esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Linear Quantization for General and Effective Low Bit-Width Network Compression
## 2 Related Work
### Post-Training Quantization
#### 2.2.1 Weight-Sharing Quantization  
For neural networks, weights can be clustered and shared. The majority of the literature on weight-sharing quantization aggregates weights into different sets to reduce memory space usage. Among them, weight aggregation is mainly based on clustering or hash functions. [PERSON] et al. proposed HashedNet, which uses the hash function to randomly group weights into hash bushets, and the weights in the same hash bushets share the same  
Figure 3: In the effect of neural network sparsity on accuracy (based on Alexnet) [17]. It can be found that the convolutional layer is more sensitive to sparsity than the fully connected layer, and the depth of the convolutional layer will also affect the sensitivity. The deeper it is, the lower the sensitivity.  
value [19]. Clustering is the process that divides objects into multiple sets according to their characteristics. Clustering is usually used to compress network models. [PERSON] et al. used the K-means algorithm to cluster the network weights, and the weight matrix stored the labels of the clusters, and then processed the labels with the Huffman coding to compress the neural network model by more than 30 times, and the accuracy of the network did not drop significantly [4]. [PERSON] et al. proposed a Q-CNN (quantized convolutional neural network) framework for compression by the K-means algorithm and then accelerated by the matrix factorization method [20].  
Most of the research mentioned above stores the cluster labels instead of the cluster centroids to maximize the compression of the neural network model. However, in the actual inference, they still need to look up the table to get the cluster centroids the cluster labels still need to be converted into cluster centers, which cannot speed up the inference. Instead, this paper applies the clustering to the traditional linear quantization, and the neural network is compressed and quantized in fix-point numbers at the same time.
# A Real-Time Fabric Defect Detection Method Based on Improved YOLOv8
## 3 Methods
### Inner-WloU Loss Function
In inner-IoU, it is assumed that the GT box and anchor box are denoted as \(\text{b}^{\text{gt}}\) and b, respectively. Their centers are \(\left(\text{x}_{\text{c}}^{\text{gt}},\text{y}_{\text{c}}^{\text{gt}}\right)\) and \(\left(\text{x}_{\text{c}},\text{y}_{\text{c}}\right)\), respectively, as shown in Figure 5. A scaling factor ratio whose value range is [0.5,1.5] is also introduced to adjust the size of the box. The value of the inner-IoU loss function is defined as the ratio of the intersection area to the union area. By introducing auxiliary boxes of different scales, this method enables more robust optimization of the predicted box's position and size during the bounding box regression process. Its formulation is as follows:  
\[\text{IoU}^{\text{inner}}=\frac{\text{inter}}{\text{union}} \tag{11}\]The formulas for the intersection area and union area are as follows:  
Figure 4:0  
Figure 4:1  
Here, \(\text{b}_{\text{f}}^{\text{gt}}\text{b}_{\text{r}}^{\text{gt}}\text{b}_{ \text{t}}^{\text{gt}}\text{b}_{\text{b}}^{\text{gt}}\) and \(\text{b}_{\text{l}}\text{b}_{\text{r}}\text{b}_{\text{t}}\text{b}_{\text{b}}\) denote the left and right and top and bottom boundaries of the ground truth box and anchor box, respectively, under the scaling ratio, where ratio is the scaling ratio. The formulas for the left and right and top and bottom boundaries of the ground truth box and anchor box under the scaling ratio are as follows:  
Figure 4:2  
Figure 4:3  
Figure 4:4  
Figure 4:5  
Finally, this paper combines WIoU with inner-IoU to propose the inner-WIoU loss function. This loss function leverages the strengths of both, improving complex fabric defect detection and accelerating bounding box regression. Its formulation is as follows:  
Figure 4:6  
Figure 4:7  
By using the inner-WIoU loss function, YOLOv8 demonstrates higher accuracy and better real-time detection capabilities in fabric defect detection tasks, making it more suitable for practical application requirements.
