esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### OCPSI Mode Simulation
Compared with the traditional wide-image imaging modes (multi-sensor internal field of view stitching, multi-camera outer field of view stitching, and satellite agile imaging based on satellite maneuvering ability), the OCPSI mode has obvious advantages of increasing the width while ensuring a high resolution. In recent years, other novel imaging modes, the optical conical scanning imaging mode and linear array sensors circular scanning perpendicular to the orbit imaging mode, have been proposed [28]. The optical conical scanning imaging mode adopts the method of splicing multiple sensors in the field of view to form a large detection area camera. The camera is installed obliquely, and the satellite rotates continuously to complete the coverage. Linear array sensors circular scanning perpendicular to the orbit imaging mode adopts linear array CCD (Charge Coupled Device) camera, and the satellite vertical orbit is continuously rotated to complete the coverage. Both methods can achieve 1000-km coverage on a 500-km track. However, compared with the OCPSI mode, these two methods use the method of inner field of view splicing to design the camera, and the structure of the long focal length of the large primary mirror is unavoidable in order to ensure high resolution. Therefore, their payload volume and weight are too large, which limits their application in micro-nanosatellites. Compared with the new imaging mode, the OCPSI mode has unique advantages, which can take into account the high resolution, ultra-large width, and light weight at the same time.
# Prospects of Structural Similarity Index for Medical Image Analysis
## 3 Current Improvement in SSIM
### Four-Component Weighted SSIM  
The concept of 4-SSIM was derived from the same authors as in 3-SSIM to overcome the SSIM issue related to blurred image measures. However, 4-SSIM in the original article is fractioned into 4-SSIM itself, 4-MS-SSIM, 4-SSIM, and 4-MS-GSSIM. Therefore, using the four-component model, it is possible to compute four types of SSIM by partitioning the  
Figure 6: 3-SSIM procedures to quantify the image quality (adapted from [19]).  
Figure 7: Demonstration using “Lena” to present (**a**) original image, (**b**) blurred image, (**c**) edge pixel image, (**d**) smooth pixel image, and (**e**) texture pixel image.  
original images into four regions, namely a changed-edge region, preserved-edge region, smooth region, and texture region. The difference between 3-SSIM and 4-SSIM is in the edge region: whereas 3-SSIM has only one edge region, 4-SSIM has two edge regions. In addition, the condition used to determine which pixel belongs to which region is also different [18; 19].  
Figure 8 shows the procedures used to yield the similarities of 4-SSIM, 4-MS-SSIM, 4-GSSIM, and 4-MS-GSSIM. The steps in Figure 8 can be defined as follows:  
* Step 1: Calculate the SSIM map. This SSIM map is called the structure information.
* Step 2: Compute the gradient magnitude applying the Sobel operator for the reference and distorted images.
* Step 3: Define the threshold value \(TH_{1}=0.12\times g_{max}\) and \(TH_{2}=0.06\times g_{max}\), where \(g_{max}\) denotes a maximum grayscale level of the gradient magnitude when computed over the original image. Here, \(TH_{1}\) and \(TH_{2}\) have an effect on the component regions under these situations, i.e., the smaller the first value, the more \"edgey\" the region. Furthermore, the smaller the second value, the less smooth the region is.
* Step 4: Based on step 3, the images are segmented into the changed edge, preserved edge, smooth, and texture regions using the following rules: If \(p_{o}(i,j)>TH_{1}\) and \(p_{d}(i,j)>TH_{1}\), the edge region is preserved; If \((p_{o}(i,j)>TH_{1}\) and \(p_{d}(i,j)\leq TH_{1})\) or \((p_{o}(i,j)\leq TH_{1}\) and \(p_{d}(i,j)>TH_{1})\), edge region is changed; and If \(p_{o}(i,j)<TH_{2}\) and \(p_{d}(i,j)>TH_{1}\), it is a smooth region. Otherwise, the pixels belong to a texture region if they are not part of the edge pixels. Here, \((i,j)\) denotes the gradient coordinate, \(p_{o}\) is original image pixel, and \(p_{d}\) denotes a degraded image pixel.  
