esa-sceva/satcom-chunk-collection huggingface dataset:
# Bi-Directional Point Flow Estimation with Multi-Scale Attention for Deformable Lung CT Registration
## 4 Experiments
### The Experimental Results  
We evaluate our method on the COPD test set and compare it with several point-based baselines, including optimization-based [20] and learning-based approaches [17]. As shown in Table 2, our method achieves the best performance across all evaluation metrics, including the mean TRE and its 25 th, 50 th, and 75 th percentiles. Notably, compared to the state-of-the-art method, e.g., PPWC [17], our model reduces the mean TRE from 2.85 mm to 2.24 mm and achieves a lower median (50%) TRE of 1.79 mm versus 2.33 mm. Additionally, our method achieves a DSC (due to the lack of publicly reported evaluations for other baselines, we only report our DSC value) of 91.4%, indicating a high degree of anatomical overlap after registration.  
In addition to the quantitative gains, the qualitative results (Figure 3) demonstrate that our method produces smoother and more anatomically consistent deformations compared to those with PPWC [17]. Notably, our model preserves the alignment in both the vascular and boundary regions better, capturing complex respiratory motion more effectively.  
\begin{table}
\begin{tabular}{l|c c c c|c} \hline \hline
**Method** & **TRE \(\downarrow\)** & **25\%** & **50\%** & **75\%** & **DSC \(\uparrow\)** \\ \hline initial & 16.25 & 10.14 & 15.94 & 21.76 & 75.8 \\ \hline VoxelMorphw/o IO[33] & 6.53 & 3.38 & 5.82 & 8.50 & - \\ VoxelMorph++w/o IO[48] & 4.47 & 2.41 & 3.74 & 5.69 & - \\ CPD [20] & 3.13 & 1.51 & 2.28 & 3.58 & - \\ CPD w/ labels [20] & 2.59 & 1.36 & 2.01 & 3.16 & - \\ PPWC sup[17] & 2.85 & 1.52 & 2.33 & 3.54 & - \\ PPWC syn[17] & 2.73 & 1.52 & 2.28 & 3.45 & - \\ Ours & 2.24 & 1.16 & 1.79 & 2.77 & 91.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative results on the COPD test cases of point-based methods, reported as the mean TRE with 25/50/75% percentiles (in mm) and the DSC (in %). A lower TRE and a higher DSC indicate a better performance. The performance metrics for the other methods are sourced from the Lung250M-4B [16] paper.
## 8 Technical Challenges for 6G Development
### Terahertz Frequency
#### 8.1.1 Significant Transmission and Absorption by the Atmosphere at Terahertz Frequencies  
Although terahertz frequencies provide substantial data transmission speeds, they encounter notable difficulties in maintaining these rates over extended distances, primarily because of considerable propagation losses and the nature of atmospheric absorption at these frequencies [269]. A redesigned transceiver architecture is necessary for THz communication systems, ensuring operation at high frequencies and maximizing the utilization of the available bandwidths. Addressing challenges such as minimal gain and effective area for distinct THz band antennas is crucial. Additionally, health and safety considerations related to THz band communications require attention.
## 4 Results of the Study
### Numerical Modeling of Uncentered Surfaces  
#### 4.2.1 Different Tunnel Depths in Unreinforced Soil  
In order to further verify the influence of different shield tunnel burial depths on the surface settlement, the original field data are modified to verify the influence of tunnel burial depth on the surface settlement of the shield tunnel under the rest of the working conditions, and the field inspection data are added to compare and analyze them with the numerical simulation data, and the settlement change diagrams are plotted, as shown in Figure 7. The relevant working condition parameters are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Programmes**} & \multicolumn{2}{c}{**Depth of Burial of**} & \multirow{2}{*}{**Radius of the Tunnel (m)**} & \multirow{2}{*}{**Grouting Pressure (kPa)**} & \multirow{2}{*}{**Pressure on Soil Chamber**} \\  & **Tunnels (m)** & & & **(kPa)** \\ \hline Scenario 1 & 11.5 & 3.1 & 152.2 & 146 \\ Scenario 2 & 13 & 3.1 & 152.2 & 146 \\ Scenario 3 & 14.5 & 3.1 & 152.2 & 146 \\ Scenario 4 & 16 & 3.1 & 152.2 & 146 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Shield parameters for different tunnel depths in Unreinforced Soil.  
