esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Results
### Performance of DLR+  
In this subsection we discuss the final performance of DLR+ (\(\alpha=0.65/0,\ \beta=0\)) and compare it against other well-known routers (Figures 7 and 8).  
As can be seen in Figure 7a, DLR+ (\(\alpha=0.65\)) offers a greater PDR than the epidemic router and PRoPHET for TTL greater than 60 and 130, respectively. Although its performance on this metric is not the best, it is very close to those who offer the best values, only about 6.07% below its better counterparts. On the other hand, with \(\alpha=0\), DLR+ outperforms all routers in PDR for TTL \(<25\). This reflects an interesting dynamic in the response of DLR+ for this case, in contrast with other routers: The more TTL is provided, the more inefficient the router becomes; however, as TTL is smaller, the response of the proposed router increases, outperforming the other routers in this metric. There is a tradeoff, nonetheless, in this range of operation, because in this part, DLR+ (\(\alpha=0\)) does not have the best performance in network overhead and hop count (Figure 8), although it shows acceptable values, very close to the ones generated by other routers.  
As for delays, in the long run, DLR+ does not provide the best performance on average delivery delay (Figure 7b). We can see that as the TTL increases, so does the delivery delay values, and although they tend to stabilize at some point, there are significant differences with respect to other routers'  
Figure 8: Performance of DLR+ in network overhead (**a**) and hop count (**b**).  
Figure 7: Performance of DLR+ in packet delivery ratio (**a**) and average delivery delay (**b**).  
performance. The proposed router, however, performs fairly well for small TTL values, laying in points very close to those resulted from their counterparts, with roughly the same ADD values than those of other routers for TTL \(\leq 25\).  
In network overhead (Figure 8a), DLR+ (\(\alpha=0\)) did not have the best results, with significant differences with respect to their counterparts, closely resembling the epidemic routing. For \(\alpha=0.65\), however, DLR+ had the best performance, with nearly zero overhead, which means extremely efficient resource usage, way below the OVH values returned by other routers.  
In hop count, on the other hand, with \(\alpha=0\) the number of hops used by DLR+ is very close to a constant 1.6 in the long run, which shows better values than other routers. Indeed, for TTL \(>50\), the proposed router (\(\alpha=0\)) outperforms all other routers in the experiment, but even for TTL values smaller than 50, the number of hops used by DLR+ is between 2.2 and 2.8, which is a range in which all other routers lie as well. For \(\alpha=0.65\), however, the proposed router shows an impressive HOP of nearly 1, which is a very significant difference with respect to the rest, confirming the highly efficient usage of network resources.
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 1 Introduction
In Section 2, we introduce the historical development history, evaluation metrics, prototypes, and key technologies of ISAC. In Section 3, we present the development status, key technologies, and network architecture of ISTNs. In Section 4, we introduce the possible application scenarios and key technologies in the future ISAC towards 6G ISTN system and present our perspectives on the evolution direction of ISAC technology towards ISTNs. In Section 5, we present potential research directions for the future development of ISAC towards ISTNs. Finally, in Section 6, we present our concluding remarks.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 6 Real Shipping Movement Model
### Results and Discussion
#### 6.8.3 Normalized Routing Load on Realistic Shipping Model  
The normalized routing load graph for each fixed station is shown in Figure 15. The network routing loads produced by each protocol follows a predictable pattern in this model. Obviously, when no connection is made (PDR is zero), the NRL is zero as well. Once there are connections being made, the NRL shows some dependency on the radio range, but this is by no means conclusive. For each protocol and base station target, there are anomalies where a higher radio range gives a higher (rather than a lower) NRL. However, across all the runs, the relative routing loads of the three protocols are clear. In all cases, DSR generates the least routing traffic, with a range from 1.8% (under 2 routing packets per 100 data packets delivered) to 16.75%. AODV yields the best performance of 18.64%, which is still a higher routing load than the worst from DSR. The highest AODV routing load is 44.47%, equivalent to nearly one routing packet for every 2 data packets. DSDV generates a minimum NRL of 127.51% and a maximum of 173.78%, so DSDV requires well over 1 routing packet per data packet delivered. It appears that the proactive DSDV protocol is generating a lot of routing traffic attempting to establish routes that are not necessary. Due to the fact that the model requires data to be delivered to only the four base stations, any routes set up to other nodes, in particular, nodes that are not on route to any of the base stations, are redundant and establishing them will incur resource waste. DSDV is a proactive routing protocol, thus, it tries to establish routes between all possible combinations of nodes and therefore generates a large routing packet overhead. Given the high NRL and no significant difference in PDR, it is unlikely that DSDV would be used in this scenario.
