esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussions
### Upstream Transmission  
For upstream direction, considering the memory requirement and computational complexity, we did not apply LUT at the transmitter of the ONU. In this case, simple linear FFE at the Rx will result in a limited dynamic range. Therefore, a nonlinear VNLE is utilized at the receiver of the OLT instead. For the 2 nd-order VNLE, we continue to use the optimized FFE as its linear part. Similarly, we measure the BER performance versus the memory length \(L_{2}\) of VNLE with \(L_{1}\) fixed to 100 at \(-8\) dBm ROP. As depicted in Figure 8, the BER decreases as we increase \(L_{2}\). Yet, good receiver sensitivity and dynamic range can be achieved when \(L_{2}=5\) in consideration of the SD-FEC threshold. Figure 9a shows the results of \(L_{2}=5\) and \(L_{2}=13\) for comparison. So \(L_{2}\) is finally set to 5.  
Figure 8: BER versus the memory length \(L_{2}\) of VNLE with \(L_{1}=100\) at \(-8\) dBm ROP.  
Figure 7: (a) BER of post FFE combined with several LUTs. Eye diagrams with 3- (b) and 5-symbol (c) LUT pre-compensation and FFE at \(-3\) dBm ROP.  
As shown in Figure 9a, with the assistance of VNLE, all BERs are below the SD-FEC threshold when ROP is greater than \(-17\) dBm. However, the eye diagram in Figure 9b shows that the pattern effect has not been completely eliminated. By combining LUT with VNLE, the pattern effect can be better mitigated, as Figure 9c shows. However, it brings no benefit to the sensitivity and dynamic range under the SD-FEC threshold. So, as long as VNLE is used at the OLT Rx, LUT becomes unnecessary in the ONU Tx.  
Table 1 is a summary of some key parameters in the system. Tables 2 and 3 summarize the receiver sensitivity, power budget, dynamic range and computational complexity of 25 km downstream and upstream transmission with different equalization algorithms, respectively. Considering the SD-FEC threshold, a receiver sensitivity of \(-17\) dB and a dynamic range of \(>\)18 dB are both achieved. The achieved 29 dB power budget meets the PR-30 requirement.
# Fuzzy-Based Dynamic Time Slot Allocation for Wireless Body Area Networks
## 1 Introduction
* Dynamic time slot (DTS) allocation technique based on fuzzy logic that can enhance the packet delivery and reduce the end-to-end delay. The time slot to each node is allocated dynamically  
Figure 1: Fog-assisted architecture for in-hospital health management. based on the parameters such as available energy in a node, buffer availability and the packet arrival rate. The remaining paper is structured as follows: Section 2 summarizes some of the existing medium access control (MAC) layer protocols. Section 3 explains the system model for in-hospital health management application. Section 4 illustrates the tree formation and the cost function evaluation for energy efficient routing. Section 5 includes the design of an energy efficient dynamic time-slot allocation for each sensor node. Section 6 presents the performance results and analysis of the MCPS and DTS algorithm. Finally, Section 7 concludes the paper.
