esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In response to the issue of low efficiency and inadequate coordination of mobile robotic arms in home environments, we propose a novel method named OGM-IRM. This method leverages the Cartographer algorithm to gather environmental data and store them in the form of an occupancy grid map. Subsequently, the working space and joint angles of the robotic arm are discretized, and distribution maps of the robotic arm's grasping ability at various positions in the working space are generated. These maps are converted into an inverse reachability map to determine the optimal base placement of the mobile chassis by considering obstacle information in the occupancy grid map, thereby achieving base optimization based on the grasping pose. In summary, this paper introduces an approach for selecting the optimal base placement of a wheelchair robot in a home environment based on task objectives.  
The proposed algorithm significantly enhances the efficiency of the WMRA in performing a wide range of home tasks and reduces the interaction time between humans and machines by a significant margin. Experimental validation demonstrates that the OGM-IRM approach can lower human-machine interaction time by 97.31% and reduce task completion time by 40.57%, increasing the range of executable tasks compared with the traditional method and reducing task completion time by 23.48% for the same task, showcasing its practicality and promoting its value.  
There are still some shortcomings in the proposed method; for example, the current OGM-IRM algorithm necessitates substantial kinematic solving, leading to a long execution time and an inability to produce the optimal base placement of the WMRA in real time.  
In the future, the algorithm needs to be further optimized in terms of execution time and computing resource consumption, so that it can be better deployed in embedded systems. To address these limitations, future research could consider screening the workspace to reduce the number of voxels and improve the algorithm speed.  
Conceptualization, M.Z., Y.H. and Y.L. ([PERSON]); methodology, Y.H.; software, Y.H., Y.L. ([PERSON]) and R.H.; resources, M.Z.; data curation Y.H., Y.L. ([PERSON]) and R.H.; writing--original draft preparation, Y.H.; writing--review and editing, M.Z. and Y.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Key R&D Program of China (Grant No. 2018 YFB1309400).  
Not applicable.  
Figure 15: Comparison chart of time for various methods.
## 3 Proposed Method
### Sizing Point Detection  
#### 3.1.1 HRNet-Based Sizing Point Detector  
We applied an HRNet [19]-based keypoint estimation model as a sizing point detector. HRNet maintains high-resolution representations and simultaneously connects multi-resolution subnetworks, showing excellent comprehensive performance in learning information around keypoints in images. This architecture proves effective in restoring low-resolution features to high-resolution representations [35]. This feature has the same advantage in the problem of keypoint detection for clothes, as the visual features around keypoints are more diverse than those of humans [36].  
Regarding the architecture of the point detector, we adopted HRNet-W48. W48 denotes the widths of the subnetworks in the third stage, which is relatively larger than the other variant of HRNet, HRNet-W32. The structure of the network consists of four parallel stages of subnetworks (\(N_{sr}\)). In the diagram below, \(N\) represents the subnetwork, the subscript \(s\) and \(r\) denote the stage and the resolution index, respectively. As shown in the diagram below, it begins with a high-resolution subnetwork (\(N_{11}\)) in the initial stage. Each subnetwork is branched into two subnetworks: a multi-resolution subnetwork (\(N_{11}\to N_{21}\)) and a high-to-low resolution subnetwork (\(N_{11}\to N_{22}\)). As a result, parallel multi-resolution subnetwork processes maintain the same resolution-level representations among the same \(r\) networks.  
\[\begin{split} N_{11}\to N_{21}\to N_{31}& \to N_{41}\\ \searrow N_{22}\to N_{32}&\to N_{42}\\ \searrow N_{33}&\to N_{43}\\ \searrow N_{44}\end{split} \tag{1}\]All parallel subnetworks repeatedly exchange the information from other stages. According to [PERSON] et al. [19], this repeated multi-scale fusion process allows the subnetwork to have rich representation by bidirectional information exchange of high-to-low and low-to-high resolutions, rather than restoring from low to high.  
