esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Model Results  
The model achieved initial accuracy values using the Hybrid-ResNet algorithm of 7% on the five randomly initialized clusters, 70% on the Binary-Matrix Clustered clusters, and 72% on the k-means Initialized 5 Clusters.  
Model was then run until 90% threshold and was able to achieve 92.3% in 9 cycles. All 3 methods were able to achieve 90% accuracy using this re-clustered method within this preset.  
\begin{table}
\begin{tabular}{c c} \hline
**Outcome Linked Genetic Mutation** & **Total Subjects** \\ \hline TP53 & 187 \\ KRAS & 157 \\ KEAP1 & 148 \\ CDKN2 & 101 \\ EGFR & 93 \\ KDR & 89 \\ NTRK & 86 \\ STK1 & 71 \\ ROS1 & 69 \\ SMARCA & 65 \\ ALK & 60 \\ BRAF & 53 \\ \hline \end{tabular}
\end{table}
Table 2: Overview of top 12 genes with mutations present across a large majority of patients present in the data and with linkage to cancer severity or treatment. These top 12 genes were the genes with the largest numbers of subjects present within the data and relevant tumor mutations as identified by the analysis performed by the TCGA cohorts through sequencing of the primary tumor and genetic analysis.  
The initial 5 clusters became 4 clusters after this process. The breakdown of genetic groupings and total patients is summarized in Figure 4.  
To account for any potential biases in the data itself, the clinical data were also analyzed to account for any potential differences. (Table 3) Overall these results appear to show that the two datasets consisted of relatively similar patient populations, with one having disproportionately more men than the other.  
Figure 4: Total genetic groupings and patients present within each cluster.  
\begin{table}
\begin{tabular}{c c c} \hline  & **LUAD** & **LUSC** \\ \hline Average Age & 65.33 & 67.25 \\ \hline
**Race** & & \\ \hline Not reported & 3\% & 0\% \\ Hispanic or Latino & 1\% & 2\% \\ Not Hispanic or Latino & 75\% & 65\% \\ Not reported & 20\% & 31\% \\ Unknown & 1\% & 1\% \\ \hline Gender & & \\ \hline Female & 54\% & 24\% \\ Male & 44\% & 76\% \\ \hline \end{tabular}
\end{table}
Table 3: Overview of differences present within the clinical data for the two datasets used in this study. Average age and percentage present of each demographic, gender, race, and stage is reported to provide a comparison.
# The Algorithms of Image Processing and Analysis in the Textile Fabrics Abrasion Assessment
###### Abstract  
The abrasion resistance and susceptibility of textiles to surface damage is an important issue for their utility properties. The currently used test method for abrasion resistance assessment has been based on criteria that define the breakage point of fabrics, visually identified and highly depended on observer perception. Taking under consideration the character of abrasion assessment process the efforts were made to elaborate the new alternative test technique which supports this evaluation. The abrasion resistance tests using instrumental method (based on image analysis tools) parallel with standard method were carried out. There were two steps of analysis. Firstly, the preliminary analysis for selecting the most effective and sensitive algorithm was carried out. Secondly, the final analysis, involving whole range of captured image samples, using chosen algorithm, based on image histogram calculation was done. Summarizing the abrasion resistance tests results obtained using both standard and instrumental methods it was found that the instrumental method gave more complex results during identification of surface changes caused directly by the abrasion process. The instrumental method is more sensitive to surface texture modifications and colour fade and its discrimination threshold is significantly lower than standard methods with qualitative breakage criteria (such as broken threads, loss of pile).  