# Cognitive Load Approach to Digital Comics Creation: A Student-Centered Learning Case
## 5 Results  
All data were analyzed using SPSS statistical software, version 20, with a significance level of 0.05. In our experiment, the data were not normally distributed. Students performed better in the post-test. Thus, in order to check whether there was a significant improvement in students' performance, we conducted a Wilcoxon Signed Rank Test showing a significant difference between the performances in pre- and post-tests (\(p\) = 0.000). This indicated that digital comics creation helped students in their knowledge construction effort, consequently giving no support to hypothesis H0. The students, after creating their digital comics, also filled in the NASA-TLX questionnaire. In order to simplify the comprehension of the study and achieve a more plausible analysis of its results, we used the scale defined by [10]. Specifically, between 0 and 20%, a very low workload was indicated; between 20 and 40%, a low workload was indicated; between 40 and 60%, a normal workload was indicated; between 60 and 80%, a high workload was indicated, and between 80 and 100%, a very high workload was indicated. According to the above scale, the overall MWL was normal (mean value = 42.21), which is considered a positive MWL in terms of motivation to keep students active. The mental demand was low to normal (mean value = 32.98), the physical effort was very low (mean value = 11.79), the temporal demand was low to normal (mean value = 36.79), the effort was normal (mean value = 45.36), the performance was high (mean value = 71.19), for the frustration was low to normal (mean value = 32.86). The results regarding the overall workload as well as those across the six dimensions were similar in general terms, except for physical demand and performance, implying that digital comics creation does not burden MWL and its dimensions unevenly. The physical demand scored the lowest, as expected, because digital comics creation does not demand any particular physical effort on behalf of the students. Performance scored the highest, implying that students' attempts to create successful digital comics was the most demanding dimension. The results also showed that digital comics creation was not considered difficult, which is notably positive for instructional goal fulfillment. Concerning the weighing procedure, we found the weights of frustration and temporal demand to be the highest (3.17, 3.12), indicating that students put great importance on the time dimension and the feelings of insecurity and discouragement through the frustration dimension. The weights of mental demand, performance, and effort were 2.71, 2.60, and 2.43, respectively. The physical demand weight was the lowest, with a value of 0.98. Finally, to better understand the relationship between the workload and the dimensions as proposed by the NASA-TLX, we conducted a Spearman correlation analysis for higher robustness, and only significant correlations with a minimum absolute value of 0.3 are mentioned because such a correlation denotes at least a medium correlation strength [8].
# Towards the Internet of Smart Trains:
## 6 IoT-Enabled Services: From More Efficient Operations to New Business Models  
Legacy infrastructure is gradually being replaced by Train Management Systems (TMS), which transform trains in communications hubs that exchange data among them and with network control centers. Moreover, M2M communications allow operators to optimize and make safer use of equipment and infrastructure. The following subsections describe examples of IIoT-enabled services (a general vision is shown in Figure 3).  
Figure 3: Industrial IoT-enabled services relevant to the rail industry.
## 3 Proposed Conceptual Framework
### Proposed Step 1: Data Acquisition--Comprehensive Strategy for Video Data  
Here, we provide our recommendations for implementing a comprehensive video data acquisition strategy in seaport operations.  
* Strategic Camera Deployment:
* Employ high-fidelity Closed-Circuit Television (CCTV) cameras strategically positioned at critical areas such as entrance gates, cargo-handling cranes, and bustling container zones.
* Determine an accurate approach over random placement to ensure effective coverage.
* Calibration for Enhanced Performance:
* Conduct rigorous calibration of each camera, optimizing their performance in diverse lighting conditions and at various angles.
* Prioritize clear and high-resolution footage capture to enhance the dataset's quality.
* Consideration of Seaport Logistics:
* Dive into the intricate details of the seaport operations, including container types, loading/unloading activities, and traffic patterns.
* Align the camera placements with the operational intricacies in order to capture a diverse range of scenarios, reflecting real-world seaport situations accurately.
* Emphasis on Data Quality:
* Focus on both quantity and quality of the acquired data.
* Utilize cameras equipped with advanced features in order to minimize distortions and enhance image clarity.
* Implement data preprocessing techniques, including noise mitigation and video feed stabilization (the specifics of which are not detailed in this conceptual paper).  
Implementing these recommendations lays the groundwork for an effective understanding of seaport operations, preparing the terrain for future experimental applications of the proposed model. Figure 3 provides a structured representation of our recommended approach for data acquisition in seaport operations.  
In our proposed framework, we prioritize data privacy and ethical considerations. To address these concerns, we plan to implement several measures. Firstly, anonymization techniques will be applied when collecting the video data, ensuring that any personally identifiable information is either removed or obfuscated, thereby protecting the individuals'  
Figure 3: A schematic representation of the proposed framework for identifying the optimal lane and parking position for container trucks based on the crane’s position.  
identities. Secondly, we will enforce strict access controls, limiting data access solely to authorized personnel, preventing unauthorized individuals from viewing any sensitive information. Furthermore, our framework will fully comply with relevant data protection regulations, ensuring that all data collection and processing activities align with legal requirements. Additionally, we will establish data retention policies, clearly defining the duration for which the data will be stored, thereby minimizing the risk of prolonged exposure of sensitive information.
