esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Machine-reading and comprehension (MRC) is a question answering task in which computers are required to understand contexts based on passages and answer related questions. With the rapid evolution of deep neural network techniques, the performance of MRC models has been substantially enhanced [1; 2; 3]. However, conventional MRC models have deficiencies in that text passages relevant to user queries (i.e., text passages containing phrases answering user queries) should be prepared in advance. Figure 1 illustrates an example in which an MRC model returns different answers according to given passages.  
To overcome this problem, open-domain MRC models based on information retrieval (IR) have been proposed [4, 5]. These models conventionally follow a two-stage process: passage retrieval based on an IR model and answer extraction based on an MRC model, as illustrated in Figure 2.  
In several cases, the performance of IR-based MRC models depends on those of underlying IR models that employ term frequency and inversed document frequency (TF-IDF) rankings. As illustrated in Figure 1, when the relevant passage (i.e., the upper document) is given, the MRC model returns the correctanswer \"April 1975\", but when the irrelevant passage (i.e., the lower document) is given, it returns the incorrect answer \"2010\". Although recent IR models have demonstrated superior performances, the highly ranked documents often do not contain answers to the relevant questions. This leads to a decrease in answer recall in open-domain MRC. Therefore, certain models have been proposed for enhancing IR performance [6]. Similar to [PERSON] et al. [6], we propose an artificial neural network (ANN) model that re-ranks retrieved documents to improve answer recall in MRC (i.e., for ensuring that documents containing answers are ranked high). The proposed model complements the underlying IR model by learning degrees of associations (i.e., possibilities for the documents to contain answer phrases) between queries and documents through a deep neural network.  
The remainder of this paper is organized as follows. In Section 2, we briefly review earlier re-ranking models. In Section 3, we describe our model. In Section 4, we explain our experimental setup and report some of our experimental results. In Section 5, we provide the conclusions of our study.  
Figure 1: Different answers of a machine-reading and comprehension (MRC) system according to given passages.  
Figure 2: Two-stage process of open-domain MRC.
# Millimetre-Wave Backhaul for 5G Networks: Challenges and Solutions
## 2 Millimetre-Wave Technology: State-of-the-Art and Trends
Specifically, the choice of routing metrics and hierarchical schemes was discussed for  
\begin{table}
\begin{tabular}{c|l|c|c|c|c} \hline \hline \multirow{2}{*}{**Frequency Band**} & \multicolumn{2}{c|}{**Path Loss**} & \multicolumn{2}{c|}{**Oxygen**} & \multicolumn{2}{c|}{**Rain Attenuation**} & \multicolumn{1}{c}{**Coverage with**} \\ \cline{2-6}  & **100 m** & **200 m** & **Absorption** & **5 mm/h** & **25 mm/h** & \textless{}20  
designing an efficient multi-hop routing protocol in the backhaul system. However, these mm-wave backhaul-related literature works either focus on exploiting one particular technique or techniques for one specific layer or mainly introduce and discuss the challenges and requirements from a global view without proposing possible solutions. Therefore, a system-level design framework is necessary to specify the procedures of mm-wave backhauling, including how to select the transmission path and to determine the transmission sequence, _etc._, in order to fully exploit the benefits of mm-wave techniques and to overcome the associated problems. In addition, jointly considering the new physical layer techniques with upper layer designs is necessary to enhance the overall system performance. Hence, there is still a long way to go to the final systematic implementation.
# Design and Evaluation of an Acoustic Modern
## 4 Autonomous Underwater Vehicle (AUV) Integration Results and Analysis  
### Experiment Setup  
The trial field of the second experiment was Xingan River, Zhejiang province, China. To evaluate the performance of the proposed design in terms of engineering AUV integration, the acoustic modem was integrated onboard a small-sized TH-050B AUV. With a length of 1500 mm and a diameter of 180 mm, the adopted AUV is a typical low-cost commercial AUV with the purpose of being used for extensive marine missions. With the help of it, the effectiveness of the proposed modem onboard AUV under different velocities was evaluated. Shown in Figure 10a is the picture of the AUV, with the picture of the acoustic modem module displayed in Figure 10b. The sound velocity profile of the trial area is displayed in Figure 11c.  
