esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Dialogue Management System for Banking Services
## 4 Methods  
Modular and End-to-End modeling are two main approaches in conversational chat-bot development [23]. Whereas Modular systems consist of two separate modules, user intention understanding and dialog management, End-to-End systems learn dialog patterns directly from interactive human-to-human conversations. In this research, we focused on a Module-based conversational agent, which consists of three main modules (Figure 3):  
* _NLU Module._ The goal of the NLU module is to process user messages, understand intent, and extract named entities. Intent identification means defining the main purpose of the user in a message. Named entity extraction is used to get important entities from text, such as person names, organizations, geological places, dates, time, etc.
* _DM Module._ Dialogue management is a machine learning based system that predicts the next bot action or response according to the previous dialogue turn, i.e., user-bot interactions, extracted from intent and entities.
* _Natural Language Generation (NLG) Module._ Currently, the NLG module of the bot consists of preformed sentences, which can be used as bot responses.  
Additionally, the chat-bot has an Integration Module to connect to the other environments. The integration module plays a main role in executing the operational part of the chat-bot, such as checking the user's account and integrating with internal and external services.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Intent** & **Number of Samples** \\ \hline
1 & card\_request & 1589 \\
2 & credit\_info & 1215 \\
3 & unblock & 434 \\
4 & limit\_increase & 328 \\
5 & work\_time & 312 \. &. &. \. &. &. \. &. &. \\
59 & other & 21 \\
60 & chitchat/ask\_weather & 17 \\
61 & stop\_credit\_application & 16 \\
62 & ubank\_operations & 15 \\
63 & chitchat/url\_not\_working & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Intent distribution.  
Figure 2: Distribution of all intents throughout the dataset.  
The NLU component itself consists of several steps, such as text representation (tokenization and feature extraction), language identification, and user intent detection. All these steps can be seen in Figure 4.
# Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods
## 2 Materials and Methods
### Method  
Different methods have always been used for internal location without the use of satellite signals or location via the internet. In this study, research is based on the environment and real tools. One of the most efficient location methods is the fingerprint method, which is widely used in commercial applications [7; 8; 21]. In fact, the main purpose and direction of this research is that first the space of a real environment is mapped on a coordinate axis. The distance between the points of these coordinate axes in the real environment is considered to be 40 cm, according to which the X coordinate axis contains 18 points and the Y coordinate axis contains 30 points. After determining the space by the optimal CiP method, the connection of the transmitters is conducted in the form of an equilateral triangle. After preparing the test space, in the offline stage, with a sampling application in 47 points, the operation of recording and sending training data to the server is performed. The reason for obtaining 47 points is that in the space, a corridor and three passable rooms have been considered for research; if paths are drawn according to the coordinate axis, 47 navigable points will be obtained. After collecting the training data in another phase, the location operation is performed in 4 different ways with another application. In this phase, the complete map of the environment is viewed on the application and by moving in the environment and sending the target point to the server; the server is positioned in 4 ways in exchange for the received signals and the error of the methods is simultaneously registered in another database. Finally, after obtaining the most optimal method from the results of error checking, the best method can be selected as the locating method in the application so that the user can see their location by moving in the environment instantly.  
To do this, four main phases are first defined as follows:  
1. The first phase: offline section, to collect training data from the environment;
2. The second phase: online section, implementation of locating algorithms;
3. Third phase: online section, combination of second phase algorithms and debugging;
4. Fourth phase: calculation of measurement error.
## 2 Materials and Methods  
The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards were followed in order to carry out this more specific investigation [38]. In order to reduce the likelihood of any potential reporting bias, the protocol for the study was submitted to INPLASY, an international platform of registered systematic review and meta-analysis protocols, and given the registration number INPLASY202230002. In order to reduce the likelihood of important data being overlooked, the search method was carried out in four different databases, namely PubMed, MEDLINE, EMBASE, and CINAHL.  
The search was conducted to locate multi-language papers published between January 2003 and November 2022.  
The study was conducted using a three-stage screening process to assure accuracy and protect the quality of the findings. Five writers ([PERSON], SO, OA, NS, and VM) independently evaluated the titles and abstracts to remove any unnecessary materials (i.e., reviews, animal studies, and non-clinical studies). Disputes were settled by conversation until a consensus was formed.  
The following data screening strategy was used to extract the papers:  
* Inclusion and exclusion criteria were used to check the eligibility of the previous studies.
* Quality assessment was performed on the methodology.
* Data were extracted based on characteristics and outcomes for the selected papers. (Figure 1).  
Direct communication was established with the authors of any studies that were potentially suitable for inclusion in the review but had inadequate information so that the authors might supply more data. The PICO method served as the foundation for the criteria for inclusion in the study [39].  