textile abrasion; image analysis method; assessment of fabric surface +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### NDVI Trends
#### 3.1.3 Spatial Pattern of NDVI Trends
In general, the growth of vegetation and its seasonal changes in the study area increased from 1982-2011, while areas with minimum apparent vegetation cover changed only marginally. Violin plots [68], a combination of box plot and histogram were used to illustrate the different trends over different vegetation regions (Figure 4). Analysis of variance shows that the impacts of vegetation region on the trends in annual average, minimum and standard deviation of NDVI are significant (\(p<0.001\)). Post hoc comparison shows that the annual mean NDVI over vegetation regions of RF, EBF and DBF had the most significant positive trends, followed by PAV and TD. Conversely, the annual mean NDVI of MF and DCF had the most significant decreasing trends. In addition, the annual minimum NDVI had the most significant positive and negative trends in RF and DCF. The trends of annual standard deviation of NDVI for all vegetation regions were significantly positive, except for RF. Grassland degradation in the Tibetan Plateau reported by [PERSON] _et al._[69] and [PERSON] _et al._[70] was not evident in this study mainly because the Tibetan Plateau was sparsely vegetated and had low NDVI values (\(<\)0.3). It is most difficult for linear trends in NDVI time series with low values to reach a significance level because of potential noise. Moreover, most of the severely degraded grassland was observed in the headwater areas of the Yangtze and Yellow Rivers [70], which was a local or small scale phenomenon and beyond the capacity of AVHRR based vegetation data set (\(\sim\)64 km per pixel). This is also a limitation of our research. Figure 4: Violin plots of trends of annual average/minimum/standard deviation of NDVI at each pixel in each vegetation region. The black dots indicate the mean values; the short-segments indicate the interquartile range, as in standard box plots. Overlaid on them are kernel density estimations. Grouping results of Games-Howell post hoc pairwise comparison analyses are labeled with letters on the left. Regions with an insufficient number of pixels (\(<\)100) with significant trends were excluded from post hoc tests. Vegetation regions with the same letter have insignificant differences in trends. Average trends from positive to negative are labeled in alphabetical order.
# Cooperative Caching and Resource Allocation in Integrated Satellite-Terrestrial Networks
## 3 System Model
### Users and Contents  
The set of users is indicated as \(U\) with \(M\) users, and all users are randomly located in the coverage of satellite network \(G(V,E)\) and can only communicate with their access satellites directly. Satellite \(v\) for user \(u\) will be indicated as the access satellite \(v_{u,a}\) when user \(u\) accesses the network by satellite \(v\). The link bandwidth resources between user \(u\) and access satellite \(v_{u,a}\) are denoted as \(bw_{u}\). For the satellite-terrestrial link between user \(u\) and access satellite \(v_{u,a}\), the transmission power, the channel gain, and the noise power can be indicated as \(p_{u,tr}\), \(S_{u,c}\), and \(p_{u,n}\), and the distance is indicated as \(k_{u}\). The velocity of data transmission is also indicated as \(c\) in the wireless communication environment.  
Furthermore, the set of available content is denoted as \(W\), and the number is also indicated as \(Q\). For each content \(w\), \(w\in W\), the data size and the content popularity are indicated as \(s_{w}\) and \(p_{w}\). Note that the greater the content popularity is, the higher the access opportunity for terminal users will be. In addition, the maximum service delay of retrieving content \(w\) for user \(u\) is indicated as \(t_{u,w}^{max}\).
# First Assessment of GF3-02 SAR Ocean Wind Retrieval
## 3 Results
### GF3-02 SAR Wind Speed from HH Polarization
#### 3.2.2 Compared with ERA5 Wind Speed  
Figure 6 shows a comparison of the wind speeds obtained by applying the above model to the GF3-02 SAR HH polarization data and the ERA5 reanalysis wind speeds. Table 4 shows the retrieval results of the wind speed of the GF3-02 HH Polarization data based on each GMF model. The RMSE of the retrieved wind speeds are 1.51 m/s, 1.41 m/s, 1.90 m/s, 1.38 m/s, and 1.19 m/s, respectively. It can be clearly seen that the RMSE of CMODH_HH is the smallest, the deviation is \(-\)0.40 m/s, and the correlation coefficient is 0.85. Furthermore, compared with the CMOD5.N for VV polarization, the retrieval accuracy of the HH polarization wind speed using the PR model combined with the CMOD5.N model is poor because there is a certain error in the PR model.
# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 2 Review of Fake News Detection Approaches and Multimodal Learning Methods  
### Content-Based Fake News Detection  
Content-based approaches primarily identify false information by analyzing intrinsic characteristics of news articles, including linguistic patterns, semantic inconsistencies, and visual manipulations. Early research relied on handcrafted features such as lexical diversity, syntactic patterns, readability metrics, and writing style analysis [25; 26; 27]. [PERSON] et al. [25] proposed a classification method based on linguistic features, identifying fake news through lexical complexity and syntactic structure analysis. [PERSON] et al. [26] further extended this approach by constructing comprehensive feature sets incorporating psycholinguistic and rhetorical characteristics. While these traditional methods provided baseline detection capabilities, they often failed to capture sophisticated manipulation techniques employed in modern fake news.  