Figure 9: Doppler and bit error rate (BER) curves obtained with software and hardware approaches. (a) Doppler curve (b) BER curve.  
With the receiver at an anchored vessel, the AUV was controlled to transmit while moving at different speeds ranging from 1 to 3 knots under a depth of 3 m. The distance between the AUV and the vessel was about 200 m with a water depth of 25 m. The signal parameters of the modem were the same as those in the first trial.  
Figure 11: The waveform and associated spectrum of the communication signal. (**a**) Noisy waveform of modern signals. (**b**) Normalized power spectral density (PSD) of noise and signals. (**c**) Sound velocity profile of the second trial.  
Figure 10: The autonomous underwater vehicle (AUV) and the acoustic modem module (**a**) The TH-050B AUV (**b**) The acoustic modem module.
## 4 Methodology
### Data Preprocessing
It is safe to assume these spikes are noise, possibly from the power analyser, which can be removed. As such, a moving average filter (MAF) was used to remove this noise in the background. A window length of 15 samples was used for the MAF as this was the maximum window size, which reduced the background noise without negatively affecting the signature of the data. The effect of the MAF can beseen in Figure 5, where the MAF not only reduces the background noise but also effectively cleans the signature of the normal data. Finally, the data were segmented into windows of equal length for input into the network. One second was already determined as the correct window size, so the data were segmented into windows of 2048 samples. As there was a limited amount of training and testing data, a relatively long sliding window overlap per segment of 75% was chosen for the data. This gives the network more opportunity to view specific features from slightly different perspectives, with new information being introduced with each overlapping window. Notably, the success observed in this work using this lower sampling rate proves that one can obtain excellent results with relatively inexpensive equipment. This fact should enable the scalability of such a framework, where expensive equipment is not imperative.
## 1 Introduction
They treat the landslide mass as a coherent body until it reaches a certain distance, at which point the depth-averaged model based on hydrodynamics will take over, and then the mass spreading will become apparent. The erosion and entrainment of bed materials are complex processes depending on the density, strength, and saturation of materials from both the landslide path and the landslide body [34; 35; 36]. There are huge potential needs for quantitative prediction methods accounting for these important processes [37; 38; 39; 27; 36]. To consider the influence of mass integrity on dynamic characteristics, two different dynamic models were effectively integrated in this study. In the early stages, the sliding mass with high integrity was simulated by a multi-block model, which is characterized by low diffusivity. A depth-averaged model will be used for sliding mass with deteriorated integrity and enhanced diffusion due to collisions. In the later stage, the erosion and entrainment effects of substrate materials will be obvious, so both of these effects should be considered in the integrated model [27]. In the following content, we will introduce the multi-block model and the depth-averaged model, respectively. Then, the integration method of the two dynamic models will be introduced. Finally, the validation of the integrated model is carried out by comparing the simulation results with the fields of a real landslide. It is worth pointing out that the executions of all these models are based on the global rectangular coordinate system, which means the data of the digital elevation model (DEM) can be used directly by the integrated model, and the results can also be directly used by GIS-based applications, such as for the spatial prediction of landslide susceptibility [40] and hazard prediction [7; 41]. The effectiveness of the integrated dynamic model in analyzing landslides with significant dynamic characteristic changes will be verified by field data. The differences in dynamic characteristics caused by the differences in fragmentation time will be analyzed. The erosion depth calculated via a simple erosion model will also be verified. All of these analyses provide references for landslide runout analysis under similar engineering conditions.
# Virtual Fire Evacuation Drills through a Web-Based Serious Game
## 5 Results and Discussions
### Drill Effect  
Through the developed serious game, the evacuation time and safety score of each trainee were calculated. As shown in Figure 10, the average trainee evacuation times of three drills were 79.77, 60.17, and 54.32 s. In the second and third drills, the evacuation times decreased by 19.60 s (24.6%) and 25.45 s (31.9%), respectively, compared to that of the first drill. Therefore, the evacuation time decreased significantly with the increase in evacuation training.  
Figure 8: The developed serious game. (**a**) User registration; (**b**) Evacuation results.  
Additionally, the average safety scores of the three-time drills increased, which were 74.71 to 78.69, and 81.21, as shown in Figure 11. Through the second and third drills, the safety scores improved by 5.3% and 8.7%, respectively. This indicates that the evacuation of trainees becomes safer through the drills.  
