esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The five major changes, which have been incorporated into an improved TMI radiometer Tb algorithm, are summarized in this article. Using this new algorithm, the entire >17-year time series has been reprocessed to produce the legacy 1B11 V8 brightness temperature product, which was used to perform inter-satellite XCAL with the other members of the TRMM constellation.  
This paper documents the validation of the 1B11 V8 Tb product. The reprocessed Tb time series are compared to the theoretical (modeled) Tb from the XCAL RTM for >15-year period, and the single differences between them are calculated. A detailed analysis of results demonstrates that all 9-channels of the TMI exhibit extraordinarily long-term stability. Moreover, the V8 Tb are compared to the previous version, V7, by using the XCAL double difference technique with the GMI as the reference standard. Overall, TMI V8 dataset exhibits smaller DD biases with GMI, and the DD STD are slightly better. Moreover, for the 19V, 19H and 23V channels, the troublesome dependence on latitude that appears in TMI V7 is essentially removed in the V8 dataset. The V8 stability with time and yaw angle is also improved when compared to V7. Additionally, V7 DD has an apparent positive linear dependence on scene brightness that has been removed from V8.  
Moreover, there is an increased confidence in this V8 data set as a climate data record, because of an improved pedigree that replaces ad-hoc radiometric calibrations (V6 and V7) by a rigorous derivation of radiometric uncertainty, based upon on-orbit measurements and electromagnetic theory. Therefore, this legacy brightness temperature product allows earth scientists to look for long-term changes in their earth geophysical models without fear of drifts in the TMI radiometric calibration.  
This work was sponsored under a grant NNX16 AE35G with the NASA Headquarters Earth Sciences Division for the Precipitation Measurement Mission. No funds have been allocated for publishing in open access.  
[PERSON] and [PERSON] performed this data analysis as PhD student research assistants under the guidance of their advisor [PERSON] The manuscript was prepared by [PERSON] and [PERSON] with suggestions and corrections from [PERSON]. All authors have seen and approved the final article.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Network Analysis
#### 2.3.2 Creating the Origin-Destination (OD) Cost Matrix  
After estimating the travel time and performing a set of procedures within the road network database, a network dataset was produced under the ArcCatalog module in preparation for creating the OD cost matrix that is used as a source to execute the 2 SFCA method. Nevertheless, this study used the maximum travel time, which was the 30-min drive time, as a reference to calculate the accessibility score of the MOH healthcare centers in Jeddah. This value of time was determined according to many sources indicating that the 30-min drive time is the rational time to access the healthcare service. For example, [PERSON] et al. [58] mentioned that a 30-min drive time is a reasonable value for accessing healthcare facilities in Mississippi, USA. One of the most important results of the Project of Ontario CR Pilot was that 66% of sick people could access the healthcare within a specific travel time, estimated at 30 min [59]. Furthermore, the Health Resources and Services Administration (HRSA) has considered populations traveling more than 30 min to access healthcare are at risk for inadequate healthcare [60]. In other words, these populations reside in areas that have a shortage of physicians or facilities.  
However, the tool of \"OD Cost Matrix\" is performed within the GIS environment to calculate scores of spatial accessibility within the drive-time threshold. This tool createsa dataset built by capturing all district centroids within a 30-min drive time (catchment threshold) from each healthcare center. It begins at the location of the first record of healthcare center by searching for all records of locations of district centroids that are located within the cut-off limit of 30-min drive time. This process is repeated through all records of locations of healthcare centers [34; 35]. The result of this process is a table containing all the origin-destination pairs. This table shows origins first and then destinations that are sorted from closest to farthest based on travel time. Overall, this process is an important step for calculating scores of spatial accessibility using the 2 SFCA method.
# Preparation of Carbon Nitride Dots with Sizes Larger than 20 nm
## 1 Introduction  
Carbon nitride dots (CNDs) mostly consist of carbon and nitrogen and possess similar properties to carbon dots. Due to theirlow cost, easy preparation, good biocompatibility, and excellent photoluminescence (PL), they have been explored for applications in fluorescent sensing [1; 2; 3], bioimaging [4; 5; 6], photocatalysis [7; 8], etc. The preparation of CNDs usually relies on the carbonization of C, N-containing precursors, including various organic amines/amides in the presence of acids or other additives [1; 9; 10; 11; 12], chitosan [13], folic acid [5], ionic liquid [14], etc. Besides, CNDs can also be synthesized via etching a larger carbon nitride structure such as graphitic carbon nitride (g-C\({}_{3}\)N\({}_{4}\)) into small nanoparticles [15]. However, work on the preparation of CNDs is still limited. Sizes for CNDs obtained by previous methods are generally smaller than 10 nm, and their PL reveals single blue color at ca. 360 nm excitation. A scalable method for preparing CNDs with sizes of several tens of nanometers is needed to exploit their applications. Here, we improve thesynthesis of CNDs from 1, 2-ethylenediamine (EDA) and CCl\({}_{4}\). After polymerization of EDA and CCl\({}_{4}\), polymer precursor was further heated in a tube furnace under Ar protection. After the heating of thepolymer, the size of theCNDs increased again. As a result, we obtain CNDs with sizes from 23 to 70 nm.