With the advancement of deep learning technologies, neural network approaches have demonstrated superior performance in capturing complex semantic patterns. [PERSON] et al. [28] proposed a CNN-based text classification method that effectively extracted local textual features. [PERSON] et al. [8] employed Long Short-Term Memory (LSTM) networks to handle temporal dependencies in sequential data, achieving significant improvements in fake news detection tasks. [PERSON] et al. [16] developed MVAE (Multimodal Variational Autoencoder) that learns joint representations of text and images, enabling the model to capture latent correlations between different modalities.  
Recent multimodal approaches have explored various fusion strategies to integrate textual and visual information. [PERSON] et al. [15] proposed EANN (Event Adversarial Neural Network) that combines adversarial training with multimodal feature extraction, significantly improving generalization capabilities across different domains and events. [PERSON] et al. [29] designed multimodal fusion networks that dynamically balance the importance of different modalities through attention mechanisms. However, these methods often fail to capture subtle inconsistencies characteristic of complex fake news and lack the ability to leverage external validation signals from user communities.
## 3 Probabilistic Forecasting of ALSC
### The Repeated Power Flow Considering Multi-Slack Buses
Given a system with \(N\) buses, multi-slack bus set \(B\), traditional generator bus set \(T\) (\(B\)\(\in\)\(T\)), and renewable energy generator bus set \(N\), the first part of the power imbalance \(\Delta P_{1}\) is \[\Delta P_{1}=\sum\limits_{i\in R}P_{Gi}+\sum\limits_{i\in T}P_{Gi}-\sum \limits_{Di}-P_{loss}\] (24) where \(\sum\limits_{i\in R}P_{Gi}\) represents the power of all traditional generators and \(\sum\limits_{i\in T}P_{Gi}\) represents the power of all renewable energy generators. 3. According to the ratio of remaining capacity of each generator, determine the allocation ratio \(k_{1,i}\) of the power imbalance \(\Delta P_{1}\) amongst slack buses as \[k_{1,i}=\frac{P_{r,i}}{\sum P_{r,i}},i\in\mathbf{B}\] (25) where \(P_{r,i}\) is the remaining power of the generator at bus \(i\). 4. Change the active power equation of the slack buses into \[P_{Gi}-k_{1,i}\Delta P_{1}-P_{Di}-U_{i}\sum\limits_{j=1}^{N}U_{j}(G_{ij}\cos \delta_{ij}+B_{ij}\sin\delta_{ij})=0,i\in\mathbf{B}\] (26) then perform the power flow calculation. 5. Make an increase of the load, \(S_{Li}=S_{Li}+hb_{i}\) and the second part of power imbalance \(\Delta P_{2}\) is \[\Delta P_{2}=\sum\limits_{i}^{N_{l}}hb_{i}\] (27) where \(N_{l}\) is the number of loads and \(b_{i}\) is the element of load growing vector \(b\). 6. According to the ratio of reciprocal of cost of each generator, determine the allocation ratio \(k_{2,i}\) of power imbalance \(\Delta P_{2}\) amongst slack buses as \[k_{2,i}=\frac{\frac{\mathbb{L}}{\mathbb{L}_{i}^{c}}}{\sum\limits_{\mathbb{L} _{i}^{c}}},i\in\mathbf{B}\] (28) \[C_{i}(P_{Gi})=0.01{P_{Gi}}^{2}+0.3P_{Gi}+0.2\] (29) where \(C_{i}\) is the cost of the generator of bus \(i\). 7. Change the active power equation of the slack buses into \[P_{Gi}-k_{1,i}\Delta P_{1}-k_{2,i}\Delta P_{2}-P_{Di}-U_{i}\sum\limits_{j=1}^ {N}U_{j}(G_{ij}\cos\delta_{ij}+B_{ij}\sin\delta_{ij})=0,i\in\mathbf{B}\] (30) and then perform the power flow calculation. 8. Check whether some constraints have been violated. If there is any constraint violation, \(S_{Li}=S_{Li}-hb_{i}\) and the generators of slack buses minus the corresponding allocation power, then go to step 9. Otherwise, go to step 5. 9. Check whether \(h\) is smaller than the convergence condition \(\varepsilon\). If yes, go to step 10. Otherwise, cut the step \(h\) in half, i.e., \(h=h/2\), and go to step 5. 10. Calculate the ALSC as shown in (22). 11. Go to step 2 and calculate the ALSC at the next moment \(t+1\). In order to intuitively illustrate the two power imbalance allocations amongst the multi-slack buses, a schematic diagram is drawn, as shown in Figure 2.