Generally, the improvement in evacuation time and safety scores demonstrates the effectiveness of the developed serious game.  
Figure 9: Evacuation paths of three drills. (**a**) First drill; (**b**) Second drill; (**c**) Third drill.
## 3 Presentation of the Proposed Model
### Sub-Modules of the Presented Model
#### 3.3.5 ANN Simulation Module  
The ANN structure has been selected based on the method of successive approximation. The different structures (different number of layers and number of neurons in it) were examined and the next structure was determined, based on the results from the simulation. Then results from both simulations were compared to propose another structure. The selection of the network structure was made by assessing the mean square error (MSE).  
For the network training the following parameters were assumed:  
1. Epochs: 1000;
2. Time: Inf;
3. Goal: 0;
4. Min_grad: \(1\times 10^{-5}\);
5. Max_fail: 6;
6. Mu: 0.001;
7. Mu_dec: 0.1;
8. Mu_inc: 10;
9. Mu_max: 10,000,000,000.  
The model finds the optimal structure of the network--self-learning. This self-learning is done by a change of the neuron's weight in the ANN, and it simulates the performance of order-picking. If the quality of the model after changing the structure is better, this structure is chosen. After this, the change in the weight of ANN and the performance analysis is done again. This loop is done until the stop condition is achieved (number of loops). The final structure of the ANN was chosen for the network of high quality--the results from the simulation are similar to the real data. The best results have been obtained by a multilayer unidirectional feedforward type network consisting of three hidden layers. A simplified diagram of the applied neural network is shown in Figure 4. Figure 5 shows an example of a graph of a mean square error that occurred during the process of learning for a selected network structure and for an exemplary structure that was not considered optimal.  
Feedforward networks are characterized by the specific direction of signal flow--it is for the specified input to output on which the network provides an established solution. _Feedforward_ networks are both the most commonly used and most useful. In the first hidden layer, 10 neurons were used, in the second, 20, and in the third, 5. In all hidden layers, the tangential activation function was applied. The input data used were the number of units sold per year, the product's popularity, its weight, and the volume.  
For the selected network structure, a histogram of network learning errors has been generated and shown in Figure 6.  
Figure 4: A structure of the applied ANN, source: own study.  
Figure 5: SNN training results, mean-square error for chosen structure (**on the left**), and rejected structure (**on the right**); source: own elaboration (MatLab R2011b).  
Figure 6: The regression and histogram of network learning errors; source: own elaboration (MatLab R2011b).  
The developed ANN structure gives high-quality results--similar to the target values (Figure 6, on left). It can be noticed that the output values from the training ANN are similar to the target value. This proves that the developed model enables us to achieve very similar results to the real ones.
# Evaluating the Flexibility Benefits of Smart Grid Innovations in Transmission Networks
## 2 Innovation Technology for Improving the Power System Flexibility: The FlexITRANSTORE Project
The demonstrated technologies are presented in Figure 1.  
Figure 1: FLEXITRANSTORE project with different demos able to improve power system flexibility. In order to quantify such benefits from tested technologies and solution, a set of 'priority' KPAs can be defined. When defining these priority KPAs (Key Performance Area), we consider these principles for the whole project:  
* Strongly highlight benefits of demos of FLEXITRANSTORE project: relevance to the activities we implement within the project;
* Compatible and in line with CBA of ENTSO-E and other good practices of CBA calculation: to have common understanding when communication [21];
* Focus on around five essential KPAs: not to have many KPAs so that we lose focus and incur high calculation burden, but not to have few so that we cannot cover the major benefits. Considering all aspects of KPAs analysis in the Section 3 and consultation with different demo leader, as well as TSO/DSO relevant, supporting the European energy targets of renewable energy target, competitiveness and security of supply, we came up with five KPAs:  
* KPA1--Renewable integration;
* KPA2--Congestion reduction;
* KPA3--Flexibility indices improvement;
* KPA4--Improving reliability and stability;
* KPA5--Improved competitiveness of the electricity market. These KPAs can be quantified through specific KPIs. Table 1 presents in detail the connection of each KPA (i.e., the integration of renewables, the reduction of congestion, the improvement of the flexibility indices, the improvement of the reliability and the quality of supply, and the competitiveness of the electricity market) with the proposed KPIs in the context of the project. Table 2 correlates each of the presented KPAs with the relevant technology of the eight demos. Besides the KPIs list and their mapping in the Table 1, which are applicable for many technologies and demonstrations, an extra set of particular KPIs could be formulated to cover specific aspects, for instance:  
* Power flow controller:
* Extension of outage windows for construction and maintenance projects;
* Time required to implement solution;
* Value of scaling the solution size over time to meet emerging needs;
* Value of the PFC assets being highly mobile and ability to be re-deployed to new locations to solve new issues as they emerge;
* Flexibility in approach to capital spending (modular design and redeployability means that capital spending can be projected for much shorter time horizons);
* Storage with conventional GT power plant:
* Primary response--increase of capacity and response time;
* Increase of power plant electricity production;
* EFR--capacity of response;
* Black Start--validation of the service with the storage;
* LVRT voltage support--quantity and BESS sizing;
* ROI (return on investment). In the following Figure 2, an indicative flowchart of the stages to be integrated with the flexibility assessment of alternative technologies is presented.
