esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Simple DPS: Scheme and Algorithms
#### 3.3.1 Topological Retreat  
Two points \(x\) and \(y\) in \(A\) are called \(r\)-connected if there is a chain of \(r\)-close points in \(A\)-\(x_{0},\ldots,x_{n}\) with the starting point \(x_{0}=x\) and terminus \(x_{n}=y\) (\(d(x_{i},x_{i+1})\leq r,i=0,\ldots,n-1\)). The ratio of \(r\)-connectivity is an equivalence splitting the set \(A\) into components of \(r\)-connectivity \(C_{r}A(1),\ldots,C_{r}A(k^{*})\), \(k^{*}=k^{*}(A,r)\):  
\[A=C_{r}A(1)\vee\ldots\lor C_{r}A(k^{*}). \tag{4}\]  
Algorithmically, the split (4) is achieved as follows: let \(a\) point be in \(A\) and \(C_{r}A(a)\) component of \(r\)-connectivity that contains it. Then,  
\[C_{r}A(a)=\cup_{i=1}^{\infty}C_{r}^{i}A(a)\]  
where  
\[\begin{array}{l}C_{r}^{0}A(a)=a\\ C_{r}^{1}A(a)=D_{A}(a,r)\\ \ldots\ldots\ldots\ldots\\ C_{r}^{i+1}A(a)=\cup_{a\in C_{r}^{i}A(a)}D_{A}(\bar{a},r)\\ \ldots\ldots\ldots\end{array}\]  
By virtue of finiteness \(A\) everything is balanced and makes sense. Let us consider \(C_{r}A(a)\) as the first component \(C_{r}A(1)\) in (4). If it is not the last one, the same reasoning applies to \(A_{1}=A\smallsetminus C_{r}A(a)\). As a result, we have the second component \(C_{r}A(2)\) and so on.  
**Statement 6**.: _If the density \(P\) is \(r\)-local, then every \(r\)-link component of the set \(X(\alpha)\) is \(\alpha\)-perfect._  
In the event of \(r\)-local density, we will understand the DPS\((P,\alpha,r)\) algorithm through a broader lens.  
**Definition 6**.: _The process of construction for the finite metric space \((X,d)\) based on the \(r\)-local density \(P\) of the \(\alpha\)-hull \(X(\alpha)\) with its subsequent splitting into \(r\)-connected components is called a \"simple DPS algorithm\":_  
\[\begin{array}{l}DPS=DPS(P,\alpha,r)\to 2^{2^{X}}\\ DPS(X)=\{C_{r}X(\alpha)(1),\ldots,C_{r}X(\alpha)(k^{*})\}\end{array}\]  
Let us summarize our conversation about \(\text{DPS}(P,\alpha,r)\) with its flow charts and comments to it (Figure 4).  
The first stage of DPS intercepts the maximal subset \(X(\alpha)\), dense against the general background, from the initial space \(X\). The second DPS stage splits \(X(\alpha)\) into components \(C_{r}X(\alpha)(k)\). Each component combines density against the background and connectivity, that is, it formally expresses empirical clustering.
## 3 Results
### Study Characteristics
#### 3.3.2 Data Collection Methods  
Data collection methodology showed strong consistency in device selection, with thirteen studies employing the ActiGraph GT3X+ accelerometer (ActiGraph LLC, Pensacola, FL, USA) and one study using the Activatch-2 (Philips, Eindhoven, The Netherlands) [26]. Accelerometer placement varied across studies, with hip placement (\(n\) = 4; [17,20,21; 24]), wrist placement (\(n\) = 3; [23,27; 28]), both locations simultaneously (\(n\) = 5; [7,18; 22,25]), and ankle (\(n\) = 3; [26]). Technical specifications of data collection displayed considerable variation, with sampling frequencies ranging from 0.25 to 100 Hz. Epoch lengths were similarly diverse, spanning from 1 to 60 s, with several studies employing multiple epoch lengths to evaluate optimal measurement intervals.