## Appendix A Framework for Physital Cultural Heritage Tourism Application Development
### Applying the Framework to the Hanging Gardens of Babylon AR Application
#### 1.4.3 Appendix A.6. Applying the Framework to \"The Life of a House\" AR Application
Stage 5: Solution Development  
Technical Specifications:  
Use blockchain standards (e.g., ERC-721, ERC-1155, etc.) to create unique digital collectibles. Enhance AR functionality with accurate 3D models and interactive overlays of the Weigh House's reconstruction. Iterative Development:  
Test the app with diverse representative user groups (including tourists, educators, and local residents) to refine usability and content. Marketing Strategy:  
Promote the app through partnerships with Tallinn's tourism board and cultural institutions, highlighting its role in heritage preservation. Stage 6: Testing and Refinement  
Functional Testing:  
Validate the app's AR features across different devices to ensure a seamless user experience. Address technical issues, such as jittering AR overlays and confusing interface elements. User Feedback:  
Conduct surveys and interviews to evaluate the app's effectiveness in enhancing learning retention and engagement. Incorporate feedback to refine interface design and instructional clarity. Compliance Check:  
Ensure alignment with UNESCO heritage standards and accessibility guidelines. Stage 7: Deployment and Support  
Launch Strategy:  
Launch the app with targeted campaigns aimed at tourists, educators, and cultural enthusiasts. Provide ongoing support to address technical issues and gather user feedback. NFT Placement and Accessibility:  
Offer NFTs representing architectural features or historical moments of the Weigh House through an integrated marketplace. Use QR codes on-site to link visitors to relevant digital collectibles. Compatibility and Maintenance:  
Regularly update the app with new features, such as additional AR content or gamified challenges. Stage 8: Evaluation and Iteration  
Performance Metrics:  
Monitor user engagement through metrics like task completion rates, NFT redemptions, and time spent in the app. * Assess the app's impact on learning retention and cultural appreciation. Long-Term Improvements:
* Expand AR content to include other historical sites in Tallinn, creating a city-wide AR experience. * Use analytics and user feedback to refine gamified elements and introduce new narratives. Applying the framework to the \"Life of a House\" AR application enhances its potential to serve as a tool for cultural preservation and public education. By incorporating gamified elements, NFTs, and immersive storytelling, the app can deepen user engagement and foster a global appreciation for Tallinn's architectural and historical heritage. These enhancements not only enrich the user experience but also promote sustainable tourism and the preservation of cultural landmarks.
# Machine Learning Techniques for Non-Terrestrial Networks
## 4 Non-Terrestrial Network Architectures
### Impact of NR in NTN  
NTN standardization in 3 GPP started in 2017, and it is fundamental to gain global diffusion and for the adoption of a unique UE, both for connecting TN and NTN simultaneously or depending on the accessed area. According to the technical specification (TS) 38.821 [27], NTN can operate in the two bandwidth groups: FR1 (i.e., at frequency \(f<6\) GHz) and FR2 (i.e., at frequency \(f>6\) GHz). In FR1, the operating frequency for the service link is 2 GHz (S-band) with a maximum channel bandwidth of 30 MHz, while in FR2, the operating frequencies are 20 GHz in downlink and 30 GHz in uplink (Ka-band) with a maximum channel bandwidth up to 1 GHz.  
Due to large delays and Doppler shifts, some changes are proposed in the NR. One important aspect is related to the UE mobility management. Due to the NTE moving with respect to the ground (except for the GEO and low-altitude UAV), there exists the shift in the antenna beam on the ground, leading to have moving tracking areas (TAs). This causes frequent location area and tracking area update on UEs, even if there are fixed or slightly moving UEs. One possible solution is to implement fixed TAs obtained by properly steering the antenna beam of the NTE, causing just a feeder link setting when a LEO, for example, goes down to the horizon and another covers the TA. In the case of fixed TAs, the mobility is managed according to similar procedures as for terrestrial NG-RAN. Anyway, moving TAs can also be implemented.  
NR defined in Release 15 and Release 16 is a good basis to support the NTN connectivity. Nevertheless, some changes are needed: in layer 1, the optimization of some timers, adapting the HARQ procedure in terms of number of processes and feedback, and definition of switching procedure for the feeder link; and in layer 2, adaptation in the random access (RA) procedure, such as for preamble ambiguity and extension of the RA response (RAR) window, timing advance for the access and synchronization of the downlink and uplink frames, the discontinuous reception (DRX), scheduling request (SR), extending the status report in radio link control (RLC), sequence numbers in packet data convergence protocol (PDCP), definition of specific system information block (SIB) for NTN (i.e., SIB-19), and additional information should be provided to UEs in the case of moving cells (i.e., moving TAs), such as satellite Ephemeris information to favor its cell reselection. For example, it is considered a common timing advance defined as an offset between the gNB and a reference point in order to favor the synchronization and scheduling activities, as well as inserting in SIB-1 the information of whether the radio access network is terrestrial or non-terrestrial [33].