# BSD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection
## 2 Materials and Methods
### Methodology  
The architecture of the BSMD-YOLOv8 model is illustrated in Figure 4. Our research introduces three significant improvements aimed at enhancing signature mark detection.  
Firstly, we replace the backbone network with LMRNet, which consists of multiple stacked Multi-scale Residual Convolution (MRConv) modules. Initially, the input image passes through a CBS module to fully extract its feature information. Subsequently, these image features are processed through a series of MRConv modules, ultimately generating a total of four feature maps of different scales, denoted as B1 (320 \(\times\) 320), B2 (160 \(\times\) 160), B3 (80 \(\times\) 80), and B4 (40 \(\times\) 40), respectively.  
Secondly, we have made improvements to the PANet structure of the original YOLOv8 network by incorporating low-GD and high-GD modules. The low-GD module is responsible for aggregating and fusing the B1, B2, and B4 features output from the backbone network, while the high-GD module is responsible for aggregating and fusing the B2 and B3 features.  
Lastly, we adopt WIoU as an enhanced bounding box regression metric in place of CIoU. The WIoU loss incorporates a strategic gradient allocation mechanism. It reduces the penalty for geometric metrics when the predicted box aligns well with the target box,  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Number of Images** & **Number of Signature Marks** \\ \hline train & 1917 & 25,338 \\ validation & 274 & 3603 \\ test & 548 & 7137 \\ total & 2739 & 36,078 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Partitioning of data sets.  
Figure 3: Analysis results of the dataset: (**a**) Information regarding the manual annotation process for objects in the dataset; (**b**) Low-quality signature mark examples (The areas circled in red).  
Figure 2: Examples of the dataset: (**a**) before data augmentation; (**b**) after data augmentation.  
thereby mitigating the harmful gradients generated by low-quality signature marks and enabling the model to possess better generalization capabilities.
## 4 Discussion
### Comprehensiveness and Complexity of EEQ Evaluation  
In this study, we employed the PCA method integrating the selected four indicators (_NDVI_, _WET_, _NDBSI_, and _LST_) derived from Landsat images across four temporal snapshots (1995, 2000, 2010, and 2019) to evaluate the EEQ of the study area. The assessment results are almost coincident with the actual conditions and change trend of the eco-environment. Although the physical significance of the selected four indicators can reflect most of the characteristics of the effects of human activities and natural conditions on the eco-environment, these indices are not exhaustive in measuring the EEQ. The factors  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Grade** & **Year** & **CenterX** & **CenterY** & **Orientation** & **Ratio** \\ \hline \multirow{4}{*}{Worse} & 1995 & 443,469.590 & 2,848,187.638 & 29,786 & 1.077 \\  & 2000 & 436,175.418 & 2,831,295.446 & 14.422 & 1.685 \\  & 2010 & 417,453.093 & 2,834,374.896 & 42.459 & 4.003 \\  & 2019 & 437,002.909 & 2,853,162.378 & 169.679 & 1.337 \\ \hline \multirow{4}{*}{Poor} & 1995 & 437,054.698 & 2,838,547.998 & 23.922 & 1.385 \\  & 2000 & 439,447.415 & 2,839,173.906 & 1  
affecting the eco-environment encompass the topography, landscape, soil, climate, and social economy activity, among others. Therefore, it is necessary not only to consider the unique geographical traits of the study area but also to incorporate a broad spectrum of influencing factors.  
