esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mathematical Formulation
### Coordinate Transformation
#### 3.2.2. Rotation  
Considering \(\underline{\tilde{E}}_{c}^{n=1,\dots,N}\big{(}\tilde{\theta},\tilde{\varphi} \big{)}=\underline{\tilde{E}}_{c}\big{(}\tilde{\theta},\tilde{\varphi}\big{)}\) the same for all the elements (e.g., mutual coupling is not considered), the element pattern is first sampled in cartesian coordinates as  
\[\tilde{x} =\sin\tilde{\theta}\cos\tilde{\varphi} \tag{1}\] \[\tilde{y} =\sin\tilde{\theta}\sin\tilde{\varphi}\] (2) \[\tilde{z} =\cos\tilde{\theta}. \tag{3}\]  
The rotated far field is then computed for each antenna position as [33]  
\[\underline{\tilde{E}}_{c}^{n}(x,y,z)=\begin{bmatrix}\cos\theta_{r}^{n}\cos \theta_{r}^{n}&-\sin\varphi_{r}^{n}&\sin\theta_{r}^{n}\cos\varphi_{r}^{n}\\ \cos\theta_{r}^{n}\sin\varphi_{r}^{n}&\cos\varphi_{r}^{n}&\sin\theta_{r}^{n} \sin\varphi_{r}^{n}\\ -\sin\theta_{r}^{n}&0&\cos\theta_{r}^{n}\end{bmatrix}\begin{bmatrix}\underline {\tilde{E}}_{x}(\tilde{x},\tilde{y},\tilde{z})\\ \underline{\tilde{E}}_{y}(\tilde{x},\tilde{y},\tilde{z})\\ \underline{\tilde{E}}_{z}(\tilde{x},\tilde{y},\tilde{z})\end{bmatrix} \tag{4}\]  
and  
\[(x,y,z)=\begin{bmatrix}\cos\theta^{n}\cos\varphi&-\sin\varphi_{r}^{n}&\sin \theta_{r}^{n}\cos\varphi_{r}^{n}\\ \cos\theta_{r}^{n}\sin\varphi_{r}^{n}&\cos\varphi_{r}^{n}&\sin\theta_{r}^{n} \sin\varphi_{r}^{n}\\ -\sin\theta_{r}^{n}&0&\cos\theta_{r}^{n}\end{bmatrix}\begin{bmatrix}(\tilde{x },\tilde{y},\tilde{z})\\ (\tilde{x},\tilde{y},\tilde{z})\\ (\tilde{x},\tilde{y},\tilde{z})\end{bmatrix}, \tag{5}\]  
where \((\theta_{r},\varphi_{r})^{n}\) are the rotation angles for the \(n\)th element. The rotated electric field can be converted back into spherical components and coordinates \(\underline{\tilde{E}}_{sph}^{n}=\big{\{}\underline{\tilde{E}}_{\theta}, \underline{\tilde{E}}_{\varphi}\big{\}}^{n}\in\mathbb{C}\) as  
\[\underline{\tilde{E}}_{sph}^{n}(\theta,\varphi)=\begin{bmatrix}\cos\theta \cos\varphi&\cos\theta\sin\varphi&-\sin\theta\\ -\sin\varphi&\cos\varphi&0\end{bmatrix}\begin{bmatrix}\underline{\tilde{E}}_ {x}(x,y,z)\\ \underline{\tilde{E}}_{y}(x,y,z)\\ \underline{\tilde{E}}_{z}(x,y,z)\end{bmatrix}^{n}, \tag{6}\]  
where \((\theta,\varphi)\) are computed accordingly to  
\[\theta =\frac{\pi}{2}-\text{atan2}\bigg{(}\text{$z$, $\sqrt{x^{2}+y^{2}}$}\bigg{)} \tag{7}\] \[\varphi =\text{atan2}(y,x). \tag{8}\]  
Electric field \(\underline{\tilde{E}}^{n}=\big{\{}\underline{E}_{LHCP}^{n},\underline{E}_{ RHCP}^{n}\big{\}}\in\mathbb{C}\) can be expressed in LHCP, RHCP components as [34]  
\[E_{LHCP}^{n}(\theta,\varphi) =\frac{1}{\sqrt{2}}\Big{(}\underline{\tilde{E}}_{\theta}^{n}-j \underline{E}_{\varphi}^{n}\Big{)}, \tag{9}\] \[E_{RHCP}^{n}(\theta,\varphi) =\frac{1}{\sqrt{2}}\Big{(}\underline{\tilde{E}}_{\theta}^{n}+j \underline{E}_{\varphi}^{n}\Big{)}, \tag{10}\]  
which are sampled in a non-uniform grid and must be interpolated.