## 6 Security Analysis and Performance Evaluation
### Performance of Personalized FL with Multi-Level ACE-Based IoT Knowledge Sharing
Its average accuracy and the convergence rate of the loss function are close to the FedAvg with multi-level ACE-based IoT knowledge sharing on IID data. This result is satisfactory. PFL adapts to the heterogeneity of data by learning the different models trained by each client, which focuses more on the local data distribution. So, it has a better training effect than FedAvg when the data are non-IID. Moreover, the correlation degree of the local model with the global model can be adjusted by varying the value of the hyperparameter \(\lambda\), so as to achieve a more satisfactory local training accuracy. The performance comparison of FedAvg and PFL algorithms trained on IID data and non-IID data are shown in Figure 5.  
Figure 4: Performance of the proposed privacy-preserving PFL. (**a**) Average accuracy on MNIST with IID data. (**b**) Training loss on MNIST with IID data. (**c**) Average accuracy on CIFAR-10 with IID data. (**d**) Training loss on CIFAR-10 with IID data. (**e**) Average accuracy on MNIST with non-IID data. (**f**) Training loss on MNIST with non-IID data. (**g**) Average accuracy on CIFAR-10 with non-IID data. (**h**) Training loss on CIFAR-10 with non-IID data. In the setting of non-IID, we implemented the comparison experiments of the proposed PFL with multi-level ACE-based IoT knowledge sharing and the PFL without the access control scheme. The training loss curve and test accuracy curve on MNIST are shown in Figure 4,f. For clients of the secret level, the average accuracy of PFL with multi-level ACE-based IoT knowledge sharing converges to 99.51%, which is slightly better than the PFL without an access control scheme (99.06%). It can be observed that our solution converges faster on both the accuracy and the loss curve. We also implement the PFL experiment on CIFAR-10 and the effect is more pronounced. It is shown in Figure 4,h that the average accuracy of multi-level ACE-empowered PFL converges to 88.21% while PFL without access control scheme achieves 78.55%, with an improvement of 9.66%. Furthermore, the multi-level ACE-empowered PFL performs better in both the average accuracy and the loss convergence rate. The experimental results reveal that the multi-level ACE-based IoT knowledge sharing approach enables IoT users with high privacy protection levels to cooperate with other users for distributed data mining. Users can obtain a better model accuracy and convergence rate through secure IoT knowledge access and sharing regardless of whether their data are IID or non-IID.
# Synthesize and Segment: Towards Improved Catheter Segmentation via Adversarial Augmentation
## 3 Methods
### Segment: From Synthesis to Segmentation  
Figure 2 presents the segmentation networks that are utilized for the catheter segmentation. Given a synthetic X-ray image as input, the segmentation network is used  
to assign pixels to one of two classes i.e., foreground (catheter) and background (other). In this study, we consider four key architectures that are representative for segmentation tasks, (i) U-net [26], (ii) Linknet [42], (iii) PSPNet [43], and (iv) Pyramid Attention Network (PAN) [44], respectively.  
U-net [26] (Figure 2a) is a popular architecture for segmentation tasks, especially in the medical imaging domain. An encoder-decoder network comprised of contracting and expanding paths enables the learning of semantic features across feature levels. The contracting path progressively extracts image representations and increases their dimension layer-by-layer, with decoding path leveraging previous layer information for high level learning. On the other hand, Linknet [42] (Figure 2c) is a U-shape variant, different from U-net in two aspects. First, it substitutes the standard convolutions of U-net with the residual modules. Second, it uses a summation of high and low level features instead of concatenation in the decoder. In this paper, we use a ResNet50 [39] as the encoder for Linknet.  