# Targeted Therapy in Metastatic Bladder Cancer: Present Status and Future Directions
###### Abstract  
The recommended treatment for metastatic urothelial carcinoma (mUC) patients is platinum-based chemotherapy. Although initial response rates are moderate, the vast majority of patients experience a relapse due to chemoresistance and eventually succumb to their disease. Furthermore, platinum-based chemotherapy is toxic and approximately 30% of mUC patients are unfit for chemotherapy. Thus, there is a clear unmet need for novel, more efficacious treatment options in mUC with a safer toxicity profile. To propel the advancement of novel treatment options, we present a summary of key signaling pathways and molecular mechanisms that are known to be involved in bladder cancer tumorigenesis with a focus on promising candidate druggable molecular targets and innovative targeted therapies currently under clinical investigation. Targetable alterations were mainly described in fibroblast growth factor receptor (FGFR) and epidermal growth factor receptor (ErbB) tyrosine kinase receptor (RTK) families, downstream pathways, and chromatin remodelers, which are major bladder cancer driver genes. Drugs targeting the FGFR family members are emerging as personalized treatment options for selected mUC patients with tumor-specific FGFR alterations. The pan-FGFR inhibitor, erdafitinib, was first-in-class to receive U.S. Food and Drug Administration (FDA) approval in 2019, while inhibitors of ErbB family members have shown less potential. Antibody-drug conjugates (ADCs) are a class of targeted therapeutics that deliver cytotoxic drugs in close proximity to cancer cells by targeting RTKs or other transmembrane proteins. Enfortumab vedotin is the first-in-class ADC that was FDA approved for the treatment of locally advanced or mUC in 2019.  
muscle-invasive bladder cancer; tyrosine kinase receptor; RTK; targeted therapy; personalized medicine +
Footnote †: journal: Medical Cancer
## 2 System Model and Problem Description
### Problem Description  
In a traditional terrestrial fixed network, the links between node pairs are hardly changed, and the classical shortest path algorithm can efficiently perform data transfer tasks. However, in a time-varying network where the resources of nodes are limited, not only does the topology of the network change dynamically, but also the constraints on the resources make it impossible for the nodes to utilize all the potential contacts. If the shortest path algorithm is directly applied at this time, it is likely that the effective data transfer path cannot be obtained, and the network performance is severely degraded.  
Considering that the large amount of spatial nodes in our model, the example network collectively refers to all spatial nodes as \(SN\) in Figure 3, and the destination node is called as \(DN\). With the concern of the time-varying nature of the network topology and the difference in each state duration, the example network is divided into three states \(C_{1}\), \(C_{2}\) and \(C_{3}\). \(SN_{1}^{1}\) indicates the spatial node \(SN_{1}^{1}\) at state \(C_{1}\). For simplicity, the unit of state capacity is set as the time delivering a packet. The contact capacity of \(C_{1}\), \(C_{2}\) and \(C_{3}\) are set to 1, 3, and 3, and there are two packets to be delivered from \(SN_{1}^{1}\), \(SN_{2}^{1}\), and \(SN_{3}^{1}\) to the destination node \(DN_{1}^{1}\).  
Figure 3: Analysis of CPD Problems in an example space network.  
The classic Dijkstra algorithm calculates the shortest path based on the current topology. Since \(SN_{1}\) is the closest load node from \(DN_{1}\) at state \(C_{1}\) and \(C_{2}\), then contacts \([SN_{3}^{1},SN_{4}^{1}]\) and \([SN_{4}^{2},DN_{1}^{2}]\) are established at the first two states in the algorithm. Because of the limitation on contact capacity at state \(C_{1}\), the network can deliver only one packet to the destination node at the first two state. At the last state of the network, the node that can be connected with node \(DN_{1}\) has no data to transmit, and it can only establish the contact with other load spatial nodes. Finally, only one packet can be transferred to the destination node within three states.  
On the contrary, if CPD is performed in a searching manner using the CI algorithms, the contact established at state \(C_{1}\) might not be \([SN_{3}^{1},SN_{4}^{1}]\) but \([SN_{1}^{1},SN_{2}^{1}]\). Moreover, \(SN_{3}^{2}\) can fully utilize the contact capacity of state \(C_{2}\), and transmit three packets to node \(SN_{4}^{2}\), so as to enable node \(SN_{4}^{3}\) to transmit three packets to the destination node \(DN_{3}^{1}\) at state \(C_{3}\).  