At the last stage, the high-resolution representation output at the final stage estimates heat maps of each keypoint. The loss of estimation error is the Euclidean distance (\(d_{i}^{2}\)) between the ground truth coordinates \((cx_{i},cy_{i})\) and the estimated coordinates \((c\hat{x}_{i},cy_{i})\) of the keypoints. The loss function uses the mean square error as follows,  
\[\begin{split} Mean\ Squared\ Error&=\frac{1}{n} \sum_{i=1}^{n}d_{i}^{2}\\ &=\frac{1}{n}\sum_{i=1}^{n}\sqrt{(cx_{i}-c\hat{x}_{i})^{2}+(cy_{i }-c\hat{y}_{i})^{2}}\end{split} \tag{2}\]  
meaning the problem of finding the shortest straight line distance from the ground truth coordinates of the keypoint.
## 6 Conclusions and Future Work  
In the present study, the XGBoost algorithm was utilized for the LS mapping in the upper basin of Ataturk Dam, a great investment in the Southeast Anatolian part of Turkey. The study area was relatively large (2718.7 km\({}^{2}\)), which is often a limiting factor in LS mapping. The study area is between the downstream of the Karakaya Dam and the upstream of the Ataturk Dam. In this region, the velocity of the Euphrates River was low and it did not carry too much sediment. However, landslides were likely to contribute to the sediments of the Euphrates River in this section. Therefore, these sediments can cause a decrease in the Ataturk dam reservoir. From this point of view, assessment of the landslide susceptibility of the study area is of particular importance, because there are large landslides in the study area, and it is possible for the displaced and disturbed landslide material to reach the Euphrates River during periods of heavy rainfall. As a result of the study, it was clearly explained that the main factors controlling the landslides in the study area were the lithology, altitude and the TWI. It was clearly demonstrated that using the methodology used here in regional landslide assessment studies produced very useful and promising results.  
The performance of the algorithm was high as indicated by various metrics, such as overall accuracy (90.18%), average precision (0.94), F1-score (non-landslide class = 0.92 and landslide class = 0.88) and AUC (0.96). The results show that the XGBoost algorithm and the proposed hyperparameters were successful for the regional LS mapping and can be recommended for future studies. Five landslides, which were not included in the training and modeling stages, could be detected by the proposed algorithm. In addition, the freely available EU-DEM from Copernicus Land Monitoring Service had sufficient quality (i.e., spatial resolution and accuracy) for the purposes of the study.  
**Author Contributions:** Conceptualization and validation, C.G. and S.K.; methodology, S.K., R.C. and C.G.; software, investigation and data curation, R.C.; formal analysis and supervision, S.K.; writing--original draft preparation and visualization, S.K. and R.C.; writing--review and editing, C.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors sincerely thank [PERSON] for her kind support.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Cognitive Anycast Routing Method for Delay-Tolerant Networks
## 4 Evaluation Testbed
Since a reliable convergence layer was used, the final experiment statistics were collected after all of the bundles arrived at the anycast destination. Note that the intention is not to find the steady-state results, but to characterize the average performance of a selected number of bundle transmissions that represent as a whole, for example, the transmission of a large scientific dataset split into 1000 bundles. The experiments were repeated multiple times and where possible; the 95% confidence interval is shown.
## 2 Computed Tomography and Aortic Valve
### CT Assessment of the Aortic Valve for Transcatheter Procedures
#### 2.3.2 Aortic Annulus  
The aortic annulus for implanting percutaneous prostheses is not directly visible. It is a virtual ring that passes over a plane via the basal insertions (nadir) of the aortic valve cusps. It has an oval shape that varies during the cardiac cycle, even in patients with severe calcific AS. While the size of the aortic annulus was initially determined almost exclusively via two-dimensional transesophageal echocardiography (TEE), it has been proven that the aortic annulus is a dynamic measure during the cardiac cycle. Thus, 2D imaging could not be precise enough, and only 3D imaging can display a correct measurement. Furthermore, compared with a two-dimensional assessment of the annulus, MDCT-based measures have been proven to be highly reproducible and provide a deeper understanding of annular geometry [35]. [PERSON] et al. found that, during the cardiac cycle, the aortic root varies its shape, suggesting that an MDCT evaluation during the early systole phase is the best choice for TAVI planning [36].  
Annular dimension can be quantified with multiple methods: cubic spline interpolation, polygon, attenuation/Hounsfield-unit-based contour detection, and freehand contour.  