PSPNet [43] (Figure 2b) has a ResNet-50 pre-trained backbone with dilated convolutions along with a pyramid pooling module. Conventional convolutions are substituted by dilated convolutions in the last layers of the pre-trained network, which results in an increase of the receptive field. Pyramid pooling enables the model to capture more global context in a given image with feature maps pooled at different levels and scales. Recently, [PERSON] et al. [44] proposed Pyramid Attention Network (PAN) (Figure 2d), it uses spatial pyramid and attention mechanisms to capture dense features for segmentation. Feature Pyramid Attention (FPA) and Global Attention Up-sampling (GAU) modules were proposed to improve high level feature representation via spatial pyramid pooling for global context, while GAU utilizes low-level features later attached to each decoding layer.
# Aerial Image Segmentation of Nematode-Affected Pine Trees with U-Net Convolutional Neural Network
## 3 Results
### Comparison of Model Results  
To fully understand the performance of the trained model, we compared it with other image segmentation models, ResNet50 and DeepLab v3+, in terms of training time, convergence speed, and related segmentation performance evaluation metrics, including _MIoU_, _PMA_, _Accuracy_, and _F1 Score_ in the Table 3.  
Upon reviewing the table, it is evident that the VGG-U-Net network surpasses the other two image segmentation models in terms of both training efficiency and accuracy on the validation set. Across all evaluation metrics, VGG-U-Net consistently outperforms its counterparts. To offer a more tangible illustration of the disparities between the three models, predictions were conducted using untrained images, and a comparative analysis of the prediction differences was undertaken. The results are depicted in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Training Time** & **Convergence Rate (Time)** & _MIoU_ & _MPA_ & _Accuracy_ & _F1 Score_ \\ \hline VGG-U-Net & 16 min, 32 s & 9 epochs & 81.62 & 85.13 & 99.13 & 0.885 \\ ResNet50 & 32 min, 53 s & 25 epochs & 71.5 & 74.6 & 98.78 & 0.765 \\ DeepLab v3+ & 15 min, 46 s & 18 epochs & 48.9 & 50 & 73.5 & 0.497 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of different models.  
Figure 8: The performance of different models on the test set: (**a**) VGG-U-Net, (**b**) ResNet50, and (**c**) DeepLab v3+.  
Upon careful analysis of the graph, it becomes apparent that the VGG-U-Net model demonstrates superior accuracy and precision in disease localization and tree edge segmentation. This signifies that the model is capable of more accurately pinpointing the disease location within the image and delivering clearer segmentation of tree edges. In contrast, while the other two models can also detect the disease position in the image, their segmentation of the disease and tree boundaries appears relatively blurred, potentially causing inaccuracies in localization and identification. Consequently, this finding underscores the remarkable proficiency of the U-Net model in managing intricate edge details, especially excelling in delicate aspects such as the contours of infected trees.
## 4 Experiments
### Experimental Results
#### 4.3.3 Results on MITBD, INCARTDB, SVDB, and LTDB  
In Table 9, we show the results of the generalization of our proposed source-free domain adaptation model in different scenarios. In this case, we only consider overall accuracy by combining each class, such as N, V, S, and F. Here, S and F tend to have  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fold, K** & **Sensitivity (\%)** & **Positive Predictivity (\%)** & **F1 Score (\%)** & **Accuracy (\%)** \\ \hline K = 1 & 93.2 \(\pm\) 0.6 & 93.4 \(\pm\) 0.6 & 94.2 \(\pm\) 0.5 & 92.2 \(\pm\) 0.3 \\ \hline K = 2 & 95.6 \(\pm\) 0.2 & 95.3 \(\pm\) 0.3 & 94.2 \(\pm\) 0.3 & 94.3 \(\pm\) 0.2 \\ \hline K = 3 & 96.1 \(\pm\) 0.7 & 96.7 \(\pm\) 0.4 & 96.2 \(\pm\) 0.5 & 96.8 \(\pm\) 0.3 \\ \hline K = 4 & 98.2 \(\pm\) 0.8 & 97.0 \(\pm\) 0.7 & 98.0 \(\pm\) 0.7 & 97.9 \(\pm\) 0.5 \\ \hline K = 5 & 99.1 \(\pm\) 0.5 & 99.2 \(\pm\) 0.5 & 98.5 \(\pm\) 0.3 & 98.2 \(\pm\) 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: K-fold cross validation results on DS1 \(\rightarrow\) DS2 with standard deviation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Sensitivity (\%)** & **Positive Predictivity (\%)** & **F1 Score (\%)** \\ \hline N & 98.3 & 98.7 & 99.1 \\ \hline V & 97.2 & 96.8 & 98.1 \\ \hline S & 96.2 & 95.3 & 96.6 \\ \hline F & 85.4 & 87.2 & 85.1 \\ \hline Overall Accuracy (\%) & **98.1** & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results on the original source to target domains.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DS2 \(\rightarrow\) DS1** & **Sensitivity (\%)** & **Positive Predictivity (\%)** & **F1 Score (\%)** \\ \hline N & 94.2 & 94.7 & 96.2 \\ \hline V & 96.2 & 92.3 & 92.3 \\ \hline S & 96.4 & 97.4 & 96.2 \\ \hline F & 85.6 & 86.8 & 83.2 \\ \hline Overall Accuracy (\%) & **96.5** & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results on DS2 \(\rightarrow\) DS1. Here, DS1 and DS2 are utilized as source and target domains. But in the adaptation time, DS2 (source) is not utilized. (All results shown here are **without requiring source data**).  