The above example explains why the traditional shortest path algorithm is not suitable for the resource-constrained spatial networks. However, it should be noted that there is more than one type of spatial node in our MLSCN model, and the characteristics of the links between nodes are not the same. Therefore, the inter-layer CPs and intra layer CPs of the MLSCN are designed separately in the following.
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 3 Analog Fronthaul Signal Processing
### Fronthaul Power Constraints  
To enforce the cable power constraints in (11), it is necessary to scale the radio signal \(\mathbf{Y}\) in (24) by a factor of \(\lambda\) prior to the transmission over the fronthaul. This is given as  
\[\lambda=\sqrt{\frac{P_{\mathbf{c}}}{\delta P_{\mathbf{B}}(1+2\alpha^{2})+1}}, \tag{26}\]  
where \(\delta\) is equal to \(\delta=\left(1-L_{U}^{-1}\right)^{-1}\) for OMA, accounting for the fact that only \(L_{U}-1\) minislots are devoted to the eMBB UE, while it equals \(\delta=1\) for NOMA, since the eMBB transmission spreads over all \(L_{U}\) minislots. To simplify the notation, in the following we will account for the gain \(\lambda\) by scaling the noise over the cable after MRC in Equation (25) accordingly as  
\[\mathbf{w}_{k}(t)\sim\mathcal{CN}\left(\mathbf{0},\frac{1}{\eta\lambda^{2}} \mathbf{I}\right). \tag{27}\]
# An Efficient Carrier Synchronization Scheme for Demodulation Systems
## 4 Carrier Synchronization Scheme Simulation and Hardware Implementation  
### Carrier Synchronization Scheme Simulation and Performance Analysis  
Under the conditions of pilot-aided environment of symbol rate of 25 Mbaud, initial CFO of 5 MHz, initial phase offset of 2o, and a code length of 8160, the proposed carrier synchronization scheme is simulated with QPSK, 8 PSK, 16 APSK, and 32 APSK BER curves. The multiplication resource consumption of the RLR algorithm is proportional to the parameter N, which is 2 \(\times\) N. The larger the value of N, the more the multiplication resource is consumed, the smaller the capture range, and the higher the algorithm accuracy. Therefore, combining various factors, the valid signal can be controlled to make the RLR architecture work in the N = 2 architecture. When the carrier frequency offset is reduced to  
Figure 8: Block diagram of CPR implementation.  
Figure 7: Pilot-aided (PA) phase linear interpolator.  
a smaller range, the valid signal can be controlled to make the RLR architecture work in the N = 15 architecture so that the carrier frequency offset accuracy can reach the carrier phase offset.  
The number of superimposed pilot segments will affect the precision of the RLR algorithm. The larger the superimposed blocks, the higher the accuracy. In Figure 9, the root mean square error (RMSE) changes little when the number of superimposed blocks is less than 64. In the four modulations, the RMSE is almost the smallest when the number of superimposed blocks is L = 16 or L = 32. In this paper, the RLR algorithm with 16 superimposed blocks is selected for CFR.  
The differential detection of the SOF and PLSCODE algorithm detects the frame header and deduces the position of the pilot block. Furthermore, CFR reduces the CFO to 0.024% using 36 pilot symbols. The PA phase linear interpolator algorithm can correct the residual frequency deviation and phase deviation. As shown in Figure 10, the BER curve of the proposed carrier synchronization scheme almost coincides with the theoretical curve under four different modulations, and the demodulation performance loss is close to 0 dB. Therefore, the proposed carrier synchronization scheme is reliable under the condition of a low SNR and a normalized CFO of 20% of the symbol rate.  
The hardware complexity of the conventional carrier frequency synchronization scheme and the proposed carrier frequency synchronization scheme are compared in  
Figure 10: Simulation of BER curves of the proposed carrier synchronization scheme.  
Figure 9: Effect of the number of superimposed pilot blocks on the performance of the RLR algorithm.  
Table 1, where the proposed scheme simplifies the carrier frequency synchronization scheme from two-step to one-step. According to Table 1, compared with studies [2; 3; 4], multiplication resource consumption can be reduced by 90.68%, 72.47%, and 90.53%, respectively.