Figure 2: (**a**) Preoperative measurements of peripheral accesses for TAVI feasibility. (**b**) Preoperative femoral and iliac courses evaluation.  
ECG-synchronized, ideally multiphasic, dataset should be used for proper measurement, identifying the reconstruction phase with the largest annular dimensions, thus ensuring accurate device sizing [24]. MDCT sizes the aortic annulus using annular area and perimeter measurements. In the Pivotal Trial of Medtronic Corevalve and the PARTNER 3 trial, it was demonstrated that annulus sizing based on CT measurements reduces the risk of complications such as paravalvular leakages. Moreover, the aortic annulus perimeter or cross-sectional area is superior to the annulus diameter for reducing the likelihood of paravalvular aortic regurgitation (PAR) [37; 38] (Figure 3).  
Another goal of current CT sizing algorithms is to calculate a certain degree of over-sizing of the transcatheter heart valve (THV) to avoid the post-implantation occurrence of paravalvular leaks (PVLs). It depends on the type of THV and the measurement used (perimeter/area). Self-expandable devices need more oversizing than balloon-expandable prostheses. Conversely, severe oversizing using balloon-expandable devices might increase the annular damage. Additionally, a 10 per cent perimeter oversize does not equal a 10 per cent area oversize, but it is closer to 20 per cent [39].
# Conditional Knowledge Extraction Using Contextual Information Enhancement
## 2 Problem Definition  
In the following, we give the formal definition of the problem. We will give the notion of the key concepts, including sentences, conditional phrases, relational triples, and conditional triples. Moreover, we will give the definition of the processes of generating the concepts and their relations.  
**Sentence**: a sentence S is a chronical set of tokens: S = {\(t_{1}\), \(t_{2}\),... \(t_{n}\)}, where \(t_{i}\) is the \(i\)th token in the sentence.  
**Conditional Phrase**: a conditional phrase \(S_{cond}\) is a subsequence of a sentence S, i.e., \(S_{cond}\) = \(t_{i}\),... \(t_{j}\), where \(1\leq i\leq j\leq n\), so that \(S_{cond}\subseteq\) S.  
**A Triple \(t=\{s,p,o\}\)** is 3-ary statement, where \(s\) is the subject, \(p\) is the predicate and \(o\) is the object.  
**A mapping function \(G_{t}\)**: \(S\to T\) gives the correlation between a sentence S and a set of triples T generated from the sentence S.  
**A mapping function \(G_{cond}\)**: \(S\to CP\) extracts all conditional phrases (CP) from the sentence S.  
**A mapping function \(G_{ct}\)**: \(S_{cond}\to T_{cond}\) generates a set of triples as Conditional Triples (\(T_{cond}\)) from a conditional phrase \(S_{cond}\), the set of all Conditional Triples from the sentence S is thus given by \(\Sigma G_{ct}(G_{cond}(S))\), and denoted as a superset of CT: \(\Gamma_{CT}\).  
**A mapping function map**: \((T-\Gamma_{CT})\times\Gamma_{CT}\to T_{cor}\) gives the correspondence (in the form of triple set \(T_{cor}\)) between relational triples (all triples that do not represent conditions) and conditional triples.  
Using the above definition, the entire process of creating conditional knowledge graphs can be defined as a mapping function G for a given sentence S, where \(G=G_{t}(S)\cup map(G_{t}(S)-\Sigma G_{ct}(G_{cond}(S)),\Sigma G_{ct}(G_{cond}(S )))\).  
From the above definition, it is observable that the conditional KG generation process is a generic framework, and the mapping of relational triplets and conditional triplets are orthogonal to the specific labels of the predicates of the relational triplets. In practice, we can model the correlation between conditional triplets and relation triplets with RDF reification. Figure 2 gives the KG of conventional triplets (without capturing the conditions) from the example in Figure 1, and Figure 3 gives the condition-aware KG of the same sentence using reification. In Figure 3, we have used the predicate \"hasTravelSpeed\" to indicate that the vessel has speed, which is different from the meaning of \"should travel at speeds\" in the example in Figure 1.
# Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
## 3 Challenges and Motivation
### Header Overhead of RLNC in LPWAN  
According to the studies on LoRaWAN [4; 32], the available LoRaWAN payload size varies between 51 and 250 bytes with the header of 13 to 28 bytes depending on the data rate settings. Because the header overhead of RLNC is _n log\({}_{2}\) q_ bits, where n is the number of packets and \(q\) is bit size in \(GF(2^{q})\), we cannot implement RLNC directly in a massive IoT network. For example, a relay using \(GF(2^{4})\) to encode 100 packets of 30 bytes in payload size will have a coded packet of 50 bytes header that cannot be supported in some data rate settings considering the packet size regulation. Therefore, we adopt overhead header compression to compensate for packet size limitation in typical LPWAN.  
Figure 3: Uncertainty of wireless transmission.
# Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods
## 5 Experimental Results
### Speed/Acceleration Prediction  
#### 5.1.1 Performance with Different Hyperparameters  
First, we integrated all the training sets in the dataset and adopted a five-fold cross validation (CV) method to find the best combination of hyperparameters. We implemented our model using the pytorch library and trained and tested it on an Nvidia GTX 1080 Ti GPU. Adam optimizer was used to train the models. The hyperparameters we need to consider here are the time step length \(T\) and the hidden state size \(m\) of the LSTM layer. For the proposed model, we set \(T\) as varying among \([5,10,15]\) and \(m\) as varying among \([16,32,64,128]\). The average RMSE results for the 5-fold CV are tabulated in Table 3. First, we describe the effect of the time step length \(T\). It is obvious from Table 3 that the average RMSE results for \(T=10\) and \(T=15\) are notably larger than for the case of \(T=5\). Therefore, for speed prediction, the best choice of time step length is 5. Then, for the case \(T=5\), the average RMSE value is the smallest when the hidden state size \(m\) equals to 32. In deep learning, too few parameters can lead to underfitting problems while too many parameters can lead to overfitting problems. Therefore, we believe that \(m=16\) belongs to underfitting, \(m=64\) and \(m=128\) belong to overfitting. Thus, the optimal combination of hyperparameters for our model for this dataset is \(\{T=5,m=32\}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**SDC (NEDC)**} & \multicolumn{4}{c}{**RDC (Four Road Segments)**} \\ \cline{3-6}  & & **AtoC** & **CtoD** & **DtoE** & **EtoF** \\ \hline SARIMA & 0.014 & 0.06 & 0.06 & 0.12 & 0.35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: MAPE velocity results of SARIMA for the SDC (NEDC) and RDC.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Hyperparameters**} & \multicolumn{2}{c}{**Average RMSE**} \\ \hline \(\mathbf{T}\) & \(m\) & \\ \hline
5 & 16 & 0.4908 \\  & 32 & 0.4880 \\  & 64 & 0.4926 \\  & 128 & 0.4926 \\ \hline
10 & 16 & 0.4988 \\  & 32 & 0.4998 \\  & 64 & 0.5013 \\  & 128 & 0.4982 \\ \hline
15 & 16 & 0.5007 \\  & 32 & 0.5055 \\  & 64 & 0.5025 \\  & 128 & 0.5059 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RMSE results of 5-fold CV.
