esa-sceva/satcom-chunk-collection huggingface dataset:
# Underwater 2D Image Acquisition Using Sequential Striping Illumination
## 5 Conclusions  
This paper presents a new approach to underwater image recovery using sequential striping illumination, which can tackle both forward scattering and back scattering problems. The imaging process is modeled on the implementation of a convolution integration, and image acquisition is issued as image recovery by deconvolution. The method has the advantages of integrating forward scattered light into a recovered image so as to eliminate image blur. Analogous to the mechanism of the synchronous scanning system, back scattered light is eliminated by a virtual \"aperture\", which can be realized by window masking each frame image. Although removal of the back scattered light was inaccurate, it performed effectively in underwater experiments. Results of image recovery verified the effectiveness of the proposed method: Visibility of the recovered object image is significantly better than that of the floodlit image in turbid water medium, although a colored version of image recovery is also available.  
In the proposed approach, image acquisition does not require 3D calibration of the system. The 3D reconstruction of the scene has not been considered in this paper. Obviously, 2D image recovery facilitates the modality of 3D reconstruction; image recovery enables the separation of the striping illumination pattern from the surface albedo, yielding to more reliable detection of the center of the projected stripe.  
Finally, it should be noted that the width of the lighting stripe affects the image recovery by deconvolution. A broadly distributed illumination function \(I(x)\) inevitably conducts the degradation of image deconvolution by using the model of Equation (5), which is a common problem mathematically. It is expected to specify a more sophisticated lighting pattern to improve the robustness of image recovery, which will be considered in future work.  
Conceptualization, G.W.; methodology, B.G. and G.W.; software, B.G.; validation, B.G; formal analysis, B.G. and G.W.; investigation, B.G.; resources, G.W.; data curation, B.G.; writing--original draft preparation, G.W.; writing--review and editing, B.G. and G.W.; visualization, B.G.; supervision, G.W.; project administration, G.W.; funding acquisition, G.W. This research was funded by the National Natural Science Foundation of China, grant number 61571407.  
This paper has undergone English language editing by MDPI. The text has been checked for correct use of grammar and common technical terms, and edited to a level suitable for reporting research in a scholarly journal.  
We declare no conflict of interest.
## 2 Related Work  
### Traditional Session Recommendation Methods  
Traditional recommendation algorithms in machine learning, such as methods based on matrix factorization [23] (MF) and collaborative filtering [24] (CF), have been widely used in many recommendation systems. However, for the task of session recommendation, traditional matrix decomposition methods are not very suitable. Session recommendation aims to recommend appropriate information or content for a user based on his/her behavior during a specific session. Traditional matrix decomposition methods are usually based on the interaction behaviors between users and items, whereas, in session recommendation scenarios, user behaviors are often transient and fragmented, and a complete interaction matrix cannot be constructed. Collaborative filtering algorithms make recommendations based on the similarity between users or items, which are divided into two categories: user-based collaborative filtering and item-based collaborative filtering. The former recommends by calculating the similarity between users, while the latter recommends by calculating the similarity between items. These methods have been widely used in the recommendation systems of Netflix, Amazon, YouTube, et al. [25]. However, collaborative filtering algorithms have some limitations in calculating the similarity between users, due to the lack of information about users for conversational recommendations. In addition, collaborative filtering algorithms often fail to fully utilize item feature information, and item-based collaborative filtering only calculates similarity using the number of co-occurrences of items in a session, ignoring the sequential relationships between items. Before deep learning became widely used, Markov chain (MC)-based methods were the mainstream session recommendation algorithms. Markov chain is a stochastic process that assumes that the current state is only related to the previous state. A Markov-chain-based approach predicts the user's subsequent behavior by building a state transfer matrix of the user's behavioral sequence, and thus recommends items with the highest probability to the user. [PERSON] et al. (2016) combined a matrix-decomposition-based approach and a Markov-chain-based approach, and proposed the FPMC (factorizing personalized Markov chain) model, which achieved good results. [PERSON] et al. (2017) developed a hidden Markov model that combines users' dynamic preference tendencies and contextual bias transition information to improve the performance of recommendations. However, the Markov-chain-based approach has some problems. First, it is based on an independence assumption ([PERSON] et al., 2016), i.e., that the user's subsequent behavior is only related to the current state and not to previous behavior. This assumption is not fully valid in practical situations, because users' behaviors usually have some contextual relevance. Second, Markov-chain-based methods tend to ignore the global information of the session sequence and focus only on the items clicked at the end of the session, resulting in poor recommendation results in long sequences.