# Isolation of Circulating Tumor Cells from Glioblastoma Patients by Direct Immunomagnetic Targeting
## 4 Discussion
However, GFAP expression is reportedly often lost in cultured cells, likely via epigenetic silencing [19]; thus, our inability to detect it in cell lines may be an artifact of cell culture. Since GFAP probing was previously used to successfully identify GBM-CTCs by others [8], we retained it as a CTC identification marker and successfully detected GFAP in patient GBM-CTCs. Here, we confirmed heterogeneity for GFAP and GLAST expression in patient CTCs; and showed that probing for both identification markers is significantly superior to only probing for GFAP to identify GBM-CTCs (Figure 4C). Combining GFAP with GLAST for GBM-CTC detection can, therefore, function as an additional validation marker for the GBM origin of these cells, as well as aid in identifying more CTCs overall. The major limitation of our study is the small patient cohort, which makes it premature to try to establish a correlation between GBM-CTC counts and disease outcomes. To date, the overall small investigated patient numbers in all relevant studies that tested for correlations of GBM-CTC counts to sub-type of glioma, histological grade, pre- and post-surgical tumour burden, treatment response, survival or pseudoprogression were challenging [8; 9; 11]. However, the advent of novel techniques such as single-cell methodologies has enabled successful interrogation of even single CTCs for clinical biomarkers, thus detecting even small CTC numbers that may provide a source of biomarker detection [6]. Given the rarity of primary brain cancer CTCs, obtaining larger blood volumes such as 50 mL would be desirable and still remain less invasive than surgical biopsy or lumbar puncture cerebral spinal fluid (CSF) collection. Another, technical limitation of this study is that we based screening for potential immunomagnetic cell surface targets on cultured monolayer cells only. As discussed, cell culture may change protein expression patterns and thus reflect less truly expression of proteins for cancer tissue or, important for this study, CTCs. While including examination of cell surface markers in tissue and potentially cultured cells that grow in spheres may have revealed additional immunomagnetic targets, their true value for CTC isolation may only be shown by direct comparison for CTC isolation from patient samples; and the limitation is the amount of patient blood that can be drawn to allow for multiple comparisons. Here we tested two immunomagnetic isolation target candidates and have shown that GBM CTCs are captured targeting these markers. This could become a benchmark to test other isolation target candidates against in the future.
## 6 Unified Comparison of PIT Methods  
To ensure uniform comparison, the multi-dimensional evaluation framework from reference [45] was used in this section to assess mainstream PIT methods across five critical dimensions: detection resolution, sensor type, cost, ease of use, and potential for AI integration. These results are presented in Table 5.  
AE and TIP exhibit the highest sensitivity but require specialized equipment, whereas SE/IR is more practical for routine screening, despite lower resolution. SE/IR offers the optimal balance for most field applications, whereas vibration methods and AE are only suitable for critical structures, given their high associated costs. SE/IR/AE signals are well-suited for AI integration, whereas TIP thermal data lacks standardized AI-based analytical frameworks. Hybrid methods combining SE and AI exhibit significant potential, increasing defect detection rates by 15-40% relative to traditional interpretive methods.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**PIT Method** & **Detection Resolution** & **Sensor Types** & **Cost** & **Ease of Implementation** & **AI Integration Potential** \\ \hline SE & Moderate & Accelerometers, velocity sensors & Low & Simple setup, complex interpretation & Very high: Structured ID/2D signals and massive data support feature-based ML \\ \hline IR & High & Force sensors, accelerometers & Medium & Moderate: Limited data and noisy signals restrict application to feature-based ML \\ \hline Vibration Wave Method & Similar to SE & Vibration sensors, vibrators & Medium & Complex excitation setup & Emerging IRF curves from deconvolution could support hybrid methods \\ \hline AE & High & Acoustic emission sensors & High & Susceptible to environmental noise; requires pre-installed sensor arrays & Moderate: Non-stationary signals sentor arrays & Not DL, but data labeling is challenging \\ \hline TIP & Traditional: \(\geq\)10\% cross-section; Distributed fiber: 4\% & Discrete temperature sensors & Medium-high sensors & Traditional has data blind spots: fiber optic requires careful deployment & Limited: Sparse data and ambiguous temperature anomaly-defect correlations limit AI application to preliminary feature extraction \\ \hline \hline \end{tabular}
\end{table}
Table 5: Unified comparison of PIT methods.