Figure 6: Cumulative settlement curves of measurement points 1, 2, 3, and 4 of the unreinforced section.  
Figure 7 shows that when the burial depth is within 13 m, the surface settlement gradually increases with the increase of the shield tunnel depth. When the burial depth is more than 13 m, the surface settlement of the shield tunnel decreases with the increase in the surface depth. Combining these four cases, when the tunnel depth is 16 m, the impact on the surface settlement is relatively small, and the trend of the field monitoring data is more consistent, so the numerical simulation can be a better prediction of the trend of the substantive surface settlement.
# Collaborative Indoor Positioning by Localization Comparison at an Encounter Position
## 3 Proposed Method
### Localization Comparison
#### 3.3.2 Feature Extraction from Localization Process  
We extract 15 features expressed in Table 1 from the localization process of the baseline method. Broadly, the features can be grouped into four categories. The features are developed by focusing on a position where a user makes a turn. We detect a turning position \(p_{i}\) from the walking data if Equation (2) is satisfied [36]. The turning position is useful for evaluating the localization quality. For instance, the localization quality is poor if there is a large gap between the turning position and the nearest intersection. Furthermore, disappearing particles are essential features. The presence of disappearing particles means there is a very low possibility that the device will be located at those certain positions, which indicates that the device is very likely passing through an obstacle. Moreover, large variations in the particle parameters indicate that a variety of particles are required to correct the localization. Since the results obtained by PDR are relative positions, errors accumulate as the number of steps increases.  
\[\left|\frac{\theta_{i}+\theta_{i-1}}{2}-\frac{\theta_{i-2}+\theta_{i-3}}{2} \right|>30 \tag{2}\]  
Figure 5: An example of walking data generation. The non-English terms in (**a**) are the store names.
# Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
## 3 Proposed Scheme
### Room Reserving  
#### 3.2.1 Image Partition  
Before the cover image is segmented, the pixel values of the image must be encoded to the points of the elliptic curve. Then, it can be encrypted using EC-EG. The grayscale image has a pixel value between 0 and 255. It is necessary to map the pixel values one by one to the points on the elliptic curve. The image owner divides the cover image into three non-overlapping blocks \(A,B,C\), as shown in Figure 4.
## 4 Experimental Setup
### Evaluation Metrics
* FN: # of defective test pairs that are predicted as undefective. * TN: # of undefective test pairs that are predicted as undefective. Then, we defined the metrics in the evaluation of prediction as follows,  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
\[F\text{-}score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{4}\]  
\[Accuracy=\frac{TP+TN}{TP+FP+FN+TN} \tag{5}\]  
where \(Precision\) is the proportion of returned results that are truly correct; \(Recall\) is the proportion of the truly correct number in all retrieved results in the test set; \(F\text{-}score\) is the trade-off between \(Precision\) and \(Recall\); and \(Accuracy\) is the proportion of the correct number in all results in the whole dataset. **Evaluating the ranking of test cases in test case prioritization**. Suppose the program under test contains \(k\) defects and the set of test cases contains \(n\) test cases. We use the Average Percentage of Fault Detect (APFD) [46] to evaluate the effectiveness of the ranking of test cases generated by each of the ranking models. \[APFD=1-\frac{TF_{1}+TF_{2}+TF_{3}\cdots+TF_{s}}{sn}+\frac{1}{2n} \tag{6}\]  
where \(TF_{k}\) denotes the test case in the ranking of test cases that first covers the \(k\)th defect in the program. That is, a high value of the APFD metric indicates the defects are defective in the early stage.