# IoT-Portrait: Automatically Identifying IoT Devices via Transformer with Incremental Learning
## 5 Experimental Results and Analysis
### Device Identification of Large Scale  
To further prove the effectiveness of the transformer model, we made another performance evaluation on the IoT Trace dataset [22] with more types of devices and a more comprehensive data volume. In the IoT Trace device network dataset provided by UNSW, there are 21 IoT devices belonging to different major categories. This dataset's amount of single-day data varies from 61 MB to 2 GB and is 365 MB on average.  
We observed that six of the IoT devices have too little daily traffic. Hence, we abandoned those six devices for their uniform amount of packets and finally selected the remaining 15 devices for the following experiment. The list of IoT devices we used is shown in Table 2. In our experiment, the size of the training \(mini\_batch\) is set to 128, and the epoch is set to 20. After each epoch, the training accuracy and loss of both the test and validation sets can be obtained. This experiment's training, verification, and test sets are divided based on a ratio of 6:2:2. For input data specification, we set the packet length to 31 bytes and the number of packets to 20. After training, we used the epoch number as the variable and plotted the training process of our model with accuracy and loss.  
As shown in Figures 5 and 6, as the epoch increases, the model's overall accuracy continues to improve, and the loss rate decreases. Finally, both of them converge and become stable. In addition, since the target is to evaluate the multi-classifier, we separately test the data of all 15 devices and generate a confusion matrix, shown in Figure 7. The confusion matrix indicates a good recognition result since most of the elements are gathered on the diagonal of the matrix, and all devices have an accuracy of over 96%. Even though there are devices of the same type in the dataset that may have similar network behaviors, IoT-Portrait can still identify them well.  
\begin{table}
\begin{tabular}{c c} \hline
**IoT Device** & **MAC Address** \\ \hline Amazon Echo & 44:65:0d:56:cc:d3 \\ Belkin Weno Motion Sensor & ec:1a:59:83:28:11 \\ Belkin Weno Switch & ec:1a:59:79:48:89 \\ Dropcam & 30:8c:fb:2f:e4:b2 \\ HP Printer & 70:5a:0f:e4:9b:c0 \\ Netatmo Weather Station & 70:ee:50:03:b8:ac \\ Netatmo Welcome & 70:ee:50:18:34:43 \\ PIX-STAR Photo-frame & e0:76:d0:33:bb:85 \\ Samsung SmartCam & 00:16:6c:cab:6b:88 \\ Smart Things & d0:52:a8:00:67:5e \\ TP-Link Day Night Cloud Camera & 4f:4f:26:d9:51:f1 \\ TP-Link Smart Plug & 50:76:f9:05:39 \\ Triby Speaker & 18:b7:9e:02:20:44 \\ Withings Smart Baby Monitor & 00:24:e4:11:18:a8 \\ Withings Smart Scale & 00:24:e4:1b:6f:96 \\ \hline \end{tabular}
\end{table}
Table 2: The list of IoT devices used in the experiment includes not only different types of devices but also devices of the same type from different manufacturers.  
Figure 5: The accuracy of training and verification sets.  
Figure 6: The loss of training and verification sets.