Figure 9 shows the images for every process within a specific region. Figure 9 depicts a preserved edge image of \"Lena,\" whereas Figure 9b shows a changed edge region image. In addition, Figure 9c displays a smooth region image, and Figure 9d depicts a texture region image.  
Figure 8: 4-SSIM and its differentiation model procedures to quantify the image quality (adapted from [18]).
# Parallel Processing Strategies for Geospatial Data in a Cloud Computing Infrastructure
## 3 Workflow: Spatial Aggregation
```
Input: Sentinel-2 Level 2A collection, parcels (as polygon features) Output: NDVI (time series at parcel level) cube \(\leftarrow\) Sentinel-2 Level 2A collection for each \(p\in\) parcels do for each \(t\in\) acquisition times of 2020 do \(npixels\gets 0\) \(\operatorname{NDVI}(t,p)\gets 0\) for each pixel \((x,y)\in p\)do if\(cube(x,y,t,SCL)\) is not masked then \(\operatorname{NDVI}(t,p)\leftarrow\operatorname{NDVI}(t,p)+\operatorname{NDVI}(x,y,t)\) \(npixels\gets npixels+1\) endif endfor if\(\operatorname{npixels}>0\)then \(\operatorname{NDVI}(t,p)\leftarrow\operatorname{NDVI}(t,p)/npixels\) else \(\operatorname{NDVI}(t,p)\leftarrow\operatorname{invalid}\) endif endfor
```  
**Algorithm 1** Pseudocode to obtain spatially aggregated normalized difference vegetation index (NDVI) time series at the parcel level. Optimizing the presented workflow for speedup is most relevant when a large amount of reference data needs to be extracted, for instance, to produce crop type maps at the continental scale ([21]), and in particular when dealing with deep learning techniques [22]. In this study, parcel information was obtained from publicly available data for the Netherlands (Basisregistratie Percelen van de Rijksdienst voor Ondernemend Nederland) and were downloaded [23]. The NDVI values were calculated and spatially aggregated for 36,644 parcels in total (Figure 2). For each parcel, a time series of 71 acquisitions was obtained that covered the year 2020. At a spatial resolution of 10 m, the spatial dimension of the data cube was 4133 rows by 3619 columns. In addition to the red and near-infrared (NIR) spectral bands, the data cube was composed of a third band corresponding to the scene classification (SCL). This band was needed to mask pixels that were either cloudy or not vegetated. Figure 2: Polygon features (36,644 in total) representing the parcels for a region in the Netherlands (\(51.50^{\circ}\)N, \(5.75^{\circ}\)E) used for spatial aggregation.
# Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
## 4 LoRa Study  
### LoRa Link Behavior  
LoRa transmission is successful if the link budget or received signal power \(P_{rx}\) greater than the receiver sensitivity \(S_{rx}\). The Sensitivity \(S_{rx}\) depends on the LoRa SF and Bandwidth:  
\[S_{rx}=-174+10 log10 BW+NF+SNR \tag{1}\]  
where \(-174\) is thermal noise in a 1-Hz bandwidth, \(BW\) is the receiver bandwidth, \(NF\) is a fixed value for receiver noise figure, and \(SNR\) is the ratio of signal power to the noise level as determined according to the selected spreading factor. The higher is the \(SF\), the more sensitive is the receiver; thus, the receiver could receive a packet from a greater distance.  
Total gains and losses determine the link budget during the transmission path can be expressed as follows:  
\[P_{rx}=P_{tx}+GL-L_{pl} \tag{2}\]  
where \(P_{tx}\) is the sender transmission power, \(GL\) is the combination of all general gains and losses, and \(L_{pl}\) is path loss in the wireless medium.  