## 3 Conclusions and Future Directions
Even though there are currently various well-established methods available for chemical analyses of phytocannabinoids, there is still a need for adaptations and enhancement of these methods in the light of new scientific evidence regarding the plant and its plant metabolites, especially taking into account the pharmacological activity and its medical use, association of medical and adverse health effects with potency and/or interplay of certain phytocannabinoids and other active constituents, quality control and stability studies of cannabis and cannabis-based products. Further advancements in phytocannabinoid profiling should move towards untargeted analysis of cannabis plant material and cannabis-based products using orthogonal analytical methods. By employment of cheminformatics approaches for small molecule identification and MSLs, a multitude of new phytocannabinoids and other compounds is expected to be identified in the near future, thus allowing access to complete and accurate phytocannabinoid and terpene profiles. The following are available online. Table S1: GC-based analytical methods for cannabinoid profiling [5, 9, 10, 17, 18, 43, 44, 45, 51, 59, 66, 67, 70, 75, 77, 79, 81, 82, 84, 85, 86, 87, 88, 89, 90, 101, 102, 103, 104, 105, 106, 107, 108, 111, 119, 121, 122, 123, 124, 128, 136, 137, 141, 148, 150, 152, 153, 154, 155, 156, 160, 161, 163, 166, 222, 223, 224]; Table S2: LC-based analytical methods for cannabinoid profiling [11, 19, 27, 40, 42, 50, 55, 60, 65, 66, 68, 69, 78, 80, 83, 92, 100, 107, 109, 111, 133, 134, 142, 144, 149, 165, 173, 176, 177, 178, 180, 182, 183, 185, 186, 190, 191, 200, 215, 217]; Table S3: Vibrational spectroscopy-based analytical methods in conjunction with multivariate data analysis for phytocannabinoid profiling and/or classification of cannabis plant material [21, 189, 203, 204, 205, 211, 212, 218]. Conceptualization, G.S. and S.K. ; methodology, I.C.K. ; investigation, V.S.G. and A.T.; data curation, I.C.K., V.S.G. and A.T.; writing--original draft preparation, I.C.K. and N.G. ; writing--review and editing G.S., M.K. and S.K. ; supervision, G.S. and S.K; project administration, G.S. ; funding acquisition, S.K. and G.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
## 6 Conclusions  
A conventional quasi co-axial TEM waveguiding structure and a newly developed circular TE\({}_{11}\) waveguiding structure sources are presented to compare the plasma characteristics along the waveguiding structure. To compare the source characteristics, experiments were conducted under 500 mTorr Ar plasma condition, where 0.6 kW microwave power was applied. The characteristics of the plasma was diagnosed by a single LP and analyzed by the numerical 3-D model using plasma fluid simulation. Attenuation of the electric field strength along the wave propagation direction and the coupled field profile at the window surface affected the spatial distribution of the electron density significantly. In the TE\({}_{11}\) mode, the attenuation coefficient of the normalized electron density was 4.8 times higher than that of the TEM mode. The field direction affected the attenuation of the electron density along the radial distance, and the electron-density reduction rate along the radial direction was higher in the TEM mode than that in the TE\({}_{11}\) mode. The plasma characteristics of each plasma source were considered by resolving the wave electric field into the radial and axial field components. The influence of the axial electric field component was more dominant in the quasi-coaxial TEM MWP source. The number of electrons produced in the entire volume was 20% higher in the TEM mode than that in the TE\({}_{11}\) mode, but the number of electrons generated in the actual deposition region was 50% higher in the TE\({}_{11}\) mode than that in the TEM mode.  