# Thermal-Mechanical Behaviour of Road-Embedded Wireless Charging Pads for EVs
## 2 Double-D (DD) Inductive Power Transfer Pad  
The 1/4 -scale IPT pad used in this study was adapted from the structural analyses detailed in [PERSON] et al. [19] and is shown in Figure 1. The length and width of this pad was scaled down to 1/4 of that of the 10 kW pad analysed by [PERSON] et al. [7]. Because the thickness of the ferrites, casing and litz wire could not be scaled down in a uniform manner, the thickness of the 1/4 -pad was around 60% that of the 10 kW pad. Two litz wires, with a diameter of 4 mm, were wound side-by-side in a bifilar fashion to create a coil in the shape of a double-D (DD). Four rectangular MnZn ferrite cores (100 \(\times\) 30 \(\times\) 5 mm) were situated beneath the coil to shape the magnetic flux. The ferrites were CF295 and sourced from Cosmo Ferrites. Beneath the ferrite, is a 3 mm thick aluminium plate to absorb leakage flux below the ferrite. These components were assembled into a 3D printed polyamide 12 (PA2200 from EOS) casing and potted with a thermosetting polyurethane: UR5608 from Electrolube. The potting material encapsulates and fixes the internal componentry within the case, aiding in the conduction of heat away from the system and improving structural integrity. A 4 mm gap was set in between the ferrite and casing, and between the ferrite and aluminium plate, to allow for instrumentation wiring to run in between.  
Figure 1: Exploded view of double-D (DD) IPT pad. Outer dimensions: 225 \(\times\) 133 \(\times\) 31.6 mm.
## Results and Discussion
### Nitrogen Emissions
[28] found 77% NH\({}_{3}\) reduction for dairy cattle slurry treated with wood shavings biochar (12% _v/v_). At this study, the reduction of NH\({}_{3}\) emissions (26% reduction) by the addition of clinoptilolite, was lower than emissions reported in other studies [29, 30] for cattle and pig slurries (50-70% reduction), which could be related with the higher rate of clinoptilolite used in referred studies (0.40-6.25 _w/w_). In addition, no advantages were gained from the combination of biochar with clinoptilolite. The daily N\({}_{2}\)O fluxes follow the same trend, independently of the treatment, with a progressive increase (0.5 to 2 mg N\({}_{2}\)O m\({}^{-2}\) h\({}^{-1}\)) in the first 50 days followed by a strong increase (2 to 10 mg N\({}_{2}\)O m\({}^{-2}\) h\({}^{-1}\)) until the end of the experiment (Figure 2B). Compared to the treatment Control, the amended treatments reduced significantly (\(p<0.05\)) the daily N\({}_{2}\)O fluxes in the first 50 days (14-18% lower) followed by a significant increase (\(p<0.05\)) until the end of the experiment (32-67% higher) (Figure 2B). On most measurement days, significantly higher (\(p<0.05\)) N\({}_{2}\)O fluxes from day 50 until the end of the experiment were observed according to the following order: Clinoptilolite \(>\) Biochar \(+\) Clinoptilolite \(>\) Biochar \(>\) Control (Figure 2B). In the 85 days of experiment, the daily N\({}_{2}\)O fluxes of treatments Clinoptilolite and Biochar \(+\) Clinoptilolite were higher in about 11% relative to treatment Biochar (Figure 2B). The cumulative N\({}_{2}\)O emissions (expressed in g m\({}^{-2}\) or as % of applied N) were not significantly different (\(p>0.05\)) between amended treatments; and only treatment Clinoptilolite was significantly higher (\(p<0.05\)), by about 30%, than treatment Control (Table 2). The cumulative N\({}_{2}\)O loss in treatment Clinoptilolite was higher in about 21% relative to treatment Biochar, but not statistically different (\(p>0.05\)) (Table 3). The N\({}_{2}\)O losses originated in the nitrification and denitrification processes, which occurs when both aerobic and anaerobic conditions coexist in the