fewer samples, but we increase the number of samples adopting the GAN framework. We evaluate the MITDB database as the base database for the adaptation task. Therefore, at first, we use the MITDB database for our initial source training and evaluate each database such as INCARTDB, SVDB, and LTDB. We denote each of the databases as MITDB (\(\mathcal{D}_{M}\)), INCARTDB (\(\mathcal{D}_{I}\)), SVDB (\(\mathcal{D}_{S}\)), and LTDB (\(\mathcal{D}_{L}\)). Then, we also use INCARTDB, SVDB, and LTDB as source sets and MITDB as target sets for experimental purposes. In every case, we have a boost after adopting our source-free domain adaptation method, even in critical S and F classes where the samples are very less than the N and V classes. Our strategy of adopting GAN for generating more synthetic samples increases the generalization of our method.
## 5 Proposed Taxonomy of the Attacks, DDoS Attacks, and DDoS Detection Approaches on CC
### Taxonomy of DDoS Attacks on CC  
DDoS attacks on CC have rapidly risen to the top of most cyber security threats lists. Attacks on CC could affect not only the CSP but also CC resources, including VMs and the networks [52]. Nevertheless, whatever the motive of an adversary to carry out DDoS attacks, any deterioration in services offered to CCC decreases its value.  
CERT experts (a variety of researchers) say most DDoS attacks against the CERT target the application layer. The vast majority of DDoS attacks use a tremendous amount of requests of a standard communication protocol, making them hard to detect. Furthermore, they typically use well-known patterns to imitate legitimate traffic to throw off detection attempts. Therefore, standard network security strategies are not well suited to detect or prevent such attacks.  
The attacks on CC are commonly classified into five categories, as illustrated in Figure 1 and summarized in Table 5.
## 4 Analysis and Discussion  
To demonstrate experimentally the difference between a system with both conditions, a calculation using the Bitzer Inverter Scroll compressor software is firstly developed for the AHRI 1361 condition and then with the COP WEUED condition. The considerations exposed in Table 10 were used.  
AHRI conditions and thermal load:  
* Sensible cooling capacity = 50 kW;
* Total cooling capacity = 55 kW;
* Inlet air condenser temperature = 35 \({}^{\circ}\)C (AHRI 1361 conditions) [16];
* Approach between bubble temperature and condenser air inlet = 10 \({}^{\circ}\)C;
* Condensing temperature = 45 \({}^{\circ}\)C;
* Evaporating temperature = 10 \({}^{\circ}\)C;
* Suction gas superheat = 10 \({}^{\circ}\)C (EN 12900-2013 conditions);
* Liquid subcooling (in condenser) = 0 \({}^{\circ}\)C (EN 12900-2013 conditions) [17].  
Figure 6: COPs vs. annual hours of refrigeration system usage.  
Figure 7: COPs percentages.  
The COP WEUED is determined using Equation (2):  
\[\text{COP WEUED = ((0.34 \times 5.98)+(0.34 \times 5.46)+(0.2 \times 4.97)+(0.12 \times 4.43))\times 0.91 = 4.92 kW/kW}\]  
As can be analyzed from results, while the Net Sensible COP with AHRI 1361 conditions is 4.43 kW/kW (but with sensible heat equal to 4.03 kW/kW), the proposed COP WEUED index value is 5.41 kW/kW (but with sensible heat equal to 4.92 kW/kW). A considerable difference of 19% is determined, due to:  
1. With the AHRI 1361 method for CRAC equipment, it is impossible to show the difference between fixed compressors and inverter for data centers in COP evaluations.
2. It has been proven that even at fixed thermal loads, there is an advantage of an air conditioning system with a variable flow of refrigerant fluid (inverter system).