## 3 The Proposed Conversational System: CORES
### Workflow of CORES
#### 3.5.3 Emotion Embedding
_Feature Embedding Matching Based on User's Preferences_ The feature embedding similarity score \(Sim\_Feature\) between the user's query \(Q\) and the item description \(d_{i}\) from the previously filtered store is calculated using a weighted cosine similarity formula as follows: \[Sim\_Feature(Q,d_{i})=\sum_{j=1}^{M}w_{j}\cdot\frac{E_{Qf_{j}}\cdot E_{f_{j,d_{ i}}}}{\|E_{Qf_{j}}\|\cdot\|E_{f_{j,d_{i}}}\|}\] (7) where \(w_{j}\) is the weight for feature \(f_{j}\), and \(E_{Qf_{j}}\) and \(E_{f_{j,d_{i}}}\) are the embeddings for the user's and item's feature, respectively. Items with the highest similarity scores are filtered for the next step. 3. _Social Embedding Matching Driven by User's Emotions_ This step evaluates the emotional alignment between a user's query and comments on filtered items. Each comment's embedding vector \(E_{c_{ij}}\) is compared with the user's query emotion embedding vector \(E_{Q\_emotion}\) using cosine similarity \(Sim\_ Emotion\) as follows: \[Sim\_ Emotion\big{(}Q,c_{ij}\big{)}=\ \frac{E_{Q\_emotion}\cdot E_{C_{ij}}}{\|E_{Q\_ emotion}\|.\|E_{C_{ij}}\|}\] (8) The similarity scores of all comments for an item are aggregated to compute the overall social emotion-based similarity \(Sim\_ Social\): \[Sim\_ Social\big{(}Q,d_{i}\big{)}=\frac{1}{L}\sum_{j=1}^{L}Sim\_ Emotion\big{(}Q,c_{ij}\big{)}\] (9)  
Items with comments that strongly match the user's emotional context receive higher scores and are prioritized for the next step. This process ensures recommendations that consider both the user's emotional intent and social feedback. 4. _QoS Matching Based on User's Context_ The store's location and the user's delivery address are crucial factors in calculating the delivery time for the recommended item from the prior step. The delivery time is estimated using the Euclidean distance between the store and the delivery address, divided by the average speed of the delivery vehicle. Additional elements, such as transportation mode, road conditions, and real-time traffic, can be factored in to enhance the accuracy of the travel time estimate. The context-aware QoS similarity score (\(QoS\)) reflects this estimated delivery time using the following formula: \[QoS(DS_{i},U)=DT=\frac{D_{i}}{\textit{speed}}\] (10) where \(DT\) is the estimated delivery time, \(D_{i}\) is the distance between the digital store \(DS_{i}\) and the user's delivery address, and _speed_ is the average speed of the delivery vehicle. The Euclidean distance \(D\) between the digital store and the delivery address is given by: \[D=\sqrt{\big{(}x_{DS_{i}}-x_{d}\big{)}^{2}+\big{(}y_{DS_{i}}-y_{d}\big{)}^{2}}\] (11) where \(\big{(}x_{DS_{i}},y_{DS_{i}}\big{)}\) are the GPS coordinates of the digital store \(DS_{i}\) and \((x_{d},y_{d})\) are the GPS coordinates of the delivery address. Items from stores with the shortest delivery times and highest positive emotional feedback proceed to the next stage.
# Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal
## 4 Results and Discussion  
### Spatial Effects in Crime Rates  
Property crime rates exhibit positive spatial autocorrelation over the study region since the value of the Global Moran's I statistic was 0.354, and it was statistically different from zero (z-score = 12.64; \(p\)-value < 0.0001). This facet means that municipalities with high crime rates correlate with high neighboring values, or municipalities with low values correlate with low neighboring values.  