Figure 1: Prisma study flow diagram.  
_PICOS Research Question_  
What is the present state of knowledge about the pharmacogenomic and genetic aspects of MRONJ?  
**Population--(P)**: Patients with MRONJ having any age  
**Interventions--(I)**: Any sort of intervention  
**Comparison--(C)**: Any sort of comparison  
**Outcomes--(O)**: The quality level of pharmacogenomics and genetic studies associated with the MRONJ  
**Study--S:** Systematic Review and/or Meta-Analysis  
The search criteria for all the selected databases were followed by searching the MeSH terms and keywords of Osteonecrosis, MRONJ, ONJ, ARONJ, BRONJ, and BONJ. Other MeSH terms used were Pharmacogenetics, Genetic predisposition of MRONJ, Pharmacogenomics and MRONJ or Genetic and MRONJ. The search method made use of appropriate syntax constraints outlined in every repository.
# Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network
## 3 Deep Learning-Based Target Detection
### Basic YOLOv5 Network Overview  
YOLOv5 is a single-stage target detection network proposed by Ultralytics LLC. As the most mature and stable target detection network in the YOLO series at present, it is the product of improvements based on YOLOv4 and YOLOv3 [18; 19; 20]. After learning the advantages of the previous versions and other networks, YOLOv5 changes the previous YOLO target detection algorithm's characteristics, i.e., faster detection but not high accuracy. This network improves detection accuracy and real-time performance, which not only meets the need for real-time image detection but also has a smaller structure. Therefore, in this paper, we use the standard YOLOv5 as the target detection model, and the network structure is shown in Figure 5[20], whose network model is divided into four parts: input, backbone, neck, and prediction head.  
Firstly, the collated dataset images are input to the network after preprocessing operations such as data enhancement and scaling. Then, the focus module downsampling, CSP structure, and SPP pooling layer are used to extract the image features and compose the main part. The focus module can segment the input image into four parts to piece together a 12-dimensional image with features and obtain 3 \(\times\) 3 feature information from it to generate a 32-dimensional image that contains feature information. The focus module downsampling has the advantage of preserving image information and reducing computation, and its use can improve the overall training speed. The structure of CBL consists of a convolution (Conv) layer and a batch normalization (BN) layer, while the signal is passed to the activation function layer. The Conv module in SPP has the advantage of achieving a reduced number of input feature maps. By using the Conv module, the maximum set of subsamples can be covered in the case where there are three different convolution kernels, and the results are stitched to the input feature maps by channel. YOLOv5 has the advantage of implementing SPP by using convolution kernels of the same step size. Therefore, although the feature maps obtained by this network are of the same size, the regional sensitivities vary, and the features are also fused. Thirdly, the neck part  
Figure 5: YOLOv5 network structure diagram.  
uses the structure of a Path Aggregation Network (PANet) combined with Feature Pyramid Networks (FPN) as the feature aggregation layer of the whole network architecture, and the function of this part is to obtain the multi-scale information of the image. Finally, we use three loss functions to form the prediction head part as a way to calculate feature classification, feature localization, and confidence loss and to make accurate network predictions by non-maximal rejection (NMS). The output of the algorithm consists of a vector of prediction frame categories, confidence levels, and coordinate positions. This network structure is used as a basis for improving the characteristics of the signal blind detection task in this paper.
## 2 Deformational Pressure Model According to Polish Standards
The actual value of the k coefficient depends on numerous factors, including the rock mass and support properties, as well as the distance between the location of the support installation and the excavation face [8; 9; 12; 19]. This topic has been extensively discussed in [20]. The dimensioning of the so-called vaulted lining (constructed of brick or a thick layer of unreinforced or reinforced concrete) according to the PN-G-05020 Standard should be carried out while considering the following load combinations [17]:  
* simultaneous loading of the lining's self-weight and the static pressure of the rock mass,
* simultaneous loading of the lining's self-weight and the deformational pressure of the rock mass,
* simultaneous load of the lining's self-weight and the injection pressure. Since the PN-G-05020 standard does not provide suitable formulas, the influence of deformational pressure on the vaulted lining is sometimes overlooked by engineers [21]. This practice may be justified due to the utilization of a yieldable primary lining, which is capable of accommodating the entirety of the initial displacement of the rock mass (associated with the advancement of the excavation face). According to the recommendation outlined in the Polish Standard, secondary support can only be constructed once an equilibrium state is reached between the primary layer and the rock mass [17]. Therefore, if a primary lining with appropriate deformability is installed beforehand, the deformational pressure acting on the secondary lining should be eliminated or significantly reduced. In summary, according to Polish National Standards, the primary lining of an underground roadway or chamber must always be designed as a yieldable structure in order to eliminate the occurrence of deformational pressure. This assumption accounts for a significant weakness of the discussed solution. Determining the value of the deformational pressure acting on the support of limited deformability (for which condition (7) is not met) is not possible using the equations provided by Polish Standards only. Additionally, it must be noted that in engineering practice, the geotechnical properties of rock masses are never constant, as they may be reduced due to mining activities, rock delamination, water inflow, and contact with the mine's atmosphere [10]. Therefore, in many cases, the use of shell support with limited deformability, which allows for the rapid suppression of rock mass displacement, may be highly desirable. This solution is widely employed in the worldwide tunneling industry, constituting one of the basic principles of the new Austrian tunneling method [22].