# Visual-Based Localization Using Pictorial Planar Objects in Indoor Environment
## 2 Related Work
### Marker-Less Visual Based Localization  
In order to avoid using unnatural markers for localization, other works have studied how to extract key information from natural camera views to calculate the camera pose. It can be divided into two categories of approaches.  
One is the frame retrieval approach using pixel feature extraction, such as SLAM [20,21], InLoc [22]. In these approaches, the camera frame, which contains the feature points used to construct a 3D map, is stored to build a key-frame library. By matching the camera frame with the key frames, the feature points in current frame can be mapped into the 3D map to estimate the pose of the camera.  
Their advantage is that the features or objects used for real-time frame matching are not pre-trained so that it can be applied in any unknown environment. On the other hand, the historical map is not able to be reused when there is something changed in the environment since it relies on the key frame library; the map may need to be re-built if there is any view change in the target space. The camera can gain its pose only when the camera orientation is similar to that used in map building as the features of the environment in other orientations are impossible to be captured and detected from the historical frames. Moreover, the camera cannot get real-world scale from marker-less based localization since the real scale of the point cloud is unknown in the 3D map.  
The other approach is visual object based localization that calculates camera pose from the object detected in the view. Some researches designed object detection and recognization algorithms [23,24,25,26] to extract significant points from camera view frames to match with that in the pre-built model for efficient pose estimation. However, they are not always reliable when one or more similar objects appearing in a frame and when the object are not completely captured by the camera. To achieve higher accuracy and robustness, DNN (Deep Neural Network) has been involved in more and more research [27,28]. However, the exact pose of the camera cannot be computed as it is hard to gain the detailed outlines or poses of the objects by from DNN approach. In our work, we have added the capabilities to find the detail outlines of identified objects in order to calculate their exact positions in a 3D map model.
## 2 Materials and Methods
### Spectral Data Post-Processing
#### 2.4.2 NDVI, NDWI, and Fractioned Water Pixel Analysis  
To identify and mask water and vegetation in the remote sensing data sets, the Normalized Difference Vegetation Index (NDVI) [51] and Normalized Difference Water Index (NDWI) [52] were calculated using the equations below:  
\[NDVI=(Red-NIR)/(NIR+Red) \tag{2}\]  
\[NDWI=(Green-NIR)/(NIR+Green) \tag{3}\]  
The NDVI ranges from \(-1\) to \(+1\), where values closer to \(1\) indicate increased green leaf density, and closer to \(-1\) equate to clear water, whereas barren rock exhibits very low NDVI. The NDWI also ranges from \(-1\) to \(+1\), where values closer to \(1\) usually correspond to water bodies, and between \(0\) and \(0.5\) (depending on the instrument and ground conditions) is either land or urbanized areas.  
Due to the close proximity between AMD and the water deposits, this study adapted a water classification methodology that was previously used in [41; 42], using atmosphericscreened TOA images to identify regions of mixed pixels (that interfere with Fe(III) iron classification). The use of band ratios with TOA-R images has the advantage of feasibly yielding indices that are atmospherically stable enough for delineation purposes [11]. However, the use of TOA images should be restricted to simplistic classifications [42]. The equations can be found below:  
\[LL=height_{lp}-0.9\times}prom_{lp} \tag{4}\]  
\[WL=height_{up}-0.9\times}prom_{up} \tag{5}\]  
where \(LL\) and \(WL\) stand for land limit and water limit, respectively. Both \(LL\) and \(WL\) were used as 100% land and water thresholds. \(height_{lp}\) and \(height_{up}\) are maximum heights of the land and water peaks, whereas \(prom_{lp}\) and \(prom_{up}\) are the respective prominence (maximum point of elevation versus its depth) of the land and water peaks. \(\text{FWP}_{total}\) is the total percent of mixed water pixels as calculated for the PS2.SD, Sentinel-2 (simulated), and UAV images taken on the 30 July 2020..  