Conceptualization, J.-H.C. and H.-J.L.; methodology, J.-H.C. and H.-J.L.; software, J.-H.C.; validation, J.-H.C., S.-W.K. and H.-J.L.; formal analysis, J.-H.C. and S.-W.K.; investigation, J.-H.C. and S.-W.K.; resources, J.-H.C.; data curation, J.-H.C.; writing--original draft preparation, J.-H.C.; writing--review and editing, J.-H.C.; visualization, J.-H.C.; supervision, H.-J.L.; project administration, H.-J.L.; funding acquisition, H.-J.L. All authors have read and agreed to the published version of the manuscript. This research was supported by Korea Institute for Advancement of Technology(KIAT) grant funded by the Korea Government(MOTIE) (P0012451, The Competency Development Program for Industry Specialist).  
Not applicable.  
Data variability statementData sharing is not applicable to this article.  
The authors would like to acknowledge the support of Korea Institute for Advancement of Technology(KIAT) grant funded by the Korea Government(MOTIE) (P0012451, The Competency Development Program for Industry Specialist).  
The authors declare no conflict of interest.
# Multimodal Diarization Systems by Training Enrollment Models as Identity Representations +
## 5 Speaker Subsystem
### Audio Processing
#### 5.1.1 Front-End and Speech Activity Detection  
The first block of this subsystem is a front-end to obtain the MFCC features [29]. For a given audio, a stream of 32 coefficient feature vectors is estimated according to a 25 ms window with a 15 ms overlap. No derivatives are considered. Simultaneously, speech activity detection (SAD) labels are estimated each as 10 ms [30]. Our approach for SAD is based on a deep learning solution that is an evolution derived from our previous SAD systems [31]. We use a convolutional recurrent neural network (CRNN) consisting of three blocks of 2D convolutional followed by three BiLSTM layers [32]. Then, the final speech score is obtained through a linear layer. As input features, 64 Mel filter banks and the frame energy are extracted from the raw audio and fed to the neural network. Cepstral mean and variance normalization (CMVN) [33] normalization is applied.  
The CRNN is trained on a combination of different broadcast datasets. Specifically, we include data from the Albayzin 2010 dataset (train and eval), Albayzin 2018 dataset (dev2 and eval), and a selection of data from the first MGB Challenge (train, dev.longitudinal, and task3 eval). Furthermore, audios are augmented with a variety of noises that can be usually found in broadcast emissions.
# Infrared and Visible Image Fusion via Feature-Oriented Dual-Module Complementary
## 3 The Proposed Method
### Spatial Gradient Capture Module
#### 3.1.2 Saliency Operator  
In image processing, constructing saliency maps is beneficial for observing the human visual system and improving fusion performance. Inspired by [PERSON] et al. [25], an image signature descriptor based on the discrete cosine transform (DCT) is applied to obtain the saliency maps. Given an image \(I\), we can approximately isolate the support of the image foreground signal by taking the sign of the DCT of the mixed signal \(I\) in the transformed domain and then computing the inverse DCT back into the spatial domain to obtain the reconstructed image \((\widetilde{I})\). The image signature descriptor (ISD) is defined as  
\[ISD\big{(}\widetilde{I}\big{)}=sign[DCT(I)], \tag{8}\]  
\[\widetilde{I}=IDCT\big{(}ISD\big{(}\widetilde{I}\big{)}\big{)}, \tag{9}\]  
where \(sign(*)\) denotes the entry-wise sign operator.  
Subsequently, the saliency maps (\(SAL_{n}\)) are obtained by smoothing the squared reconstructed images, which greatly overlap with the regions of human overt attentional interest and can be measured as the salient points on the input images. Its definition is given as follows:  
\[SAL_{n}=g*\big{(}\widetilde{I}\circ\widetilde{I}\big{)}. \tag{10}\]  
where \(\circ\) and \(g\) denote the Hadamard product operator and Gaussian blurring used to suppress the noise introduced by the sign quantization, respectively.