# Agreement Technologies for Coordination in Smart Cities
## 3 Applications
### Emergency Medical Service Coordination
Even though most EMA services employ different types of ambulances, for reasons of simplicity, we just consider a single type. Each ambulance has a position and an operational state which vary during time. \(p(a_{i})\) and \(s(a_{i})\) denote the current position and the current state of ambulance \(a_{i}\), respectively. The position refers to a geographical location, and the state can be one of the following:  
* _assigned:_ An ambulance that has been assigned to a patient and is moving to the patient's location. * _occupied:_ An ambulance that is occupied, either attending a patient \"in situ\" or transferring him/her to a hospital. * _idle:_ An ambulance that has no mission in this moment. We denote the sets of available, occupied, and idle ambulances at a given moment by \(A^{A}\), \(A^{O}\), and \(A^{I}\). Regarding the patients, \(P=\{p_{1},\,\ldots,\,p_{m}\}\) denotes the current set of unattended patients in a given moment (e.g., patients that are waiting for an ambulance), where \(m\) is the cardinality of \(P\). Each patient \(p_{j}\in P\) has a location (denoted by \(p(p_{j})\)). We assume that patients do not move while they are waiting for an ambulance, thus \(p(p_{j})\) is constant. Furthermore, once an ambulance has reached a patient's location in order to provide assistance, this patient is removed from \(P\).
# A Video-Based Fire Detection Using Deep Learning Models
## 4 Experiments and Results
### Training LSTM and Its Performance  
The LSTM in the proposed method is trained with video clips. We collected 1,309 video clips from Youtube, comprising 672 clips of fire, and 637 of non-fire. As in the Faster R-CNN, the video clips of non-fire objects can include hard negative examples like clouds, chimney smoke, lighting lamp, and steam, or simply objects that are irrelevant to fire. Figure 10 shows samples of flame, smoke, and non-fire objects from the LSTM training video dataset.  
Here, we do not discriminate between flame and smoke as mentioned before. The video clips are divided into 60 consecutive frames with 15 frames of overlap that last for about 2 s if 30 frames per second are assumed. This implies the LSTM network captures short-term dynamic behaviors of a fire or non-fire and decides whether it is a fire or not for every 1.5 s. Here we assume a person's quick glance for fire decision happens every 1.5 s. The time duration can be adjusted according to the situation when our method is implemented.  
For the LSTM training, we prepared 8,527 positive and 7,547 negative examples of 60 frame video clips from Youtube videos. From the examples, 75% of the data were selected for training, 15% for validation, and 10% for testing. From each video clip, we obtained bounding boxes and their corresponding 1024-dimensional feature for every consecutive frame, which gave a set of sequential inputs to LSTM. Table 3 shows the parameters of LSTM training and the performance of the test data shown in Table 4, according to the number of memory cells in LSTM. To compare the performance with another method, we evaluated the results using the dataset in reference [11]. Information of the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Method** \\ \hline Iteration & 150,000 \\ Step size & 100,000 \\ Weight decay & 0.00004 \\ Learning rate & 0.01 \\ Learning rate decay & 0.00001(iteration equal step size) \\ Batch size & 256 \\ Pre-train weight & ResNet-101 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training parameters of Faster R-CNN.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**mAP** & **Flame** & **Smoke** & **Non-fire** \\ \hline
88.3\% & 89.4\% & 87.5\% & 88.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean Average Precision (mAP) of Faster R-CNN.  