To calculate the path loss, we use a log-distance path loss model [33]:  
\[L_{pl}(d)=\overline{L_{pl}}(d_{0})+10\gamma Log_{10}(\frac{d}{d_{0}})+X\sigma \tag{3}\]  
where \(L_{pl}(d0)\) is the mean path loss at reference distance \(d_{0}\), \(d\) is distance, \(\gamma\) is the path loss exponent, and \(X\sigma\sim(N,\sigma^{2})\) is the Gaussian distribution with a zero mean. To make our simulation more realistic, we provide the PDR acceptance model by observation:  
\[PDR(\hat{P_{rx}})=Pr(\hat{P_{rx}}>S_{rx})=P_{r}\bigg{(}z>\frac{S_{rx}-\hat{ \mu}}{\hat{\sigma}}\bigg{)} \tag{4}\]  
where \(z\) is Gaussian distribution, \(\hat{\mu}\) is the mean of the observed \(P_{rx}\), \(S_{rx}\) is the receiver sensitivity, and \(\hat{\sigma}\) is the observed standard deviation.  
The empirical measurement of LoRa transmission has been discussed in Reference [34], with \(\gamma\) = 2.32, \(LPL(d_{0})\) = 128.95, and \(X_{\sigma}\) = 7.8 dB. We also considered the measured receiver sensitivity on SX1272 Dataset [35].
## 5 Experimental Results and Analyses
### Hyperparameter Analysis
#### 5.2.2 Bridge-Percentage  
According to the work in [29], the proportion of bridge nodes in the network also affects the model test results. Therefore, we set different Bridge-Percentage for repeated experiments to change the proportion indirectly. For example, if Bridge-Percentage is set to 10%, the top 10% of nodes by Commn are marked, and we do the same for nodes ranked with NBNC and GLR. Then, the intersections of the three parts are defined as bridge nodes. It is clear that Bridge-Percentage directly determines the proportion of bridge nodes. Therefore, we set different value of Bridge-Percentage to verify the effect of the parameter on the effect of the BND-GCN model. The results are shown in Figure 4.  
The experimental results in this part show that as the Bridge-Percentage increases, the number of nodes defined as bridge nodes in the network increases, and the overall effect of the model decreases. Based on the experimental results, we speculate that when the Bridge-Percentage value is smaller, the bridging value of the selected node is more concentrated, and the characteristics of bridge nodes that are different from other nodes are more prominent and easier to capture. This may mean that our model will be more applicable in networks where there are few bridge nodes. When Bridge-Percentage is too small, it will lead to the problem of insufficient labels and data waste. This is another aspect that we should take into account when setting parameters.  
Figure 3: Effects of different Label-Ratio on five social networks.
## 2 Related Works
This approach has a high computational demand, achieving a DC of 0.8560 using 119 million parameters. Similarly, [PERSON] et al. [26] used a DenseNet architecture with 22.3 million parameters to segment the ISLES 2022 dataset, which obtained a mean DC of 0.694. Another state-of-the-art approach was presented by [PERSON] et al. [27]. They utilized the nnU-Net framework, which performs an automatic configuration and optimization of hyperparameters of a U-Net architecture [28]. The loss function consisted of combining the Dice and the Cross-Entropy loss functions. The model achieved a DC of 0.7641 when trained only with the ISLES 2022 DWI images and 0.7869 when transfer learning from different MRI modalities and databases was employed. Some studies revised in this section show improvements in segmentation procedures when they employ light changes in the U-Net architecture. However, the principal challenge resides in class imbalance, especially when small brain lesions have to be segmented. As can be observed, some methods address the challenge by using the Dice loss function. Nonetheless, they have reported that its behavior is sometimes unstable [23]. Alternately, some procedures use the combination of loss functions to deal with the class imbalance and enhance the segmentation performance on the edges of the ground truth mask, such as in the work of [PERSON] et al. [29]. Other approaches consist of using a combination of different MRI modalities and planes or proposing more weighty models but at a higher complexity cost. These last strategies represent a drawback in model training due to the requirement of specialized computer architectures explicitly oriented to data processing, making applying the models in non-high-tech environments challenging. In this work, the proposed approach involves a modified U-Net network, based on the work of [PERSON] et al. [15], and a loss function that combines the advantages offered by the Focal loss and Dice loss in addressing class imbalance. The principal contributions of this study are listed below:  
* A new system based on U-Net architecture using attention mechanisms that enhance the segmentation in MRI images of small brain lesions caused by ischemic stroke by incorporating the Generalized Dice Focal Loss (GDFL) composite function. * Efficient segmentation in different MRI modalities and planes. * Improved segmentation performance compared to state-of-the-art systems based on the evaluation of the system using the ISLES 2015 dataset and competitive performance with state-of-the-art models in evaluating the ISLES 2022 dataset. * The model moderately converges at 200 epochs with 7.9 million parameters, making it suitable for training in general-purpose mid- and high-end graphic processing units (GPU).