## 5 Case Study: An Actual Deployment at the City Campus of the University of Auckland
### Model Fine-Tuning  
To enhance the accuracy of the urban sound classification model within the specific acoustic environment of the campus, we fine-tuned the pre-trained CCBA model using data collected from our deployment. We deployed the system for an hour on campus to gather a dataset for fine-tuning, which we manually checked and labeled into three classes: bus and truck, car, and pedestrian traffic light. The audio lengths for these classes were 10 min and 5 s, 11 min and 16 s, and 10 min and 37 s, respectively, totaling around 32 min. Figure 11 illustrates the first five seconds of each class. Since we only trained the final fully connected (FC) layer while keeping the rest of the CCBA model unchanged, we standardized the collected dataset to align it as closely as possible with the pre-training dataset. Specifically, we subtracted the mean value of the original UrbanSound8K training set from our data and then divided it by the original standard deviation. This approach ensured that our dataset was on the same scale as the original training data, facilitating more effective model fine-tuning.  
The dataset was split into training, validation, and testing sets in a ratio of 8:1:1 to fine-tune the final layer of the pre-trained CCBA model and assess its performance. For training, we employed a batch size of 16, an initial learning rate of \(1\times 10^{-2}\), and the Adam optimizer with a weight decay of \(1\times 10^{-4}\). As depicted in Figure 12, the confusion matrix of the fine-tuned CCBA shows that the Bus and truck sound (label 0) has the lowest classification accuracy at 83%, while the Pedestrian traffic light sound achieves the highest accuracy at 97%. A notable 17% of bus and truck sounds were misclassified as car sounds, reflecting occasional confusion between large and small vehicles. Despite this, the overall accuracy of the fine-tuned CCBA on the test set reached 91.05%, which is commendable given the limited dataset size. By leveraging transfer learning, the model effectively adapted pre-trained features to the specific acoustic environment, enabling the CCBA to achieve 91.05% accuracy with only around 30 min of collected data. This demonstrates the model's robust adaptability and indicates that minimal additional training is required when deploying CCBA in new environments. The approach significantly reduces the time and resources needed for fine-tuning, highlighting its potential for efficient implementation across diverse acoustic settings with limited data.  
Figure 11: Five-second plots of collected audio samples for fine-tuning: (**a**) Bus and truck; (**b**) Car; (**c**) Pedestrian traffic light.
# Analyzing and Controlling Inter-Head Diversity in Multi-Head Attention
## 5 Inter-Head Similarity Analysis
### Does Multi-Head Strategy Diversify a Model's Representation Subspaces?  
Table 3 shows inter-head similarity using SVCCA and CKA of each model. Both inter-head similarity measures using SVCCA and CKA show a persistent tendency that the inter-head similarity of each model decreases as the number of heads increases. On the other hand, we observe that increasing hidden dimension size \(d\) does not meaningfully affect the inter-head similarity with a fixed number of heads.  