###### Abstract  
The utilization of Building Information Modeling (BIM) on-site has been limited due to the lack of an appropriate medium for visualizing and accessing models and project data intuitively in the field. AROS, an Augmented Reality-based site management system, was developed to allow the visual projection of BIM models and relevant data directly to field personnel. Detailed field experiments with inspection experts revealed specific issues with the stability and accurate placement of model projections in AROS. Projection stability was improved by reducing the number of triangle meshes of the model to relieve the need for processing power. Investigations revealed that simplification rates of 40% and 20% were optimal for rectilinear and curvilinear components, respectively. Projection placement was improved by implementing a hybrid of target anchoring methods. Specifically, ARWorldMap was used to add additional anchor points, which were identified from the topologies of the structure and surrounding planes. Post-evaluations demonstrated increased stability and reductions in displacement errors. The formalizations provide measures for using AR and BIM models when applying these technologies to large-scale civil infrastructure projects.  
building information modeling; augmented reality; site management; projection stability; placement accuracy
# Monocular Depth Estimation Based on Dilated Convolutions and Feature Fusion
## 4 Experiments and Results
### Experimental Environment and Parameter Settings  
The PyTorch deep learning framework was used to run all experiments on a 64-bit Core i9-9900k CPU (Santa Clara, CA, USA) running at 3.60 GHz, with 32 GB of memory, and an NVIDIA GTX 2070 SUPER GPU (Santa Clara, CA, USA) under Win10 (Redmond, WA, USA). The network parameters underwent updates through the Stochastic Gradient Descent (SGD) optimization strategy, initialized with a learning rate of 0.0001 and a momentum value of 0.9. The network adopted a learning rate strategy based on polynomial decay, with a batch size of three and an epoch of 10,000 for the network. The KITTI Dataset was divided into 80 depth intervals, while the NYU dataset was divided into 68 depth intervals.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Advantages** & **Disadvantages** \\ \hline \multirow{2}{*}{RMSE} & Capable of quantifying the absolute & \multirow{2}{*}{Highly influenced by outliers and} \\  & discrepancy between the predicted & \\  & values and ground truth & incapable of quantifying relative error \\ \hline \multirow{3}{*}{RMSELog} & The utilization of logarithmic & \multirow{3}{*}{Unable to quantify the relative error} \\  & transformation on the Root Mean Square & \\  & Error (RMSE) diminishes the influence & \\  & of outliers & \\ \hline \multirow{3}{*}{Abs Rel} & Capable of assessing the relative error & \multirow{3}{*}{Unable to quantify the absolute error} \\  & between the predicted values and the & \\  & ground truth. & \\ \hline \multirow{3}{*}{Sql Rel} & Applying the square operation to the & \multirow{3}{*}{Unable to quantify the absolute error} \\  & Abs Rel metric enhances & \\  & the distinction & \\ \hline \multirow{3}{*}{Acc} & A visual representation of the accuracy & \multirow{3}{*}{Inability to quantify the deviation of the deviation between the predicted value} \\  & of the predicted values & \\ \cline{1-1} \cline{4-4}  & & and the ground truth \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative analysis of model performance metrics.
## 4 The Proposed Hybrid Approach for Intrusion Detection
### Training Classifier  
The method for training classifier is a two-phase hybrid approach: unsupervised clustering and supervised learning. First, the unsupervised clustering algorithm MDPCA is utilized to divide the original training dataset into K subsets \(DC_{1},DC_{2},\cdots,DC_{K}\). Next, the DBNs initialize the network parameters through the unsupervised pre-training of BernoulliRBM, and then use the back-propagation algorithm to fine tune the network connection weights. We use the K subsets \(DC_{1},DC_{2},\cdots,DC_{K}\) from the training dataset to train the K sub-DBN classifiers. Each subset \(DC_{i}\) (where \(i=1,2,\cdots,K\)) is assigned to train the corresponding \(DBN_{i}\) (where \(i=1,2,\cdots,K\)). These DBNs are different from each other because they have been trained on different subsets. Each DBN has one input layer, three hidden layers, and one output layer. Three hidden layers automatically learn features from each training subset. The network structures of each DBN in the NSL-KDD and UNSW-NB15 datasets are 122-40-20-10-5 and 196-80-40-20-10, respectively.