# End to End Delay and Energy Consumption in a Two Tier Cluster Hierarchical Wireless Sensor Networks
## 5 The Intra-Cluster Communication
### The Contention Process
\begin{array}{cccccc}A_{0}^{M_{c}}(a)&A_{1}^{M_{c}}(a)&A_ {2}^{M_{c}}(a)&A_{3}^{M_{c}}(a)&\dots&A_{M_{c}}^{M_{c}}(a)\\ 0&A_{0}^{M_{c}-1}(a)&A_{1}^{M_{c}-1}(a)&A_{2}^{M_{c}-1}(a)&\dots&A_{M_{c}-1}^{M_ {c}-1}(a)\\ 0&0&A_{0}^{M_{c}-2}(a)&A_{1}^{M_{c}-2}(a)&\dots&A_{M_{c}-2}^{M_{c}-2}(a)\\ 0&0&0&A_{0}^{M_{c}-3}(a)&\dots&A_{M_{c}-3}^{M_{c}-3}(a)\\ \vdots&\vdots&\vdots&\vdots&\ddots&\vdots\\ 0&0&0&0&\dots&A_{0}^{0}(a)\end{array} \right] \tag{7}\]  
Combining the departure and the arrival processes, respectively Equations (4) and (7) we get,  
\[\mathbf{P}_{C}=\mathbf{D}_{m}(M_{c},r,V)\cdot\mathbf{A}(M_{c},a)= \tag{8}\] \[= \left[ \begin{array}{cccccc}D_{0}^{0}(r,V)&0&0&\dots&0\\ D_{1}^{1}(r,V)&D_{0}^{1}(r,V)&0&\dots&0\\ D_{2}^{2}(r,V)&D_{1}^{2}(r,V)&D_{0}^{2}(r,V)&\dots&0\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ D_{M_{c}}^{M_{c}}(r,V)&D_{M_{c}-1}^{M_{c}}(r,V)&D_{M_{c}-2}^{M_{c}}(r,V)&\dots &D_{0}^{M_{c}}(r,V)\end{array} \right]\cdot\mathbf{A}(M_{c},a)\]  
Observe that, in Equation (8), the elements of matrix \(\mathbf{D}_{m}(M_{c},r,V)\) are the elements of the matrix \(\mathbf{D}(M_{c},r,V)\) defined in Equation (4) but are sorted in a different way. Let \(P_{C;j,j}\) denote the general term of \(P_{C}\); it is given by \(P_{C;i,j}=\sum_{k=\max(0,i-j)}^{i}D_{k}^{i}(r,V)A_{j-i+k}^{M_{c}-i+k}(a)\), and it is explained as follows. At the beginning a Combi-Frame we have \(M_{c}-i\) inactive terminals and \(i\) terminals with a data packet in its unit size buffer ready to compete for access. Let us assume that \(k\) out of \(i\) terminals gain access, this will happen with probability \(D_{k}^{i}(r,V)\), so \(M_{c}-(i-k)\) terminals becomes idle and \(i-k\) remains active. Let us assume that \(j-(i-k)\) out of \(M_{c}-(i-k)\) terminals generate one data packet during the present Combi-Frame, this will happen with probability \(A_{j-i+k}^{M_{c}-i+k}(a)\). Given the independence of the departure and arrival processes \(P_{C;j,i}\) follows. Clearly, the steady state probabilities \(\mathbf{\pi}_{C}=[\pi_{C,0},\pi_{C,1},\dots,\pi_{C,M_{c}}]\) are obtained by solving the system \(\mathbf{\pi}_{C}=\mathbf{\pi}_{C}\mathbf{P}_{C}\) with the normalization condition of \(\mathbf{\pi}_{C}\mathbf{\mathrm{e}}=1\), where \(\mathbf{\mathrm{e}}\) is a column vector with all its elements equal to 1. The subscript \(C\) in the probability denotes the contention process. Then, for our Combi-Frame scenario, the output of the contention process, expressed in terms of its probability generating function (PGF) is,  
\[L(z)=\sum_{k=0}^{V=N_{\mathrm{suc}}C}\pi_{C,k}z^{k} \tag{9}\]  
In Equation (9) \(L(z)\) denotes the local traffic in each cluster. Obviously the following condition \(L^{{}^{\prime}}(z=1)=L^{{}^{\prime}}(1)\leq N_{ms\mathcal{C}}\) is satisfied. Some additional details can be found in [33].
