remaining useful lifetime (RUL) is predicted based on the health indicator (HI). The HI detects the condition of equipment or components by monitoring sensor data such as vibration signals. To construct the HI, multiple features are extracted from signals using time domain, frequency domain, and time-frequency domain analyses, and which are then fused. However, the process of selecting and fusing features for the HI is very complex and labor-intensive. We propose a novel time-frequency image feature to construct HI and predict the RUL. To convert the one-dimensional vibration signals to a two-dimensional (2-D) image, the continuous wavelet transform (CWT) extracts the time-frequency image features, i.e., the wavelet power spectrum. Then, the obtained image features are fed into a 2-D convolutional neural network (CNN) to construct the HI. The estimated HI from the proposed model is used for the RUL prediction. The accuracy of the RUL prediction is improved by using the image features. The proposed method compresses the complex process including feature extraction, selection, and fusion into a single algorithm by adopting a deep learning approach. The proposed method is validated using a bearing dataset provided by PRONOSTIA. The results demonstrate that the proposed method is superior to related studies using the same dataset.  
continuous wavelet transform; convolutional neural network; bearings; remaining useful lifetime; prognostics and health management; health indicator
# A Systematic Literature Review on Image Captioning
## 4 Discussion
### Model Architecture and Computational Resources  
Most of the models rely on the widespread encoder-decoder framework, which is flexible and effective. Sometimes it is defined as a structure of CNN + RNN. Usually a convolutional neural network (CNN) represents the encoder, and a recurrent neural network (RNN) the decoder. The encoder is the one which \"reads\" an image--given an input image, it extracts a high-level feature representation. The decoder is the one which generates words--given the image representation from the encoder (encoded image), it generates words to represent the image with a full grammatically and stylistically correct sentence.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Year/Method** & **Encoder-Decoder** & **Attention Mechanism** & **Novel Objects** & **Semantics** \\ \hline
**2016** & [11; 12; 13] & [13] & & [12] \\ \hline
**2017** & [15; 17; 19; 22; 23; 25; 26; 27; 28; 29; 31; 32; 33; 34] & [19; 21; 22; 26; 27; 32; 33; 22; 26; 27; 34] & [15; 18; 25; 27] & [27; 28] \\ \hline
**2018** & [35; 37; 39; 40; 41; 42; 44; 46; 48; 50; 52; 54; 56; 57; 59; 58; 54; 56; 57; 59; 56; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68] & [36; 42; 44; 47; 50; 58; 56; 57; 59; 58; 56; 59; 61; 63; 65; 66; 67; 68] & [36; 42; 44; 47; 50; 57; 58; 66; 68] \\ \hline
**2019** & [71; 72; 73; 75; 76; 78; 82; 83; 85; 86; 87] & [71; 74; 75; 77; 78; 80; 83; 85; 86; 87] & [72; 73; 77; 78; 80; 83; 85; 86; 87] & [75; 80; 82; 83; 85; 86; 87] \\ \hline \hline \end{tabular}
\end{table}
Table 10: Methods used by year.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Encoder/Decoder** & **Year** & **LSTM** & **RNN** & **CNN** & **cGRU** & **TPGN** \\ \hline \multirow{4}{*}{**AlexNet**} & 2016 & [11] & & & & \\  & 2017 & [22] & [45] & [21] & & \\  & 2018 & & & & & \\  & 2019 & & & & & \\ \hline \multirow{4}{*}{**VGG Net**} & 2016 & [10; 11; 13] & & & & \\  & 2017 & [14; 15; 16; 19; 20; 25; 30; 31; 32] & [26; 32] & [21] & & \\  & 2018 & [40; 43; 47; 49; 54; 61; 40; 43; 47; 48; 55; 62; 61] & & [51; 52] & \\  & 2019 & [71; 72; 73; 77; 83]
# Pido: Predictive Delay Optimization for Intertidal Wireless Sensor Networks
## 4 Design of Pido
### The Prediction of \(S^{{}^{\prime}}(t+\tau)\)  
Predicting \(S^{{}^{\prime}}(t+\tau)\) accurately is crucial for reducing delay, and a pair of electrodes are embedded on the node to obtain the current node status, which is shown in Figure 1c. When the node is under water, the sea water serves as a conductor and wires two electrodes to makes electrodes conductive. In this way, we can acquire the node status by reading the voltage difference between the two electrodes. However, only the current status (\(\tau=0\)) can be sensed by electrodes and they are not always precise due to sea waves.  
For a \(Node\)\(i\), obtaining its status directly seems to be impossible when \(\tau>0\). Though tidal periodicity can be utilized for roughly predicting node status, the prediction result is not good enough for route selection. To validate our conclusion, we randomly select two nodes (node 8 and node 14) in our Zhoushan system and predict the node status with only tidal periodicity. We compare the prediction status with the real trace we collected in the field test, and results are shown in Figure 6. In Figure 6, the accuracy of predicting node status at the next cycle (\(\tau=1\)) is only around 80%, and the accuracy is even decreasing for further prediction (\(\tau=2,3...\)). Remember that nodes only have two states: above-water and under-water, and thus utilizing tidal periodicity alone is not ideal.  
To fill in the gap, we utilize a classifier with three indicators to predict node future status, and we describe the details in the following.
# Design of Supply Chain System Based on Blockchain Technology
## 4 System Construction
### Smart Contract Design
#### 4.4.2 Information Smart Contract  
The information smart contract is the basis of information exchange in the supply chain. It has two main tasks: to check whether information uploaded by the enterprise and its permissions meet requirements; and to check the legality of the information acquisition request of each node. To ensure the accuracy and availability of data information in the supply chain, the information smart contract must verify the information uploaded by an enterprise, i.e., whether it is consistent with the enterprises' permissions. Algorithm 3 shows the process of information uploading.  
Input: enterprise part a uploads product information message a [];  
Output: If the uploaded information matches the permissions, then the information will be stored in the information alliance chain; if it does not match, then the request will be rejected.  
The enterprise part a calls the information smart contract and sends message a [] to the information smart contract;  
Information smart contract obtains the public key address of enterprise part a, _pkA a_ = msg. sender;  
Retrieve tag a = message a [\(inf_{p}(tar,t)\)];  
If tab a == lab of message a []  
then  
Verify the data format of form a in message a [];  
If form a = = stand [tag a]  
then  
The information smart contract classifies message a by label and stores it in the information chain;  
else Return error message;  
end If  
else return;  
end If  
**Algorithm 3**. Upload information  
After the information is stored in the information alliance chain, other enterprises in the supply chain can obtain it. However, this information is not completely open to all users, as nodes have different permissions. Users needing to obtain information from the information chain platform must send an information acquisition request to the information smart contract, which determines whether the request is legal based on the requestor's permissions and the requested information. Such information acquisition process can be described in Algorithm 4.  
Input: enterprise part b sends an acquisition request application b [];  
Output: If the content of the request matches its permissions, then the information smart contract sends the corresponding information to the requestor's account; if it does not match, then the request is rejected.  
Enterprise part b calls the information smart contract and sends an information query request application b [];  
The information smart contract obtains the public key address of the participant part b, pkA b = msg. sender;  
Purview b = purview [_pkA b_];  
If application b [_conp(tp,ti\({}_{d}\))_] = = purview b  
Smart contract retrieves request content _conp_, checks the availability of information;  
If _conp_ = 0  
then return;  
else  
_conp_ [] \(\rightarrow\) part b, and saves the request record in the information alliance chain;  
else return; end If  
**Algorithm 4** Information acquisition