# Synthesis of Zeolitic Material with High Cation Exchange Capacity from Paper Sludge Ash Using EDTA
## 3 Results and Discussion
As shown in Figure 9, the reaction sequence in the present study is consistent with the results reported by [PERSON] et al. The dissolution, gelation, and crystallization reactions started simultaneously after the ash was introduced. [PERSON] et al. reported that the high affinity of Ca ions for silicate species disturbed the dissolution of Si into the alkali solution [47]. [PERSON] et al. reported that approximately 40% of the Ca content in PSA is present as calcium aluminosilicate hydrate [54]. Without the addition of 1 M EDTA Ca hydrate dissolves into the alkali solution, and the Al content is higher than that of Si because of the Ca interference observed during zeolitization (Figure 9a). The Si, Al, and Ca contents in PSA dissolve into the solution and precipitate as a Si-Al-Ca gel because of the strong reaction between Ca and Si, forming aluminosilicate gels with lower Si/Al ratios in the solid, and the unreacted Si and Al contents remain in the liquid. Figure 1:3 Relative crystallinities of the product phases in the product, and CECs of the product during synthesis at 80 \({}^{\circ}\)C: (a) without 1 M EDTA and (b) with 1 M EDTA (NaOH concentration: 3 M NaOH). Figure 1:4 Concentrations of Si, Al, and Ca in the solution during synthesis at 80 \({}^{\circ}\)C: (**a**) without 1 M EDTA and (**b**) with 1 M EDTA (NaOH concentration: 3 M NaOH). During the crystallization process at 80 \({}^{\circ}\)C, the Si-Al-Ca gel did not react, and the aluminosilicate gels with lower Si/Al ratios reacted with Si in the liquid forming zeolite phases. Therefore, the product synthesized at 80 \({}^{\circ}\)C had low hydroxysdalite and zeolite-P contents. With the addition of 1 M EDTA, the Ca hydrate dissolved into the alkali solution, and the concentration of the Al content was almost the same as the Si content because Ca capture by EDTA inhibited Ca interference during zeolitization (Figure 9b). The Si, Al, and Ca contents in PSA dissolved into the solution; the Ca content was masked by EDTA, the aluminosilicate gels with higher Si/Al ratios mainly precipitated, Si and Al remained unreacted, and the Ca content was captured with EDTA in the liquid phase. In the crystallization process, the aluminosilicate gels with higher Si/Al ratios reacted with Si and Al in the liquid phase forming zeolite phases. The interference by Ca content was inhibited by Ca-masking by EDTA, and a high content of the aluminosilicate gels with higher Si/Al ratios were converted into zeolite-A, zeolite-P, and hydroxysdalite.
## 3 Result Analysis and Discussion
### Bit Error Rate Analysis  
This subsection introduces a simulation examination of the proposed Flip-F-OFDM error rate compared to HSF Flip-OFDM, ACO-OFDM, DCO-OFDM, JM-DCO-F-OFDM,and conventional Flip-F-OFDM. This study considers AWGN to be the communication channel as in [43], and the simulation was conducted in a MATLAB R2023a environment utilizing a 1024-point IFFT for various M-QAM schemes [38; 39; 43]. Filter design is vital in the F-OFDM technique to minimize out-of-band emissions. The spectrum can be more efficiently utilized by keeping the guard interval between sub-bands minimal [38; 39]. Table 2 provides the detailed simulation parameters employed in this study. Figure 7 assesses the BER efficacy of the aimed Flip-F-OFDM, HSF Flip-OFDM, ACO-OFDM, JM-DCO-F-OFDM, and DCO-OFDM for 4 QAM, 16 QAM, 64 QAM, and 256 QAM, respectively.  
The imitation results indicate that the suggested technique surpasses the previously mentioned techniques in all utilized QAM orders. This improvement is attributed to the employed filter's windowing technique, which ensures satisfactory temporal localization within the truncated filter's response. Thus, the F-OFDM signal maintains an acceptable inter-symbol interference (ISI) limit. Similarly, regarding JM-DCO-F-OFDM, the proposed scheme provides a better result because no DC bias effect is inserted into the signal.  
Additionally, per the mathematical analysis in [37; 40], ACO-OFDM exhibits the same bit error rate performance as HSF Flip-OFDM for the different QAM constellations. As illustrated in Figure 7A-D, the simulation outcomes fully agree with the analytical findings for the given QAM constellations. Figure 8, on the other hand, illustrates the bit error rate comparison between the suggested Flip F-OFDM and classical Flip F-OFDM. According to  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline IFFT/FFT size & 1024 \\ No. of F-OFDM symbols & 1000 \\ No. of subcarriers & 600 \\ Filter type & Raised cosine \\ Modulation technique & QAM \\ Cyclic prefix & 72 \\ Constellation order & 4, 16, 64, 256 \\ Channel model & AWGN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 7: Evaluation of BER among the proposed Flip\(-\)\(-\)\(-\)OFDM, HSF\(-\)Flip\(-\)OFDM, ACO\(-\)OFDM, JM\(-\)DCO\(-\)F\(-\)OFDM, and DCO\(-\)OFDM: **(A)** 4 QAM, **(B)** 16 QAM, **(C)** 64 QAM, and **(D)** 256 QAM.  
the simulation results, the proposed and conventional F-OFDM techniques exhibit similar BER performance, consistent with the mathematical conclusions.