# An Efficient Retrieval Technique for Trademarks Based on the Fuzzy Inference System
###### Abstract
In the literature [2; 26; 27; 28; 29; 30], fuzzy theory is employed to deal with the vagueness and ambiguity of human judgment in terms of image similarity. It provides flexible and fuzzy mapping from low-level features to high-level human concepts. [PERSON] et al. [26] proposed a novel fuzzy similarity measure based on the generalization of the Hamming distance (FHD) for estimating the color histograms of two images. [PERSON] et al. [27] proposed an unsupervised fuzzy clustering algorithm to automatically classify database images into perceptually meaningful clusters. [PERSON] et al. [29] presented a fuzzy decision support system (FDSS) with three input features, three normalized distance vectors of shape, texture, and color, respectively, to measure the degrees of similarity between a query image and all reference images. This study is motivated by developing an efficient TIR technique in real time application, which can rapidly extract visually similar trademarks from a large database. To improve computational efficiency and retrieval accuracy during the retrieval process, the simple and robust integration of global and local descriptors is employed to describe multiple objects in an image. In addition, the local descriptor utilizes a tree representation to organize multiple objects, and the five local geometric features of each object are assigned to the tree node. However, the heavy computational burden of isomorphism between two trees is an important issue. Therefore, we proposed a novel algorithm of tree similarity measure based on the fuzzy inference system (FIS). In this algorithm, the weighting of subtrees with an optimal assignment method significantly enhances the computational performance. Finally, we use a weighting strategy to integrate the similarity of global and local descriptors for the similarity coefficient between the query image and the database image. The remainder of this paper is organized as follows. Section 2 outlines an overview of the proposed TIR technique. Section 3 describes the processes of image representation: tree structure and feature extraction. Section 4 presents the similarity measurement. Section 5 contains the experimental setup and discusses the experimental result of the proposed technique. Finally, the conclusions of this study are given in Section 6.
## 3 Hybrid FEM/SEA Model and Interstage Response Analysis
Both parameters depend on the modal density, representing the number of modes per hertz associated to the subsystem, while the MOF is also dependent on the loss factor; together they provide the conditions for which a \"diffuse-kind\" vibration or acoustic field characterizes the SEA subsystem's dynamic behavior. Hybrid FEM/SEA modeling, then, requires an iterative process aiming for the exact identification of the frequency range accuracy for each subsystem in order to correctly define their geometries and dimensions. For accurate predictions in a large frequency band, more than one model might be necessary, where the number of subsystems modeled according to one methodology or the other could consequently change. The validation process of the SEA and hybrid structural models is carried out by relying on the following:  
* Theoretical assessments of SEA subsystems against simplified formulations to verify the consistency with some global distinguishing parameters. * Numerical checks on sections of the interstages performed by comparison of the prediction data provided by the hybrid model and by the equivalent FEM model. * Numerical mutual checks between the full interstage hybrid and FEM models performed in the overlap frequency region where the two approaches are reasonably applicable. The acoustic loading applied is considered a diffuse field, acting on the external surface of interstages; the power spectral density (PSD) of the pressure autocorrelation function is assumed to be constant over the entire surface. The responses of all the interstages are expected to be characterized by a more or less uniform modal energy, which is beneficial to reduce the effect of nonideal modeling, in terms of SEA accuracy, of some of the model components, and in order to reduce the effect of not simulating the entire launch vehicle. In fact, modal energy equivalence can also be interpreted as an opportunity to perform a first approximation analysis on each component or subsystem by assuming it as being disjointed from the others. This assumption is strictly applicable to models made of SEA subsystems only and for which SEA criteria are fulfilled; thus, the application to hybrid models must be carefully verified, because of the \"non-diffuse-field\" characteristics of FEM subsystems.