Figure 10: Sample still shots including flame, smoke, and non-fire objects taken from video clips for LSTM training. The (**a**) images are taken from videos of fire, while the (**b**) images are from non-fire video clips.
## 3 Results
### Repeatability of the Maximum Range of Motion  
All recorded data was visually inspected after joint angle calculation. Abnormal results were excluded for further data analysis in case of clearly identifiable sources of error. These were mostly missing or occluded markers crucial for joint angle calculations. A misplaced thumb marker was identified in two different cases, once by reassessing the photograph of the marker set and in another case by a marker shift becoming identifiable during data processing.  
The mean difference of the analyzed joint angles ranged from \(-\)3\({}^{\circ}\) to 2.7\({}^{\circ}\) when comparing testing days (positive: higher values in the first measurement; negative: higher values in the second measurement). The p-values of the paired t test ranged from 0.53 to 0.997, reflecting that there was no statistically significant difference (\(p<0.05\)) between test and retest in all joints.  
The estimated measurement error exceeded the limit of 5\({}^{\circ}\) for the flexion-extension ROM of the MCP5 and all thumb joints as well as for the maximum extension angle of the MCP1 and IP (Figure 5).  
Figure 4: SEM for the ROM in flexion of the finger joints over all subjects for FM1 and FM2. The number of analyzed subjects was different between the two studies (FM1: \(n=11\); FM2: \(n=20\)).
## 4 Experimental Results
### Inference Results  
As described earlier, the results of inference in the actual on-board environment using the.pt file obtained from the training results and the.hwh,.bit, and.tcl files obtained from the hardware design are as follows. Figure 8 shows the results of inference using PYNQ-Z2, organized by execution time. The experiment was run 10 times for each method, and all results showed that the optimal routing path was found accurately. First, PYNQ-Z2 used only PS for inference, and the average execution time was 0.9396 s. Next, when PS and PL were co-designed, but the naive code was executed on PL without any parallelization process, the execution time was 1.4012 s on average. Finally, when the PS and PL were co-designed, and the 2D convolution operation was parallelized by applying the proposed method, the average execution time was 0.2991 s. When checking the experimental results, it can be seen that the naive method of using the same computation structure in the PL as in the PS without parallelization actually increased the execution time by about 1.51 times compared to the PS only. However, when the parallel 2D convolution operation was used following the proposed method, it was accelerated by about 3.10 times compared to the PS only and by about 4.68 times compared to the naive method. This shows that the proposed method improves real-time performance while maintaining accurate results.  
Finally, Table 2 summarizes the resources of the boards used to design the naive and proposed methods. Both the proposed method and naive code required PS and PL to perform the work. The ZYNQ XC7z020-1 CLG400C has a maximum hardware resource of LUT 53200, LUTRAM 17400, FF 106400, DSP 220, and BRAM 140. Table 2 shows that the proposed method utilized 25.32% of the total resources for LUTs, 4.76% for LUTRAM, 15.51% for FF, 25.36% for BRAM, and 9.09% for DSP, with a slight increase in the usage of the LUTs and BRAM compared to the naive code. However, since the proposed method significantly reduced the inference time, the results of almost the same or slight increase in resource usage compared to the naive code indicate that the proposed method is an efficient method.  
Figure 8: Comparing the inference time of several methods, including the proposed method. (The longer inference time can cause problems with the real-time performance of satellite routing algorithms.).