slurries [31]. The N\({}_{2}\)O emissions observed in the present study appears to be mainly emitted by the nitrification process, because the aerobic condition created by the continuous air exchange and low depth of the slurry containers. The addition of biochar and clinoptilolite alone or combined led to NH\({}_{4}\)\({}^{+}\) retention, decreasing the N\({}_{2}\)O losses until day 50 of experiment. From this day until the end of the experiment an increase of N\({}_{2}\)O emissions was observed, which may be related with the saturation of the capacity of NH\({}_{4}\)\({}^{+}\) adsorption of the additives [19, 20].
# Mobile Robot for Security Applications in Remotely Operated Advanced Reactors
## 4 Mobile Robots Categories  
Numerous industries all over the world in one way or the other have considered using automated robots to increase their productivity. As our research is also focused on using the mobile dog robot to improve physical security at nuclear power plants, it is imperative for us to dive into detail about mobile robots and their types. There are several categories of mobile robots. Arms/manipulators are the most common type of robot that has been used in these sectors for decades. Despite their successes, these commercial robots suffer from a fundamental disadvantage: a lack of mobility. Although robotic arms/manipulators have been considered a type of mobile robot, due to their limited range of motions, we cannot use them in a wider scope of applications such as patrolling, transporting, cleaning, and so on. In contrast, mobile robots can travel easily and carry out flexible tasks within manufacturing plants, moving around to maximize their effectiveness. For instance, AGV (autonomous guided vehicle) robots transport parts between assembly stations using electrical guide wires or onboard lasers for navigation. Helpmate service robots deliver items in hospitals by tracking ceiling lights' positions. Autonomous cleaning robots are also developed for large buildings [26].  
Mobile robots are also being used as an automatic hotel assistant system that uses mobile platforms to interact with visitors and staff, assisting in activities such as delivering things, providing instructions on hotel services, guiding guests to their rooms, and providing general information [27].  
Mobile robots are also extensively being used for disaster response applications. A huge earthquake and tsunami devastated eastern Japan in March 2011, causing significant destruction and loss of life. The Fukushima Daiichi nuclear power plants were badly damaged, causing power outages. Hydrogen explosions damaged three nuclear reactor buildings, with one reactor losing control. Initially, the focus was to analyze the accident site's damage and radiation levels; however, this was too dangerous for humans due to the risk of significant radiation exposure. To respond to this situation, [PERSON] et al. conducted research involving the use of a mobile robot, Quince, to carry out surveillance missions in such a dangerous environment [28].  
An autonomous robot can determine and execute tasks on its own, with the assistance of a perception system. Locomotion, perception, cognition, and navigation are all fundamental features of mobile robotics. Understanding mechanics, kinematics, dynamics, and control theory is essential for understanding locomotion. Signal analysis, including computer vision and sensor technology, is involved in perception. Cognition is the control system that interprets sensor input and controls the robot's activities. Navigation requires an understanding of planning algorithms, information theory, and artificial intelligence [29].  
Based on their locomotion system, mobile robots can be classified [28] into the following major categories.