In addition to Table 3, we plot SVCCA coefficient curves of inter-head similarity in Figure 3. With various number of heads \(H=\{2,4,8,16\}\) and fixed \(dim/head=\{32,64\}\) ((a) and (b) in Figure 3), we observe that increasing number of heads make SVCCA coefficients smaller, indicating that inter-head diversity also increases. We also observe the same tendency with fixed \(dim\) ((c) in Figure 3), while we cannot find any consistency of inter-head similarity with fixed number of heads ((d) in Figure 3). Besides, we observed an interesting feature of SVCCA similarity curves that well-trained models have steep slopes on top-few SVCCA coefficients. We later discuss the steepness of top-few SVCCA coefficients in Section 6. Our analysis of inter-head similarity measured by SVCCA and CKA statistically support the hypothesis that multi-head attention diversifies deep representations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{_Modules_} & \multicolumn{5}{c}{_Number of Heads H_} \\ \cline{2-6}  & **1** & **2** & **4** & **8** & **16** \\ \hline _enc self_ & 0.448 & 0.407 & 0.374 & 0.272 & 0.202 \\ _dec self_ & 0.474 & 0.4 & 0.353 & 0.328 & 0.237 \\ _enc-dec_ & 0.429 & 0.378 & 0.319 & 0.289 & 0.189 \\ \hline \hline \end{tabular}
\end{table}
Table 2: SVCCA similarities versus a single headed model.  
Figure 3: SVCCA coefficient curves of inter-head similarity.
# Recent Advances in the Targeting of Head and Neck Cancer Stem Cells
## 2 Cancer Stem Cells
### Stemness-Associated Signaling Pathways
#### 2.2.2 Notch Signaling Pathway  
The Wnt signaling pathway crosses paths with other signaling pathways to further enhance its impact on HNSCC. Another important element in the pathophysiology of HNSCC is the Notch signaling system. Notch receptors (Notch 1-4) and their ligands (Jagged and Delta-like) have been shown to play crucial roles in the differentiation, proliferation, and determination of cell fate. Targeted gene expression is regulated by the Notch intracellular domain (NICD) which translocates to the nucleus after the cleavage during the activation of Notch signaling [18]. Dysregulation of the Notch signaling pathway is common in HNSCC, and mutations in Notch receptors and ligands are frequently observed. NOTCH is one of the most frequently altered genes in squamous cell carcinoma, with incattivating mutations detected in about 10% of all cases, including those involving the oral cavity [19]. Aberrant Notch signaling can promote tumor cell survival, epithelial-mesenchymal transition (EMT), and immune evasion [19]. Furthermore, Notch signaling interacts with other signaling pathways such as Wnt and Sonic-Hedgehog (SHh) to form a complex network of molecular events that drive HNSCC progression.
# A Dual-Branch Speech Enhancement Model with Harmonic Repair
## 4 Conclusions  
In this paper, we proposed HRLF-Net, a dual-branch speech-enhanced network. The harmonic repair module used in the model significantly restores the harmonic distribution of the speech. In the subsequent imaginary and real dual-branch structure, the FFC module plays a key role in expanding the receptive field of the model, while the dilated DenseNet effectively overcomes the phase wrapping and significantly improves the comprehensibility of the speech. The effectiveness and necessity of each module in the model are validated through ablation experiments. The proposed network shows a significant improvement in the PESQ metric compared to other state-of-the-art models, and it maintains a high level of short-term speech intelligibility.  
Furthermore, during the experiments, we observed significant variations in the dynamic range of speech signals due to differences in speakers, contexts, and recording  
Figure 6: Comparison of spectrograms.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **PESQ** & **STOI (\%)** & **SI-SDR** & **CSIG** & **\#Params. (M)** \\ \hline
2020 DEMUCS [10] & 3.03 & 87.0 * & 18.5 * & 4.36 & 60.8 \\
2022 HiFi++ [35] & 2.76 & 89.53 * & 18.4 * & 4.09 & 1.7 \\
2022 CMGAN [36] & 3.41 & 96.0 * & 16.34 * & **4.63** & 1.83 \\ Ours **HRLF-Net** & **3.519** & **96.78 * & **18.67 * & 4.23 & 13.105 \\ \hline \hline \end{tabular}  
* indicates that the corresponding result is not provided in the original paper, and the value in this table is obtained through our experiments.  