## 2 Metadata Systems for Name Disambiguation
### Virtual International Authority File (VIAF) [[http://viaf.org/](http://viaf.org/)]([http://viaf.org/](http://viaf.org/))  
According to the project's web page, \"VIAF is a joint project of several national libraries, implemented and hosted by OCLC Online Computer Library Center, Inc. ([[http://www.oclc.org/](http://www.oclc.org/)]([http://www.oclc.org/](http://www.oclc.org/))). The project's goal is to lower the cost and increase the utility of library authority files by matching and linking the authority files of national libraries, and then making that information available on the Web.\" The database aggregates name authority records from over 15 participating organizations. Because name headings differ in different databases, the VIAF aggregates the different forms for each heading. So for a single person, there may be three or four name headings listed together. Indeed, voluminous and classical authors may have a dozen or more forms of their names in the database. Country flag icons follow each heading to indicate the source of the heading. Several countries frequently share a single form of the name. The database has numerous add-ons, including cover art that correspond to books published by authors listed in the database, publication statistics, and it makes the name authority records available in both MARC and UNIMARC formats. A \"history\" tab records all changes to each individual record.  
One issue with aggregating name metadata databases such as this one is the accurate grouping of name headings that correspond to the same individual. In this case the work is done algorithmically, and the algorithms are rather conservative. That is, the database will only group different headings together when it is sure that the two forms indeed represent the same person, using co-author information and birth and death date information [13]. VIAF also uses bibliographic information to group two or more forms of a name into a single author. This grouping is done when two sources cite an author for the same title.
## 1 Introduction
this demonstration, the field of nanomaterial SAs gained traction as 1D carbon nanotubes [13] (CNTs) and 2D graphene [14; 15] emerged as promising materials exhibiting intensity-dependent absorption and sub-picosecond relaxation times [16]. Graphene, a single atomic layer of carbon atoms that can be exfoliated from graphite, attracted particular interest, as its 2D structure and zero bandgap enable wideband optical operation [10]. However, graphene is only one of a family of 2D materials that can be extracted as monolayer and few-layer crystals from a variety of bulk materials, including topological insulators (TIs), transition metal dichalcogenides (TMDs) and black phosphorous (BP). All of these materials offer distinct, yet complementary properties [10; 11; 17; 18] and, hence, new opportunities for optical applications in fibre-based systems. The possibility of combining layers of 2D materials to form van der Waals heterostructures also offers an exciting prospect for a wide range of new engineerable photonic devices [19], as does the potential to vary nanomaterial properties through their growth conditions, doping and electronic control [20; 21]. In this review, we summarize recent progress in 2D SAs, with an emphasis on few-layer transition metal dichalcogenides, a sub-class of 2D materials currently receiving particular interest. We discuss their nonlinear optical properties, which can be tuned through the fabrication procedure, and highlight recent demonstrations of Q-switched and mode-locked fibre lasers based on the technology. Finally, we conclude with an outlook exploring the opportunities and avenues for future work in this vibrant field.
# HDLNIDS: Hybrid Deep-Learning-Based Network Intrusion Detection System
## 6 Performance Analysis
### Evaluation Metrics  
A confusion matrix (CM) assists in determining the actual and expected classification. The classification result is divided into two categories: normal and abnormal. Four crucial states in the confusion matrix must be measured.  
* True Positive (TP): this implies demonstrating that the model is accurate and representative and that it predicts favorable results.
* False negative (FN): this is defined as an inaccurate prediction. It accurately classifies malicious situations as normal, whereas it predicts bad outcomes wrongly.
* False positive (FP): when the number of attacks seen is typical, the model predicts a favorable result.
* True negative (TN): this refers to events that are appropriately identified as an assault and forecasts unfavorable outcomes.  
The following performance matrices were used to evaluate the performance of the proposed system in terms of accuracy, precision, recall, and F1 score as mentioned below in Equations (1)-(4), respectively:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{Total Samples}} \tag{1}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
\[\text{F1 Score}=\frac{\text{T2}\times\text{Precision}\times\text{Recall}}{ \text{Precision}+\text{Recall}} \tag{4}\]
## 4 Design and Electrical Performance Characterization
### Configuration Set-Up, and Identification of Materials and Their Electrical Performance
#### 4.2.2 Identification of Materials and Electrical Performance  
The materials employed during characterization and design include the following:  
* A ceramic cylindrical dielectric resonator [30] with a dielectric constant \(\epsilon_{r}=24\), loss tangent tan \(\delta=10^{-4}\), initial aspect ratio AR = 0.5, and radius constraint \(r_{DR}=3\) mm [31].