Figure 2 shows the distribution of significant positive (high-high and low-low clusters) and negative (high-low and low-high outliers) spatial autocorrelations for property crime rates. Local Moran's I revealed local clusters of high-high values in southern municipalities and the Lisbon and Porto metropolitan regions, where high crime rates are surrounded by high crime rates. Many municipalities in the central area of the north and the municipalities of Reguengos de Monsaraz, Alandroal, and Estremoz (east and southeast of Lisbon) correspond to low-low clusters (i.e., these municipalities with low crime rates have neighboring municipalities with low crime rates). Furthermore, two municipalities near Lisbon (Vila Franca de Xira and Odivelas) and Monchique in the south correspond to low-high spatial outliers, where low values correlate with high neighboring values. The municipalities of Montalegre, Braga, Felgueiras, Mesao Frio, Viseu, Celorico da Beira, Seia, Coimbra, Gois, Castanheira de Pera, and Evora have high crime rates surrounded by low crime rates; thus, they are depicted as high-low spatial outliers.  
The Getis-Ord Gi* statistic was used to identify the locations of statistically significant clusters with either high (hot spot) or low (cold spot) values (Figure 3). As expected from the previous analyses, the metropolitan regions of Lisbon and Porto, as well as the Algarve region in the south, stand out as clusters with high property crime rates. The north region displays a cluster of low crime rates with 43 municipalities (at significance levels smaller than or equal to 10%). Additionally, the municipalities of Santa Comba Dao,  
Figure 2: Results of the Local Moran’s I statistic for property crime rates per thousand inhabitants by municipality in 2017.  
Penacova, Tabua, Condeixa-a-Nova, Lousa, Miranda do Corvo, Gois, Castanheira de Pera, Proenca-a-Nova, Estremoz, Alandroal, and Reguengos de Monsaraz are included in other cold spot clusters.
# Integrated Motion Planning for Assembly Task with Part Manipulation Using Re-Grasping
## 1 Introduction
If there is no feasible grasp for the part for its given initial, and final poses and assembly constraints then we can find intermediate poses of the part such that we can have at least one feasible grasp from initial pose of part to its intermediate pose and also from intermediate pose to final pose. A finite set of stable poses of a part can be evaluated based on the geometry of the part to build an orientation graph. Since the proposed approach considers a finite set of orientations of the part, it is computationally less costly than the C-space search based approaches. Integrated assembly sequence and motion planning is presented in [17; 18] using combinatorial approach for assembly planning. But the motion planning for the robot in [17] was variant of RRT [19] which grows a tree in the configuration space from start at every stage of the assembly process. But it would be more efficient if we can use a single tree for the whole assembly process. Because while assembling multiple parts sequentially, the task space is changing at every step of the assembly operation. In this situation, the robot configuration which is collision free at an earlier stage of assembly operation may not be collision free in a later stage. In [20], lazy collision checking was introduced for single query probabilistic planner which performs the collision checking only along the searched path. In [21], multi-partite RRT (MP-RRT) was introduced for re-planning in a dynamic environment. The MP-RRT removes the failed nodes and edges and stack the collision free broken branches in the forest data structure. On the contrary, we propose a semi-lazy collision checking strategy for part assembling task. While growing tree, the new node added to tree is checked for collision according to the current state of the task space. But collision checking along the edges is only performed for those which are in the searched path. In case of collision along the edge (between nodes \(a\) and \(b\)), the edge is removed while the node \(b\) (which is the root of a tree disconnected from the main tree) is included in the set of forest roots. While growing tree, the algorithm tries to connect the disconnected node to main tree using what we term two stage extend-RRT function. While extending main tree, it adds a new node \(q_{1}\) in main tree in first stage and extend the \(q_{1}\) to nearest forest root in second stage. This strategy helps to re-connect the forest roots to the main tree.
# A Novel Perturbation Consistency Framework in Semi-Supervised Medical Image Segmentation
## 4 Experimental Results and Analysis
### Deployment and Usage Details  
Our framework is implemented using PyTorch and can be deployed on servers via Docker for scalability. For large-scale deployment, we recommend running the model on servers with GPU support, such as AWS or Google Cloud, to handle high computational demands during training and inference. Docker ensures the portability of the framework across different platforms, enabling easy scaling and maintenance of the segmentation model in various environments.
## 5 Experimental Results
### State-of-the-Art Comparisons  
We have already described that the proposed method fulfills the desired objective with better performance compared to the state-of-the-art methods for human action recognition using 3D skeleton dataset. For the fairness of the experimental results, we have separately mentioned the strategies with classifiers to compare the recognition accuracies between the proposed system and the prior works for UTD-MHAD and MSR-Action3D datasets as depicted in Tables 5 and 6. Among the six different experimental results (front, side, top, average, multiplication, and maximization), we only list the best one for each model.  