In order to generate the NDWI histograms needed for the above FWP analysis, a modified OTSU adaptive thresholding [53] methodology was used to create buffered polygon features needed to capture the maximum extent of the waterbody at Wheat Maid and some of the surrounding land and vegetation. Buffered water polygons around Wheat Maid's central lower lagoon were intersected with the NDWI images, and the values were extracted so that histograms could be made for the FWP analysis.  
An NDWI pixel distribution graph was generated for each instrument, which needed to exhibit sufficient bi-model distribution that could be divided into two distinct peaks (one peak for water, the other for land). The plateau between both land and water peaks was then used to identify the region of mixed pixels, which can impact Fe(III) iron values.
## 5 Analysis of Template-Monomer Functional Interactions
### Fourier Transform Infrared Spectroscopy Analysis (FTIR)  
FTIR spectroscopy can analyze samples in many forms, including liquids, solutions, gases, powders, and pastes [113]. FTIR is used in many MIP developing to identify the formation of chemical bonds, especially hydrogen bonds between molecular templates and functional monomers, due to absorption of the infrared spectrum by the sample and the peak shifting [83; 114].  
[PERSON] et al. [14], in a theoretical spectrum, investigated the interaction between template chloramphenicol (CAP) and MAA functional monomer. There are peaks characteristic of strain vibrations O-H (3354 cm\({}^{-1}\)) and C=O (1669 cm\({}^{-1}\)) in the MAA spectrum. In the CAP spectrum, there are distinct peaks of O-H (3481 cm\({}^{-1}\)), C=O (1165 cm\({}^{-1}\)), and N-H stretch (3705 cm\({}^{-1}\)). In the spectrum of the CAP-MAA complex, which was compared with the respective MAA and CAP spectra, there was a slight shift in the stretching peaks of N-H and C=O to shorter wavenumbers, respectively 3705 to 3684 cm\({}^{-1}\) and 1669 to 1657 cm\({}^{-1}\). The peak shift to the lower wavenumber in the spectrum of the CAP-MAA complex compared to the pure CAP and MAA spectra indicates that hydrogen bonds are formed between CAP-MAA [14].  
Analysis of the functional template-monomer intramolecular interaction of hydrogen bonds using FTIR was carried out in the study of [PERSON] et al. [83] with deltamethrin (DM) as a template and AM as a functional monomer. In the AM spectrum, there is a peak characteristic of the NH stretching group at wavenumbers 3354 and 3184 cm\({}^{-1}\). Meanwhile, In the DM spectrum, there is a peak characteristic of the C=O group at wave number 1735 cm\({}^{-1}\). Compared to the single spectrum of AM, the DM-AM complex shows a shift in the peak of the NH stretching to shorter wave numbers 3346 and 3167 cm\({}^{-1}\). This peak shift indicates the formation of hydrogen bonds. The hydrogen bond formation is due to the reaction between the C=O group from DM with -NH from AM [83].  
[PERSON] and [PERSON] [115] also examined the hydrogen bond interaction between oxalic acid (OA) and AM as a functional monomer at the OA: AM (1:4) ratio. At a wavelength of 2640 cm\({}^{-1}\), a shift occurred, which is thought to result from NH stretching of four AM monomer units. In addition, there are hydrogen bonds in the OH group (3421 cm\({}^{-1}\)) and hydrogen bonds in the C=O group (1650 cm\({}^{-1}\)), which form broad peaks [115]. Therefore, it can be concluded that the analysis of hydrogen bonding interactions between templates and functional monomers using FTIR is influenced by the electron distribution, which will cause a shift of the peak to a lower wavenumber. The formation of hydrogen bonds can be determined based on the results of MIP analysis before and after extraction [97; 103; 116].