* A Rogers RO6002 substrate (relative permittivity \(\epsilon_{r}=2.94\pm 0.04\), tan \(\delta=0.0012\) at 10 GHz [32]) with thickness \(t=0.254\) mm (10 mils).
* Dielectric adhesive Ablebond 8360 with nominal thickness 0.15 mm and \(\epsilon_{r}\approx 3\)-6, and tan \(\delta=0.005\)-0.01 (1-10 GHz)--electrical properties not specified in the datasheets. The \(Q\)-factor method (detailed in Sections 3.5 and 3.6) enables characterization via  
Figure 8: Device under simulation views.  
comparison between standalone and bonded DR configurations, though variations occur due to manual application preventing substrate overflow.
* A cavity with height \(h\) = 7 mm (arbitrary lateral dimensions), featuring lateral boundaries of conductivity \(\sigma\) = 1000 S/m and Perfect Electric Conductor (PEC) conditions for both bottom and top boundaries.
# Path Planning of Rail-Mounted Logistics Robots Based on the Improved Dijkstra Algorithm
## 2 Materials and Methods
### The Improved Dijkstra Algorithm
#### 2.1.4 Priority Rules and Conflict Coordination Strategy  
Setting priorities can effectively prevent robots from colliding in orbit. When two in-orbit robots collide, conflict coordination is first carried out according to the priority. The priority rules are as follows:  
1. The on-orbit priority of the robot is determined by the system, and it is not allowed to reset the priority for the robot performing material distribution.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Constrained Object** & **Constraints** \\ \hline Number of robots from site \(V_{j}\) to site \(V_{k}\) & \(N_{jk}^{t_{jk}^{a}}=L_{jk}\cdot\sum\limits_{i=1}^{i=1}x_{ri}^{t_{jk}^{a}}\) \\ Number of robots from site \(V_{k}\) to site \(V_{j}\) & \(N_{jk}^{t_{jk}^{a}}=L_{jk}\cdot\sum\limits_{i=1}^{i=1}x_{ri}^{t_{jk}^{a}}\) \\ The number of robots on the path from site \(V_{k}\) to \(V_{j}\) must not exceed the maximum capacity & \(N_{kj}^{t_{jk}^{a}}+N_{jk}^{t_{jk}^{a}}\leq L_{jk}/L_{nk}\) \\ No robot driving conflicts from site \(V_{k}\) to site \(V_{j}\) & \(N_{kj}^{t_{jk}^{a}}\cdot N_{jk}^{t_{jk}^{a}}=0\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Constraints of time window.  
2. The priority of the robot with a fault in orbit is the highest, and the track section is forbidden to pass, so its priority is 0. The priorities of the other robots performing tasks are set in advance. After the path is planned, the path conflict problem must be considered to prevent multiple robots from colliding in orbit. The conflict types can be divided into several categories: node conflict, opposite direction conflict, placeholder conflict, etc. * Node conflict: Multiple robots coming from different directions to the same site at the same time. * Opposite direction conflict: Two robots are driving opposite each other on the same road at some point. * Occupying conflict: The node that one robot will occupy the next moment is the node that the other robot is currently occupying. A conflict coordination strategy is developed as follows: 1. It is determined whether there is a common site for other orbiting robots on the initial path. If not, it will follow the initial planned path, or if there is a common station, then the priority of the robot is judged. 2. The robot with high priority has a driving priority and passes according to the established route. 3. The robot with low priority calculates the time required for waiting and replanning, choosing the strategy with a shorter time.