## 4 Experiment and Results
### Evaluation Metrics  
Nine quantitative metrics are employed to assess the effectiveness of our DEPANet, including MAE [28], S-measure (SM) [29], weighted F-measure (w-\(F_{\beta}\)), Adaptive F-measure (\(F_{e}\)), Mean F-measure (\(F_{\beta}\)), Maximum F-measure (\(F_{max}\)), [30], Adaptive E-measure (\(E_{e}\)), Mean E-measure (\(E_{\beta}\)), and Maximum E-measure (\(E_{max}\)) [31].  
The MAE quantifies pixel-level discrepancies through absolute differences between the saliency map \(S\) and binary annotation \(T\):  
\[\text{MAE}=\frac{1}{H\times W}\sum_{x=1}^{H}\sum_{y=1}^{W}|S(x,y)-T(x,y)| \tag{20}\]  
where \(H\) and \(W\) denote spatial dimensions. The SM evaluates structural preservation through dual components:  
\[\text{SM}=\gamma\times Q_{obj}+(1-\gamma)\times Q_{reg} \tag{21}\]  
where \(Q_{obj}\) measures object-level shape fidelity, \(Q_{reg}\) assesses regional coherence, and \(\gamma\) balances their contributions.  
Figure 4: Visual examples of different types of SD-saliency-900 dataset.  
The Weighted F-measure, Maximum F-measure, Mean F-measure, and Adaptive F-measure collectively provide a nuanced evaluation of precision and recall in the outcomes of saliency segmentation. These metrics are defined as follows:  
\[F_{\gamma}=\frac{\left(1+\gamma^{2}\right)\times\mathcal{P}\times\mathcal{R}}{ \gamma^{2}\times\mathcal{P}+\mathcal{R}} \tag{22}\]  
where \(\mathcal{P}\) and \(\mathcal{R}\) are computed with threshold-specific strategies. Different values of \(\gamma\) can correspond to various versions of the F-measure, including the weighted F-measure, Maximum F-measure, Mean F-measure, and Adaptive F-measure.  
Adaptive E-measure, Mean E-measure, and Maximum E-measure are metrics used to assess the similarity or matching degree between the predicted saliency map and the ground truth. The formulas are presented as follows:  
\[E_{\xi}=\frac{1}{H\times W}\sum_{x=1}^{H}\sum_{y=1}^{W}\psi\odot M(x,y) \tag{23}\]  
where \(\psi\) denotes an enhancement operator for local contrast adjustment, and \(\xi\) determines evaluation mode: The Mean-E-measure averages across thresholds, Maximum E-measure selects optimal thresholds, and Adaptive E-measure uses adaptive thresholding.