3. Just as in AHRI, there are the IPLV and NPLV that use the same formula. The main difference between them is that IPLV is based on AHRI characteristics and NPLV is based on local characteristics. COP WEUED can also be used based on local characteristics. For example, using the same methodology for the city of Sao Paulo, Equation (2) provides:  
\[\text{COP NEUED = ((0.66 \times 5.98)+(0.25 \times 5.46)+(0.08 \times 4.97)+(0.01 \times 4.43))\times 0.91 = 5.23 kW/kW}\]  
That is, in the case of Sao Paulo, the difference would be 23% to COP NEUED vs. COP WEUED. While the current NetSCOP method value was 4.03 kW/kW, COP WEUED was 4.92 kW/kW, and with specific data from the city of Sao Paulo, COP NEUED was 5.23 kW/kW, all simulated with the same inverter compressor.
## 5 Further Application Examples
### Disjoint Paths  
Let \(G\) be a graph with two distinguished vertices \(u\
eq v\). Further, let \(\mathcal{Q}_{1},\ldots,\mathcal{Q}_{n}\) be given sets of \(u-v\) paths, such that each set contains \(m\) different paths. We would like to select a path \(P_{i}\in\mathcal{Q}_{i}\) for each \(i\), such that the selected paths are edge-disjoint. Under what conditions can this be done?  
Assume the family of path systems is _diffuse_ in the following sense: any \(P\in\mathcal{Q}_{i}\) is edge-disjoint from all but at most \(k\) paths in \(\mathcal{Q}_{j}\) for every \(j\
eq i\). Let us now pick a path \(P_{i}\in\mathcal{Q}_{i}\) uniformly at random from each \(\mathcal{Q}_{i}\). Let \(E_{ij}\) denote the event that \(P_{i}\) and \(P_{j}\) are not edge-disjoint. Then, we have  
\[\Pr\left(E_{ij}\right)\,\leq\,\frac{k}{m}\]  
since, for any \(P_{i}\in\mathcal{Q}_{i}\), the other path \(P_{j}\in\mathcal{Q}_{j}\) can be chosen \(m\) different ways, and among these at most \(k\) can share an edge with \(P_{i}\), by assumption. Further, any event \(E_{ij}\) is mutually independent of all events \(E_{s,t}\) for which \(\{i,j\}\cap\{s,t\}=\emptyset\) holds. The reason is that \(E_{i,j}\) involves paths selected from \(\mathcal{Q}_{i}\) and \(\mathcal{Q}_{j}\), and these are selected independently from the paths picked from \(\mathcal{Q}_{s}\) and \(\mathcal{Q}_{t}\) whenever there is no common index, i.e., \(\{i,j\}\cap\{s,t\}=\emptyset\). This implies that \(d<2n\), where \(d\) is the maximum degree in the dependency graph of the \(E_{i,j}\) events. Therefore, if we satisfy  
\[\frac{k}{m}\,\leq\,\frac{1}{8n},\]  
then the upper bound  
\[\Pr\left(E_{i,j}\right)\,\leq\,\frac{1}{4d}\qquad\left(\forall i,j\right)\]  
holds, due to  
\[\Pr\left(E_{i,j}\right)\,\leq\,\frac{k}{m}\,\leq\,\frac{1}{8n}\,=\,\frac{1}{4 (2n)}\,\leq\,\frac{1}{4d}.\]  
It means, the \(E_{i,j}\) events satisfy the conditions of the LLL (Lemma 1), yielding  
\[\Pr\left(\,\cap_{ij}\overline{E}_{i,j}\,\right)>0.\]  
Consequently, there must exist a path system \(P_{i}\in\mathcal{Q}_{i}\), \(i=1,\ldots,n\), such that all the paths are edge-disjoint. Thus, by means of the LLL, we have proved the following result:  
**Theorem 2**.: _Let \(G\) be a graph with two distinguished vertices \(u\
eq v\), and let \(\mathcal{Q}_{1},\ldots,\mathcal{Q}_{n}\) be sets of \(u-v\) paths in \(G\), such that each set contains \(m\) different paths. Assume that any \(P\in\mathcal{Q}_{i}\) is edge-disjoint from all but at most \(k\) paths in \(\mathcal{Q}_{j}\), for every \(j\
eq i\). Then, whenever \(k\leq m/(8n)\) holds, it is possible to select a path from each \(\mathcal{Q}_{i}\) such that all the selected paths are edge-disjoint._
# Carrier-to-Noise-Threshold Filtering on Off-Shore Wind Lidar Measurements
###### Abstract
[PERSON] and [PERSON] demonstrated that the line-of-sight-velocity measurements performed by a long-range lidar over a horizontal distance of \(\sim\)3 km over the sea can be dynamically filtered to maximize accuracy and data availability of mean radial velocities. The theory has not been implemented for the horizontal velocity in a vertical wind profile and will not be considered in this study.