## 4 Experiments and Measurement Uncertainty Analysis
### Measurement Accuracy Experiment  
Diameter measurement experiments were conducted in the clean room. The \(\phi\)3-mm probe shown in Figure 8 was mounted on the HDMM. To validate the measuring method and the developed HDMM, a calibrated \(\phi\)20-mm ring gauge standard with a depth of 35 mm from Edmunds Gages was used. The standard was mounted on the 4D worktable through a clamper, as shown in Figure 15. The measurement results are shown in Table 1. The average of ten diameter measurement results was 19.99998 mm. The diameter calibration results from NIM (National Institute of Metrology) of China, PTB of Germany, and the results obtained by the authors (HIT-UOI, Harbin Institute of Technology, center of Ultra-precision Optoelectronic Instrument) are compared in Table 2. The uncertainty intervals of all calibrations overlapped, indicating agreement.  
Figure 14: Diameter measurement results as a function of measured depth.  
Figure 15: Measurement of \(\phi\)20-mm ring gauge standard with the HDMM.
## 2 The Proposed Bi-OCDMA System
Similarly, the complement matrix of \(X\) can be decomposed into the following form:  
\[\overline{X}=\left[ \begin{array}{cccccccc}-1&1&-1&-1&-1&1&1&1\\ 1&-1&-1&-1&1&1&-1&1\\ -1&-1&-1&1&1&-1&1\\ -1&-1&1&1&-1&-1&1\\ 1&1&-1&1&-1&-1&1\\ 1&-1&1&-1&-1&-1&1\\ \end{array} \right]=\left[ \begin{array}{cccccccc}0&1&0&0&0&1&1&1\\ 1&0&0&0&1&1&0&1\\ 0&0&0&1&1&0&1&1\\ 0&0&1&1&0&1&0&1\\ 0&1&1&0&1&0&0&1\\ 1&0&1&0&0&0&1\\ 1&0&1&0&0&0&1&1\\ 1&0&1&0&0&0&1&1\\ \end{array} \right]=\overline{C_{V}}-C_{H} \tag{3}\]  
Therefore, the optical signal \(R_{k}\) sent from the encoder of the \(k\)-th user can be derived as:  
\[R_{k}=b_{k}(c_{kV}-\overline{c}_{kH})+(1-b_{k})(\overline{c}_{kV}-c_{kH}) \tag{4}\]where \(b_{k}\in\{0,1\}\), \(c_{kV}\) and \(c_{kH}\) are the \(k\)-th codewords for encoding with the vertical and horizontal polarization states, respectively. That is, if \(b_{k}=1\), the spectrum of the coded signal corresponds to \((c_{kV}-\overline{c}_{kH})\). Similarly, the spectrum of the coded signal is \((\overline{c}_{kV}-c_{kH})\) if \(b_{k}=0\). Table 1 shows the modified M-sequence code of length eight with bipolar schemes. The symbols \(H\) and \(V\) represent the optical signals with horizontal and vertical polarization states, respectively. Based on these derivations, a corresponding FSO system using BICODMA schemes can be constructed. To implement modified M-sequence codes with length \(N\) in the proposed schemes, the results of the following correlation must be obtained:  
\[\theta_{xx}(k,l)=\sum_{i=1}^{N}{(c_{kV}(i)-\overline{c}_{kH}(i))(c_{lV}(i)- \overline{c}_{lH}(i))}=\left\{ \begin{array}{c}N,\;k=l\\ N/2,\;k\
eq l\end{array} \right. \tag{5}\]  
\[\theta_{x\overline{x}}(k,l)=\sum_{i=1}^{N}{(c_{kV}(i)-\overline{c}_{kH}(i))( \overline{c}_{lV}(i)-c_{lH}(i))}=\left\{ \begin{array}{c}0,\;k=l\\ N/2,\;k\
eq l\end{array} \right. \tag{6}\]  
The correlation properties in Equations (5) and (6) verified that the modified M-sequence codes can be implemented in the proposed system to alleviate MAI with a balanced photodetection method. Figure 1 illustrates the design of the proposed encoder, which is composed of two continuous wave (CW) laser arrays as the light source, a PRBS generator as the bit sequence generator, two sets of electrical bias, electrical gain, and PSK pulse generator as the modulation signal, two-phase modulators for optical signal modulation, four optical couplers, several optical adders, an inverter, two polarization splitters (\(0^{\circ}\) and \(90^{\circ}\)) for polarization coding, and a polarization combiner.