## 4 Results
### Evaluation of Ensemble Models
#### 4.2.1 Combining Contextual Embeddings with ConceptNet Numberbatch  
In the SBERT group, the late fusion ensemble, SBERT \(\#\) CN-NB @0.5 consistently achieves the best results with respect to both Recall@3 and MRR in all subjects and both exercise types. Here, we take an unweighted average of the cosine similarity scores, i.e., \(\alpha\) = 0.5, so that the two models contribute equally. The McNemar test shows that the differences in Recall@3 between SBERT \(\#\) CN-NB @0.5 and the single use of SBERT are statistically significant (quiz: \(p\) = 0.029, study question: \(p\) = 0.000). Additionally, SBERT \(\#\) CN-NB @0.5 performs significantly better in Recall@3 than SBERT \(\bigoplus\) CN-NB for study questions (\(p\) = 0.000), although not for quizzes (\(p\) = 0.727).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Quiz**} & \multicolumn{10}{c}{**Study Question**} \\ \cline{2-13}  & \multicolumn{4}{c}{**Recall@3**} & \multicolumn{4}{c}{**MRR**} & \multicolumn{4}{c}{**Recall@3**} & \multicolumn{4}{c}{**MRR**} \\ \cline{2-13}  & **Bio.** & **Soc.** & **Phy.** & **Mean** & **Bio.** & **Soc.** & **Phy.** & **Mean** & **Bio.** & **Soc.** & **Phy.** & **Mean** & **Bio.** & **Soc.** & **Phy.** & **Mean** \\ \hline SBERT & **0.921** & 0.977 & 0.957 & 0.951 & 0.724 & 0.922 & 0.814 & 0.820 & 0.864 & 0.980 & 0.908 & 0.917 & 0.721 & 0.907 & 0.758 & 0.795 \\ ADA- & 0.909 & **0.997** & **0.986** & **0.964** & **0.775** & **0.966** & **0.881** & **0.874** & **0.916** & **0.984** & **0.960** & **0.953** & **0.773** & **0.926** & **0.865** & **0.855** \\ CN-NB & 0.830 & 0.951 & 0.918 & 0.899 & 0.665 & 0.897 & 0.755 & 0.773 & 0.742 & 0.856 & 0.827 & 0.808 & 0.610 & 0.760 & 0.691 & 0.687 \\ \hline \hline \end{tabular}  
* Bio. = Biology, Soc. = Social Science, Phy. = Physics. CN-NB = ConceptNet Numberbatch.  
\end{table}
Table 3: Results of using embeddings derived from a single model. (**Bold**: max.).  
In the ADA-002 group, ADA-002 \(\bigoplus\) CN-NB, i.e., a simple embedding vector concatenation in an early fusion fashion, exhibits the best results for all the mean performances, except slightly lower mean Recall@3 than using ADA-002 alone. A closer comparison between the results of the single use of ADA-002 and the best performer ADA-002 \(\bigoplus\) CN-NB shows that the difference is very subtle and the advantages can almost be ignored. [PERSON]'s test shows that the differences in Recall@3 between ADA-002 \(\bigoplus\) CN-NB and using ADA-002 alone are not significant (quiz: \(p\) = 1.000, study question: \(p\) = 0.203). Moreover, ADA-002 \(\bigoplus\) CN-NB outperforms ADA-002 \(\#\) CN in all mean Recall@3s and MRRs.
## 3 Problem Definition and Formulation
### Constraints
#### 3.2.2 Task Constraints  
Each task \(j\) must be completed within a certain time period, and the total resource demand for the task cannot exceed the total available resources of the satellite nodes. For task \(j\), the total resource demand \(D_{j}\) must satisfy the following condition:  
\[\sum_{i=1}^{n}t_{ij}=D_{j},\quad\forall j=1,2,\ldots,m \tag{4}\]where \(D_{j}\) is the total resource requirement for task \(j\). This constraint ensures that the resource requirements for each task are fully met and that there is no resource shortage during the allocation process.
# DIKWP-Driven Artificial Consciousness for IoT-Enabled Smart Healthcare Systems
## 6 Experimental Results
### Communication Overhead and Efficiency  
One major advantage of our architecture is reducing data transmission through local processing. We logged network usage for each scenario. The DIKWP Edge-Cloud AC system only transmits summarized information and occasional raw data on demand, whereas the baseline was set to stream all raw sensor data to cloud (which is typical in many current IoT deployments).  