\end{table}
Table 5: Comparison with other models on the VoiceBank + DEMAND dataset.  
conditions. Similarly, background noise ranges from low-intensity environmental noise to high-intensity interference signals such as traffic or industrial noise. Therefore, to further enhance the speech enhancement system's ability to handle dynamic range, we will explore additional data-driven approaches in the future. Also, we plan to extend our work to real-time applications by achieving even lower latency levels.  
L.J.: Conceptualization of this study, Methodology, Software, Writing. Y.X.: Supervision. D.K.: Writing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are openly available.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Determining the AMSR-E SST Footprint from Co-Located MODIS SSTs
## Data
### Modis  
MODIS was also carried on the Aqua satellite. This infrared instrument had several spectral channels in the thermal infrared allowing for high quality, high resolution SST retrievals. The L2 MODIS SST product used here was derived from the far-infrared bands, which had a resolution of approximately 1 km at nadir. For this study, we used the MODIS SST product obtained from the NASA Ocean Biology Processing Group (OBPG) [14]. This product is based on the nonlinear SST (NLSST) algorithm [15; 16]. Various factors contribute to errors in the retrievals, with atmospheric water vapor and/or clouds thought to be the primary contributors to statistical measures of the uncertainty of L2 products [17]. (For simplicity, we refer to contamination from either of these sources as 'cloud contamination'.) Because of the impact of clouds on SST values retrieved from IR measurements, significant effort is devoted to flagging the presence of suspect, generally cloud contaminated, pixels. Specifically, a quality level from zero to four was assigned to each MODIS SST pixel. The higher the quality level, the poorer the quality of the pixel. In this study, we used only quality level 0 data; i.e., the 'best' quality retrievals. However, some contaminated pixels were still flagged as quality 0; i.e., a type II misclassification error based on the statistical hypothesis that the pixel is cloud free. Type I  
Figure 1: The scan geometry of the Advanced Microwave Scanning Radiometer-Earth observing satellite (EOS) (AMSR-E). The ellipses schematically illustrate the AMSR-E footprint.  
errors, clear pixels being flagged as 'bad', occurred as well but these only had a marginal impact on the uncertainty of the product.
# Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
## 2 From State-of-the-Art SDR Quality Assessment Metrics to HDR/WCG Quality Assessment Metrics
### Adapting SDR Metrics to HDR/WCG Images
#### 2.4.1 Color Space Conversion  
Most SDR metrics were designed with the assumption that the images are encoded in the legacy \(Y^{\prime}Cr^{\prime}Cb^{\prime}\)[1] color space; this color space is approximately perceptually uniform for SDR content.  
To use SDR metrics with HDR images, we propose to leverage perceptually uniform color spaces adapted to HDR and WCG images (\(ICtCp\), \(J_{2}a_{2}b_{2}\), \(HDR\)-\(Lab_{100}\) and \(HDR\)-\(Lab_{1000}\)).  
To illustrate the importance of using uniform color space, we also consider two non-uniform color spaces, namely \(XYZ\) and \(Y^{\prime}Cr^{\prime}Cb^{\prime}\) color spaces as defined in the BT.2020 recommendation [5]. This last space can't be considered as approximantly uniform for HDR content as it uses the classical gamma function. This function is applied to the \(RGB\) component of an image:  
\[E^{\prime}=\left\{\begin{array}{ccc}4.5E&\text{if}&0\leq E\leq\beta\\ \alpha E^{0.45}-(\alpha-1)&\text{otherwise}\end{array}\right. \tag{21}\]  
where \(\alpha=1.099\), \(\beta=0.018\) and \(E\) is one of the \(R\), \(G\) and \(B\) channel normalized by the reference white level. In SDR, it is supposed to be equal to the peak brightness of the display, so we choose as being the maximum value taken by our own HDR images: 4250 cd/m\({}^{2}\).  