# Noble Metal-Based Heterogeneous Catalysts for Electrochemical Hydrogen Evolution Reaction
## 2 Factors That Affect HER Performance
### Catalytic Process
#### 2.3.3 Geometric Structure Effect  
In addition to the influence of the special crystal surfaces mentioned above, the dimension and density of the material (solid or hollow) also have an influence on the catalytic performance. For solid nanoparticles, only the surface atoms act as the active sites to catalyze. Especially for precious metals, in which the internal atoms cannot be utilized, this results in great waste. According to this, hollow structures such as nanoframes, nanocages, core-shell structures and porous structures can be constructed [35], which are lighter than solid particles and have a higher specific surface area and more efficient utilization of atoms. Therefore, atomic clusters and single-atom catalysts have attracted extensive attention. They have extremely high atomic economic benefits and unique geometric configurations and electronic structures [36; 37], so their catalytic activity is significantly improved compared with the corresponding bulk-phase and solid nanoparticles. However, clusters and single atoms can easily aggregate due to their high surface energy. Therefore, substrates are often introduced to stabilize the clusters and single atoms, such as C, C-N and a metal-organic framework.  
In essence, the fundamental characteristic that affects the catalytic activity is the outer electronic structure of the catalyst. It is the surface layer of the catalyst rather than the body that plays a crucial role in catalysis, so appropriate strategies should be adopted to modulate the surface electronic structure. Many studies have shown that alloying and interface engineering are effective strategies for regulating the surface electronic structure of catalysts, which will be described in detail in Section 4.
## 5 In Situ AE Monitoring During Hydraulic Fracturing in Mines
The solid grey line outlines injection well and the observation boreholes (modified from [43]). All dimensions are given in meter. up to 5 m from the injection point (see Figure 22). Events occurred mostly during the refracturing cycles once a critical injection volume of 0.5 to 1 liter was exceeded and less during the initial fracturing cycle. A comparison of the fracture plane outlined by AE events and stress measurements using an imprint packer and overcoming testing, revealed significant deviations. The imprint packer revealed that fractures initiated at the borehole wall within the foliation plane, but the fracture growth than rotated, as outlined by AE events, in such way that it extends normal to the minimum principal stress. The deviation of the overcoming stress measurement result to the actual fracture plane observed could be explained by using a transversely isotropic elasticity model. The authors conclude that AE monitoring was crucial for the combined interpretation of the stress characterization results and to maintain meaningful stress estimation. We summarize that HF routine monitoring using in situ AE monitoring systems becomes feasible for underground production, if sensitive AE sensors for in situ operation are used. Two upcoming projects performing underground medium-scale HF testing will implement AE monitoring accordingly: the enhanced geothermal system (EGS) Collab project's stimulation experiment in Sanford Underground Research Facility in the former Homestake Gold mine, USA [112] and the STIMTEC stimulation experiment in the underground laboratory in the silver mine \"Reiche Zeche\" in Germany (personal conversation with [PERSON] and [PERSON], 2018) (more details at [[http://stimtec.rub.de](http://stimtec.rub.de)]([http://stimtec.rub.de](http://stimtec.rub.de))). Figure 21: (**a**,**b**) Grimsel test site is located in the Bernese Alps in southern Switzerland (see www.grimselstrom.ch). (**c**) In situ AE monitoring was performed during hydraulic fracturing tests HF1 to HF3 in Borehole SBH3. Positions S1 to S28 mark the location of the AE sensors and accelerometers [88].