# Automatic 3D MRI-Ultrasound Registration for Image Guided Arthroscopy
## 1 Introduction
In standard clinical practice, close initialization is obtained by selecting 3D landmarks. Defining these landmarks requires not only tedious manual intervention but also keen knowledge of the targeted anatomy and clear geometrical understanding, especially in the case of 3D US, where there is limited volumetric coverage. Hence, to automate the registration process, instead of manually identifying landmarks in 3D, we show that obtaining coarse segmentations and using them for global initialization is a much more convenient alternative [17]. Segmentation-based initialization has been studied for prostate biopsy in [18], where registration was done between trans-retal US and the corresponding MRI volumes. Even so, the target organ complexity or limited field-of-view of US could demand further regularization of initialization. Hence, we introduce a strong two-level initialization procedure which utilizes structural information of the tissues segmented followed by the distance maps, which were proven to be useful for correspondence estimation [18; 19; 20]. In this work, we present the first attempt to automatically register intra-operative 4D US to pre-operative 3D MRI as a first step towards the feasibility of autonomous or robotic-assisted knee arthroscopy. Initialization of registration requires strong anatomical priors, which we draw from automatic segmentation of structures from both the modalities. Given the cross-modal nature and partial view obtained in the intra-operative modality (as compared to the view available in pre-operative MRI), an alternative domain that utilizes both the computed priors is adopted--the Euclidean distance transform of segmented masks. This choice addresses the dual needs of working with cross-modal registration, and partial views. The registration of segmented masks in distance space gives us the initial transform for the registration of original volumes. A globally convergent similarity metric, LC\({}^{2}\)[11], is used for this registration.
# Positioning Errors Measurement of CNC Machine Tools Based on J-DBB Method
## 6 Conclusions  
This paper proposes a J-DBB method that combines the structural characteristics of traditional DBB and two-bar linkage DBB. The three-degree-of-freedom spherical joint is a key innovation of the J-DBB method, which greatly improves the flexibility and measurement range of the J-DBB. J-DBB can be measured freely in the spherical space with the maximum bar length as the radius.  
This paper simulated and analyzed four positioning errors of the machine tool based on the J-DBB method, including the uniform contraction error of ball screw and linear grating, periodic error of the ball screw and linear grating, interference of measurement devices error, and opposite clearance error. The influence of the length and angle of the two connecting bars on the positioning errors is studied. The simulation results based on the J-DBB method show that the equipment error increases with the increase of length \(L\) and remains unchangeable with the variation of angle.  
The J-DBB realizes the whole area measurement of machine tool error by measuring the angle of the spherical joint, which helps to better explore the spatial distribution of CNC machine tool error and clarify the error of the local area of the machine tool.  
Conceptualization, W.W. and Y.Z.; methodology, T.S. and Y.Z.; validation, K.X. and H.W.; writing--original draft preparation, T.S., Z.C. and Z.S.; writing--review and editing, T.S., W.W., K.L. and H.Y.; supervision, W.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Zhejiang Provincial Natural Science Foundation of China under Grant No. LZ16E050001 and the National Natural Science Foundation of China under Grant No. U1709206 and No.51275465.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Not applicable.  
The authors declare no conflict of interest.  
The distance between the spherical joint and each ball  
\(\theta_{1}\)The angle between horizontal plane and bar 1  
\(\theta_{2}\)The acute angle between two connecting bars  
\(\theta_{3}=vRt\)The angle of rotation of machine tool after running at speed \(v\) for \(t\) seconds  
\(a\)The contraction ratio of the ball screw and linear grating, \(\mu\)m/mm  
\(ax\)The contraction ratio of the ball screw and linear grating on \(x\)-axis, \(\mu\)m/mm  
\(p\)The screw pitch of the ball screw or the grating pitch of the linear grating  
\(d\)The amplitude  
\(\varphi\)The phase angle  
\(b_{x}\)The ratio of amplitude to velocity along the \(x\)-axis direction  
\(b_{y}\)The ratio of amplitude to velocity along the \(y\)-axis direction  
\(\varphi_{x}\)The phase angle along the \(x\)-axis direction  
\(\varphi_{y}\)The phase angle along the \(y\)-axis direction  
\(F\)The velocity of machine tool spindle, m/s  
\(f\)The opposite clearance  
\(r\)The design trajectory of circular motion  
\(r_{1}\)The real trajectory of circular motion