For example, the ARSEI was established by integrating salinities and land degradation information to consider the unique characteristics of an arid area [34]. The EQEI was constructed based on 11 selected indicators, including the natural capital index (NCI), social pressure index (SPI), and economic support index (ESI) [35]. Additionally, factors such as slope [36], points of interest (POIs) [37], and land use [38; 39] have been selected in other studies. Human interventions and shifts in natural conditions, including alterations in urban impervious surfaces [40], the construction of road networks [20], land consolidation [41], and events like floods, could cause dynamic changes in the EEQ. Therefore, an evaluation result at a fixed date cannot completely reflect the state of the eco-environment. It is essential to pay more attention to the effect of different time scales on the EEQ, such as various seasons or specific years, and a more comprehensive approach could ensure a more accurate representation of the ecological environment, providing a solid foundation for environmental policy and management decisions.
# Group Authentication Scheme for Neighbourhood Area Networks (NANs) in Smart Grids
## 3 The Smart Grid Network Model
### Authentication of Group Gateway GW  
We now describe the method that is used by NOC to authenticate \(GW_{g}\). Since not every \(GW\) has a direct link to NOC, some \(GW\) will be authenticated via other \(GW_{g}\).  
Figure 3 shows a NAN topology indicating the hierarchical authentication structure/path that is used for \(GW_{g}\) authentication. For completeness, in the figure we also show SMs. The authentication of SMs is discussed in later sections. The group controller of a group g is denoted by \(GW_{g}\). The smart meter n of group g is denoted by \(SM_{ng}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(G\) & Unique group number \\ \(SM_{ng}\) & Smart meter ID \\ \(GW_{g}\) & A gateway for a group of smart meters to the NOC \\ \(MK_{NOC}\) & Master Key for NOC \\ \(MK_{GW}\) & Master key for the group Gateway \\ \(K_{un,vac}\) & Symmetric key generated by \(K_{un,Noc}=F(MK_{NOC}\mid SSM_{ng})\), and shared with NOC, and \(SM_{ng}\). \\ \(K_{GW,SM}\) & Symmetric key generated by \(GW_{g}\), and shared with \(GW_{g}\), and \(SM_{ng}\). \\ \(Proxy\)\(SM_{ng}\) & Existing smart meter for authenticating a new smart meter \\ \(k_{SMSM}\) & Symmetric key shared between \(Proxy\)\(SM_{ng}\), and \(SM_{ng}\) \\ \(K_{CW_{g}}\) & Symmetric key shared with NOC and GW \\ \(AV_{i}\) & Authentication value inside the group where \(AV_{i}=F(R||MK_{GW})\) \\ R & Random number generated by \(GW_{g}\) to produce \(AV_{i}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notations for the authentication scheme for the NAN.  
Figure 3: Authentication for Group Gateway.  
NOC creates a random master key \(K_{NOC}\). This key will be used to generate keys for each child \(GW_{g}\) (\(GW_{1}\) and \(GW_{2}\), _etc._):  
\[K_{GW_{g}}=\mathcal{F}\left(K_{NOC}||GW_{g}\right) \tag{2}\]  
where \(\mathcal{F}\left(\ \right)\) is a secure one-way hash function and \(\ |\ |\ \) is the concatenation operator. The key \(K_{GW_{g}}\) is stored at the corresponding \(GW_{g}\). The NOC does not need to store it, since it can be generated from \(K_{NOC}\). In a similar way, each child node \(GW_{g}\) produces shared keys for its own child nodes. For example, if \(GW_{1}\) has several child nodes as group gateways, \(GW_{1}\) uses its master key \(K_{GW_{1}}\) to generate a key for each of its child nodes, \(GW_{g^{\prime}}\):  
\[K_{GW_{g^{\prime}}}=\mathcal{F}\left(K_{GW_{1}}||GW_{g^{\prime}}\right) \tag{3}\]  
The keys generated are stored at the corresponding child nodes. Similarly, each of these nodes will generate keys for its child nodes and so on, until all the leaf nodes with no children have been reached.