We found that on average, our system transmitted about 1.2 MB of data per day per patient, whereas the baseline transmitted roughly 15 MB per day per patient in our test (which had one reading per minute for vitals; higher frequency like ECG would exacerbate this difference dramatically). This is a 92% reduction in data volume. The savings come from the following:  
* Only sending one message per minute with 4-5 key values (JSON ~100 bytes) instead of continuous streams.
* Suppressing transmission during idle periods (if nothing abnormal, some less critical vitals like accelerometer detailed data are not sent, only a summary).
* Utilizing semantic compression, e.g., instead of raw time series, sending \"All vitals normal in last 5 min\" as a single status message.  
The communication frequency also drops. The baseline sent ~60 messages/hour (one per minute per vital maybe), whereas our system sent on average 6 messages/hour (most being periodic summaries, plus occasional alerts). In one scenario with an emergency, our system spiked to 20 messages in that hour (to communicate the alert and subsequent data needed for the emergency), but still well below baseline continuous usage.  
In terms of network latency, because we reduced traffic, network congestion is less and our important messages (alerts) get through faster. We measured the round-trip time between edge and cloud under both loads. Under baseline heavy load, the median RTT was ~200 ms (with spikes if bandwidth choked), whereas with our light load, median RTT ~50 ms. This matters if cloud acknowledgment or further analysis is needed.  
Energy efficiency: Lower communication implies the device's radio is used less, which typically saves battery. We estimated (based on common Bluetooth/WiFi usage patterns) that our wearable would spend perhaps 10% of time transmitting vs. 80% for baseline, potentially extending battery life significantly (exact battery calculation is complex though, so we leave it at a conceptual improvement).
## 1 Introduction
Deep learning harnesses self-supervised reverse algorithms for optimization and adjustment, enabling the automatic extraction of fault feature information from the original signal devoid of manual interference. It sequentially processes this information layer by layer, transforming it into abstract features conducive to classification. Consequently, it mitigates the constraints linked to manual feature selection [16; 17]. [PERSON] et al. proposed a quantitative recognition model based on convolutional neural networks to address the issue of insufficient manual signal feature extraction, converting one-dimensional signals into two-dimensional signals and thereby improving the accuracy and speed of wire rope breakage recognition [18]. [PERSON] et al. used machine vision technology to train ground wave radar surface images, automatically extract discriminative features, and import them into the constructed convolutional neural network for training, overcoming the inherent limitations of manual feature extraction methods and achieving good results [2]. However, it is worth noting that the above-mentioned wire breakage detection methods mainly focus on detecting external wire breakage damage, and there has been relatively limited research on the detection of internal wire breakage damage characteristics. Therefore, there is an urgent need for a new method to accurately identify the internal and external wire breakage characteristics of steel wire ropes. The residual network was proposed by Microsoft Labs in 2015; it greatly improves the training depth and speed of the network and enhances its feature extraction ability by utilizing residual modules. Hence, residual networks will be proposed and compared with three common neural networks in this article, using sample data to adjust network parameters in a timely manner and determine the optimal method for quantitative damage identification. The main research findings of this article are as follows:  
1. A new method for processing broken wire signals has been proposed, which converts the one-dimensional signal of broken wire MFL into a two-dimensional time frequency map through a neural network, eliminating the tedious step of manually extracting features and improving the efficiency of signal processing. 2. By constructing a residual network for internal wire breakage recognition and utilizing the residual module to improve the training depth and speed of the network, the accuracy of identifying internal wire breakage within steel wire ropes can be markedly enhanced. The rest of this article is organized as follows: Section 2 introduces the theoretical knowledge of convolutional neural networks and residual networks. Section 3 provides detailed preparations for the experiment. In Section 4, the experimental method of this paper is proposed, and the accuracy of four neural networks for wire breakage recognition is compared. Finally, the conclusions are drawn in Section 5.