Figure 10: Visual comparisons of different views and fusions of dataset and models; **(a)** UTD-MHAD and **(b)** MSR-Action3D datasets.  
Figure 11: Classification performance of the proposed system based on individual classes in **(a)** UTD-MHAD and **(b)** MSR-Action3D datasets.  
\begin{table}
\begin{tabular}{c c} \hline
**Methods** & **Accuracy** \\ \hline STIF with MobileNetV2 & 98.93\% \\ STIF with DenseNet121 & 99.65\% \\ STIF with ResNet18 & 98.80\% \\ \hline BOP with ELM [51] & 95.30\% \\ TPSMM with CNN [65] & 88.10\% \\ JDM with CNN [66] & 93.26\% \\ SOS with CNN [67] & 86.97\% \\ JTM with CNN [68] & 85.81\% \\ \hline \end{tabular}
\end{table}
Table 5: Performance comparisons of human action recognition using UTD-MHAD dataset.  
The previous method in [51] attains about 95.30% outcomes for human activity recognition using the UTD-MHAD dataset which is the highest among the references [64, 66, 67, 68, 51]. The proposed method achieves accuracy of 99.65%, the highest experimental results, with STIF representation of UTD-MHAD skeleton dataset using DenseNet121 which is around 4% greater than the methods using TPSMM [64], JDM [66], SOS [67], and JTM [68] with CNN. Even though we apply MobileNetV2 and ResNet18, our method secures about 3% better accuracy than the state-of-the-art methods.  
We further provide the comparative results for MSR-Action3D skeleton dataset to boost up on the suggested system. The proposed method ensures 98.75% accuracy with DenseNet121 which is the better than the defending best accuracy of 97.25% accuracy achieved by SOFGF with CNN [62]. The other methods [51, 52, 53] classified human action with accuracies of about 4% lower than the proposed method. On the other hand, our method can obtain about 96.00% and 97.08% recognition accuracies with MobileNetV2 and ResNet18 correspondingly.
## 2 Materials and Methods
### Implementation Details  
Each set of experiments was assigned the same random seed number to ensure reproducibility of the results. All models were fine-tuned on our self-built dataset without using pre-trained models. All model codes in the experiments were written based on the PyTorch framework, and the experiments were run in an RTX 3090 (24219 MiB) GPU environment. All experiments were set to 70 training epochs, except for the Vivit model which was trained for 100 epochs. The batch size was set to 16, the optimizer used was SGD, the learning rate was set to \(10^{-3}\) and the loss function was the cross-entropy function.
# An In-Depth Assessment of the New BDS-3 B1C and B2a Signals
## 4 Carrier-To-Noise Density Ratio  
When GNSS receivers record navigation messages and observations, they generally record the signal strength, which can characterize the quality of observations data and receiver signal tracking performance [18]. The signal strength is described by the signal-to-noise ratio index (that is, the signal-to-noise ratio SNR), which is defined as the ratio of the carrier signal power to the noise power in dB. Carrier-to-noise ratio C/N\({}_{0}\) is the normalized signal-to-noise ratio, which is defined as C/N\({}_{0}\) over a 1 Hz bandwidth in dB/Hz (see Equation (1)).  
\[\mathrm{C/N_{0}}=20\cdot\mathrm{lg}\left(\frac{\mathrm{SNR}}{\sqrt{\mathrm{s} }}\right) \tag{1}\]  
where the C/N\({}_{0}\) indicates the carrier-to-noise density ratio 1 Hz bandwidth, 20 is a constant, \(\mathrm{lg}\) is the logarithmic operator, SNR (usually in dB) is the signal-to-noise ratio, and s is short for seconds.  
There are many factors affecting C/N\({}_{0}\), including external factors of the receiver, such as satellite transmit power, satellite antenna gain, space loss, and atmospheric attenuation. The local factors, such as include receiver antenna gain, receiver performance, noise level  
Figure 4: Schematic diagram of the GNSS zero-baseline.  
Figure 3: Trimble receiver and choke antenna.  
and multipath effects, etc. In addition, it may also be affected by factors such as season and temperature. C/N\({}_{0}\) has been widely used in the modeling multipath error or random model in the GNSS high-precision applications [19].  
Because the signals of the GNSS satellites in different orbits are different, the MEO, GEO and IGSO satellites of BDS-3 are discussed and analyzed separately.