From the non-linear \(R^{\prime}B^{\prime}G^{\prime}\) color space, the \(Y^{\prime}Cr^{\prime}Cb^{\prime}\) color space can be easily deduced:  
\[\left\{\begin{array}{rcl}Y^{\prime}&=&0.2627\times R^{\prime}+0.6780\times G ^{\prime}+0.0593\times B^{\prime}\\ Cr^{\prime}&=&(R^{\prime}-Y^{\prime})/1.4746\\ Cb^{\prime}&=&(B^{\prime}-Y^{\prime})/1.8814\end{array}\right. \tag{22}\]  
In addition to the previous color space, for the color-blind metrics, we use the PU-mapping function for the luminance [7]. As mentioned earlier, this transfer function keeps the same behaviour than the \(Y^{\prime}Cr^{\prime}Cb^{\prime}\) with a reference white of 80 cd/m\({}^{2}\) (which is perceptually linear within a SDR range) and retains perceptual linearity above. Thus any color-blind metrics can be used thanks to this mapping.
# Optimal Base Station Density of Dense Network:
## 3 The Expected Link Rate of the SmBS Tier
### Probability of a User Connecting to the SmBS Tier  
Since a user chooses a BS to connect depending on the strongest received power, the condition that a user is associated with the SmBS tier is \(BP_{\rm m}R_{\rm m}^{-\alpha}>P_{\rm M}R_{\rm M}^{-\alpha}\), where \(B\) is the bias of the SmBS tier and \(R_{\rm M}\) is the distance from the user to the nearest MaBS. Thus, the probability that a user is associated with the SmBS tier can be obtained as follows.  
**Lemma 2**.: _A user connected to the SmBS tier with the probability \(A_{\rm m}\) which is_  
\[A_{\rm m}=\frac{\lambda_{\rm m}}{\hat{C}\lambda_{\rm M}+\lambda_{\rm m}}, \tag{5}\]  
_where \(\hat{C}=\left(\frac{P_{\rm M}}{BP_{\rm m}}\right)^{\frac{2}{\alpha}}\)._  
Proof of Lemma 2.: See Appendix A.  
Since the fairness of resource allocation is not the main topic of this paper, we assume \(B=1\) unless explicitly stated. \(B=1\) means that users have no preference to select any certain tier. We will discuss the problem of fairness in Section 4.4, where \(B\) can take other values.  
Equation (5) indicates that SmBS density and the transmit power of SmBS have the influence on network association. Users are more likely associated with the SmBS tier when SmBS network gets denser and its transmit power becomes stronger.
## 3 Experiment Setup
Figure 3: Example of different turbulence levels from the free-space data set. Column header indicates the pattern number and the row label indicates the level of turbulence. Inspecting the different levels for the OAM modes shows pattern displacement or distortion due to the turbulence. Each free-space and underwater data set is divided into training, validation, and test sets at a 70%/15%/15% respective split. The test sets are used only after a classifier is fully trained and its final performance metrics are gathered. The test set is often referred to as a holdout set, as it is put aside until the very end. Training took place on a computer with a NVIDIA RTX 2080 GPU with 8 GB of RAM. The computer also has 32 GB RAM and an Intel i9 processor with 16 cores. For the CNN training and evaluation in this paper, the code is developed in Python and the ML library used is PyTorch. Table 1 provides the total number of trainable parameters for the architectures used in this study, as reported by PyTorch. \begin{table}
\begin{tabular}{c c} \hline \hline
**CNN** & **Parameter Count** \\ \hline ShuffleNet & 374,592 \\ SqueezeNet & 751,840 \\ MobileNet & 2,264,864 \\ DenseNet & 6,982,048 \\ ResNeXt & 2,3045,472 \\ ResNet & 23,573,600 \\ AlexNet & 57,134,944 \\ WideResNet & 66,899,808 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: CNN Architecture Trainable Parameter Count. Figure 5: Example of OAM patterns from the underwater data set.