# Anomaly Detection in Network Traffic via Cross-Domain Federated Graph Representation Learning
## 5 Experiments and Results
### Experimental Set-Up
#### 5.1.2 Baseline  
In order to verify the effectiveness of the methods in this paper, AD-FG was compared with eight methods, and the specific results are shown in Table 3. Among them, eight baselines were included:  
1. Local: Each client trained local models based on local data and did not communicate with other clients.
2. FedProx [32]: Based on FedAvg, a regular term was introduced into the original loss function to alleviate the problem of non-independent homogeneous distribution among clients.
3. FedAvg [5]: The clients sent all learnable parameters to the server and received aggregated parameters from the server for the next round of training.
4. FedSage [33]: The extraction of graph information was achieved using the two steps of aggregation and splicing combined with the GraphSAGE sampling algorithm.
5. GCFL [34]: A federated learning approach based on graph clustering, with federated searching for clients where graphs with similar structures and features were located and training graph models between clients within the same cluster using FedAvg.
6. MemAE-EIF [35]: Unsupervised detection methods based on deep autoencoders and Isolation Forest.
7. CTGCN [36]: Modeling network devices and communications as graphs to capture changes in communications over time and detect anomalous traffic patterns by aggregating neighbor information through convolutional operations.
8. DyGCN [37]: A GCN was used to capture the graph structure features of each time period to mine the abnormal substructure in the graph and identify network anomalies.
### Wood Drying and Heat Treatments  
The work of [PERSON] et al. (Paper 9) approaches the drying of 50 mm thick Turkey oak (_Quercus cerris_ L.) lumber. Both air drying and conventional kiln drying were applied in order to establish the optimum drying conditions for this particular wood species, which is denser than common oak (_Quercus robur_ L.), and highly prone to cracks and deformations during the drying process. The influence of steaming prior to drying was also taken into consideration. Some recommendations for an optimised drying schedule were formulated.  
[PERSON] et al. (Paper 10) compared the drying behaviour of western hemlock (_Tsuga heteophylella_ (Raf.) [PERSON]) with that of Norway spruce (_Picea abies_ ([PERSON]) [PERSON]) and Scots pine (_Pinus sylvestris_ L.) from the green to the oven-dried condition. A CT scanner was used instead of resistance-type pin electrodes in order to assess the moisture content evolution within the specimen, by calculating moisture content based on CT images. Consequently, the reason why hemlock dries much more slowly than the other two species could be identified, this being the presence of large wet pockets. An adapted drying schedule was developed for timber prone to the formation of such wet pockets.  
[PERSON] et al. (Paper 11) investigated the thermal treatment of Teak (_Tectona grandis_ L.f.) and the effects of this treatment on the colour and chemical composition of this valuable species. The heat treatment was carried out in two variants, in an autoclave (closed system) at 160 \({}^{\circ}\)C and in an electric oven (open system) at 185 \({}^{\circ}\)C and at 210 \({}^{\circ}\)C. The closed system demonstrated higher efficiency even though it operated with a lower temperature. This resulted in a higher mass loss, a stronger reduction of the hemicelluloses content and an increase of the extractive content for all solvents used.
## 5 Experimental Results
### Channel Hole Bowing  
Figure 12 shows the experimental results for detecting channel hole Bowing defects. In the Soft Bowing case, it does not cause a chip failure but only affects the error characteristics in specific WL regions. As shown in Figure 12a, WL regions where Bowing occurs exhibit higher raw bit error rates (RBERs) compared to other WLs. A large and distorted channel hole shape due to Bowing makes flash cells in WLs more vulnerable to noise or stress, which gets more severe as P/E cycles increase. Therefore, managing error-prone WLs due to Bowing is one of the key challenges in developing 3D NAND flash memory. As explained in Section 4.2, Bowing defects can be detected by monitoring the state of the status pin after programming all WLs in the target flash blocks. Figure 12b shows the results of the change in bad block counting as the program voltage (\(V_{PROG}\)) is raised. Similar to the case of the Not-Open defect, even though \(V_{PROG}\) is raised from 20 V to 24 V, there is no significant change in detection accuracy for the Bowing defect. Figure 12c shows the effect of pre-condition P/E cycles to eliminate infant mortality originating from Soft  
Figure 11: Experimental results for channel hole Not-Open defects. (**a**) Detection accuracy vs. erase voltage. (**b**) Detection accuracy vs. erase cycles. (**c**) Infant mortality in a flash chip.  
Bowing defect. (Conceptually, a program-only cycle can be a good solution to apply stress. However, due to the erase-before-program property of NAND flash memory, the P/E cycle was selected as a practical solution.) From the results, we observed that by applying just 50 pre-condition P/E cycles, Hard and Soft Bowing defects could be efficiently screened out, so we can secure the quality of a flash product.