## 3 Experimental and Simulation Setup
### Experimental Setup  
The experiment was conducted at the University of Antwerp Campus Groenenborger in Antwerp, Belgium. It involved a real-world deployment scenario with an OBU mounted in a BMW and an RSU strategically placed on the campus, establishing a 5G connection for communication. The goal of the experiment was to generate Cooperative Awareness Messages (CAMs) in the OBU and send them to the RSU over the 5G connection from different distances while recording the energy consumption and central processing unit (CPU) load at the RSU (see Figure 1).
## 1 Introduction
[32] discovered that introducing flow distribution devices suppresses jetting within the bag filter and reduces dust accumulation on the filter surface. For example, [PERSON] et al. [33] used flow distribution deflectors to modify airflow direction and velocity. [PERSON] et al. [34; 35] designed three sets of distributors with different numbers and angles in the hopper to organize the airflow. These studies demonstrate that adding flow distribution devices is the most effective approach for optimizing airflow. Thus, this study proposes the addition of deflectors in the hopper to optimize the existing flow field distribution. Despite advancements in optimizing bag filters, several key limitations remain in current research. First, many studies focus on individual chambers or neglect the impact of filter bags on overall airflow distribution. Second, the localized effects of airflow on filter bag abrasion and dust scouring are often underexplored, leading to an incomplete understanding of wear mechanisms. Third, optimization strategies typically lack comprehensive, multi-dimensional evaluation, limiting their practical effectiveness. Additionally, while CFD methods offer valuable insights, most studies fail to experimentally validate optimization measures due to operational challenges. These gaps underscore the need for more robust and validated optimization approaches that address both airflow dynamics and practical performance across multi-chamber systems. This study introduces a novel optimization strategy for improving airflow distribution in bag filters by incorporating descending deflectors. The proposed optimization measures are validated through both scaled-down model experiments and full-scale CFD simulations, ensuring the robustness and scalability of the results. By using a multi-dimensional airflow uniformity evaluation system, this research offers a more precise and comprehensive approach to assessing airflow uniformity and filter performance. These innovations not only enhance the understanding of airflow dynamics in bag filters but also provide a practical solution for improving filtration efficiency and operational performance in industrial applications. Additionally, by providing quantifiable indicators of pressure drop and airflow distribution, the study can solve key technical problems such as uneven airflow and inconsistent filter bag life, which are common problems in industrial dust removal systems. The proposed solution can provide theoretical guidance for the structural optimization of multi-chamber filters and has important engineering application value.
# Utilizing Ensemble Learning to Improve the Distance Information for UWB Positioning
## 2 Preliminaries
### k-Means Clustering  
Clustering is a way bto group data into disjoint sets without labeling training data beforehand and is one of the most common unsupervised algorithms [4,8,9]. Moreover, classifying data into several disjoint sets should be purposeful. \(k\)-means clustering is one of the most utilized manners; it can group elements into \(k\) separate clusters for \(k\geq 2\).  
The pseudocode is abstracted below [10]:  
* Place \(k\) centroids \(c_{1},c_{2},\ldots,c_{k}\) randomly;
* For each point, which is not a centroid, find the nearest centroid and assign it to the cluster where the nearest centroid belongs;
* After assigning each point to its cluster, find a new centroid for each cluster;
* Repeat steps 2 and 3 until a predefined number of iterations or convergence.  
\(k\)-means clustering is very simple, with low computational complexity.
# Nearshore Benthic Mapping in the Great Lakes:
## 2 Materials and Methods  
### Study Area  
For this study, an 11.7 square kilometer area in southwest Lake Michigan along Illinois Beach State Park, Illinois was selected (Figure 1). It falls within Lake County, just north of the heavily populated Cook County (Chicago's North Shore) and just south of the Wisconsin state line. As noted in [PERSON] et al. [33] the southwest Lake Michigan shoreline has experienced periods of erosion due in part to impacts from anthropogenic activity and development together with recent high lake levels. The study area, while currently in a more natural, state-owned park setting, historically experienced urban housing development and construction of a military base during low lake levels (1915-1975), followed by severe shoreline erosion during periods of high lake levels, at which point shoreline armoring and protection structures and infrastructure along the immediate shoreline were destroyed with remnants currently submerged in the nearshore part of the study area [33]. The area was selected for this study in part because it is the site of on-going shoreline mapping to better understand regional sediment transport with coordinated efforts conducted between the Illinois Coastal Management Program, Illinois State Geological Survey (ISGS), USACE, the National Oceanic and Atmospheric Administration (NOAA), and other local partners. As such, it provides an ideal opportunity to maximize a variety of on-going data collection activities. While the majority of the study area consists of unconsolidated sand, it is marked by the unique anthropogenic remnants described earlier, patches of substrate (coarse gravel fields), and biotic (_Cladophora_ sp. macroalgae and quagga mussels [Dreissena rostriformis bugensis]) features, and sporadic cultural features (shipwrecks). This is consistent with the nearby Wisconsin--Lake Michigan lake-bed characterization conducted by NOAA in fulfillment of the proposed sanctuary [31].
# User-Perceived Capacity: Theory, Computation, and Achievable Policies
## 7 Conclusions  
This paper has focused on the concept of user-perceived throughput, a crucial performance metric that encapsulates the essence of the user experience in mobile multimedia services. Our study has considered the impact of channel state information, both at the receiver (CSIR) and the transmitter (CSIT), on enhancing the user-perceived capacity. It is observed that the capacity is severely limited when only CSIR is available, potentially reaching a lower bound of zero. In contrast, the integration of CSIT with CSIR facilitates substantial improvements in the user-perceived capacity associated with adaptive power and rate strategies. We have proposed an MDP-based methodology for calculating the user-perceived capacity under a cross-layer design of power and rate adaption. Based on this, we have solved an average power minimization problem with a constraint on the user-perceived capacity by converting it as linear programming. Consequently, the maximum user-perceived capacity under different power consumption amounts has been calculated based on a binary searching method. Furthermore, our findings have indicated that the optimal cross-layer policy adheres to a threshold-based structure dependent on  
Figure 7: The threshold structure of the optimal policy: the three subfigures present the optimal policy with \(|h[t]|^{2}=1,4,\text{ and }9\), successively.  
time, backlog, and channel gain. Additionally, when power adaptation is implemented as the sole policy, the user-perceived capacity corresponds to the hard-delay-constrained capacity established in our previous research [9], realized through a combination of diversity and channel inversion techniques. Note that several critical issues, including massive user access, the inter-dependency of data, and secure requirement provisioning, remain to be addressed concerning user-perceived capacity. In future research, we aim to expand this work to achieve immersive, highly dense connectivity, and secure wireless access technologies for emerging human-centric applications in 6G, such as extended reality, digital twins, and remote driving.  
Conceptualization, W.C.; formal analysis, Y.L.; methodology, Y.L. and X.Z.; software, Y.L. and X.Z.; writing--original draft, Y.L. and X.Z.; writing--review and editing, X.Z. and W.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant No. 62401140.  
Not applicable.  
Data Availability Statement: Data are contained within this article.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# A Wireless EEG System for Neurofeedback Training
## 4 Materials and Methods
### Statistics  
Nonparametric bootstrap tests compared the threshold-selected z-scores for pairs of brain locations of the groups (controls vs. pre-training group with DD, controls vs. post-training group with DD, and pre-training vs. post-training group with dyslexia) and the hemispheres (left vs right side), with subsets of 1000 permutations for each comparison. The waves with \(\mid\)z-scores \(\mid\) higher than the group standard deviation were categorized as outliers, which were excluded from the comparisons. The Bonferroni correction for the multiple comparisons was applied to the significance level in the permutation tests. For behavioral analysis of the task (correct responses, response times, and reading achievements), non-parametric Kruskal-Wallis tests were performed in pre-post conditions.