# Advancing Biological Research: New Automated Analysis of Immunofluorescence Signals
## 3 Results
### Q-IF Software Flow Description
#### 3.2.2 ROIs Cropping  
ROIs are cropped to remove the background, as shown in Figure 4, where cropped ROIs derived from original images in both cellular experimental sets are illustrated. Q-IF achieves this operation by using a developed function that takes the path of the ROIs as input. The function starts by reading the image using the OpenCV _inread()_ function and opening the same image using the Python Imaging Library (PIL).  
Figure 4: ROIs identification and cropping in IF images. **(A)** Representative image of SH-SYSY \(\mathrm{H_{2}O_{2}}\)-treated cells and subjected to confocal analysis for the Nrf2 protein. **(B)** Images obtained from the calculation and cropping of the ROIs present in Figure A. **(C)** Representative IF of 8-OHdG in skin fibroblasts after \(\mathrm{H_{2}O_{2}}\) stimulation. **(D)** Respective images are derived from the cropping process performed by the Q-IF tool. All cells are identified, cropped, and shown as examples.  
The image is then converted to grayscale using the _cv2.cvtColor()_ function. The function applies thresholding on the grayscale image using a custom threshsold function to create a binary image, which is formalised in the formula below:  
\[\textit{threshold}=\sqrt{\bar{x}+s^{2}} \tag{1}\]  
where (\(\bar{x}\)) indicates the mean of the pixel values and (\(\sigma\)) stands as the standard deviation.  
The use of this combination of mean and SD performs well, allowing the consideration of both central tendency (mean) and variability (SD), effectively classifying pixels with higher intensity as the cellular region and those with lower intensity as the background.  
Next, the function splits the original image into its three colour channels (red, green, and blue) and the alpha channel, which contains the binary image created earlier. Please notice that images are encoded in RGB as the accepted standard. It creates a list of the four channels and merges them using the _cv2.merge()_ function to obtain a new coloured/multi-channeled image.  
Finally, the function saves the new image using the _cv2.imwrite()_ function and returns the path of the saved image. The path includes a new directory called _gennetedandcropped_, and the file name includes the string \"crop\" at the end.  
Cropped ROIs are saved as.tiff files and subsequently targeted by the quantification algorithm, which has been computed by using the integrated density (ID) as the sum of all pixels within a region; indeed, a plethora of evidence has shown that ID is a reliable measure in this field [14; 15].
## 1 Introduction
### Structure of the Paper  
Following this introduction, Section 2 explores the fundamentals of FSS and various design strategies, correlating different approaches with their respective performances. Section 3 classifies previously published FSS designs into a taxonomy of cross cell, patch cell, patch cell with additional elements, loop/ring, and meandered cell, as outlined in [9]. Section 4 discusses the potential for harvesting the power blocked by the FSS to generate electricity for building facilities: a process known as energy harvesting. Section 5 analyzes the critical issues and challenges of incorporating FSSs into real-world construction. Finally, Section 6 summarizes the benefits of integrating FSSs into building envelopes.
# A System View to the Risks of COVID-19 Vaccination Projects
## 1 Introduction  
From November 2019 until November 2021, the World Health Organization (WHO) has received reports of more than 228,807,630 confirmed cases of Coronavirus Disease (COVID-19), with more than 4,697,095 deaths [1]. Up to now, public vaccination is the most important means of controlling the COVID-19 pandemic. Within just under a year from the start of the pandemic, researchers developed vaccines against COVID-19. These vaccines, such as Pfizer, Moderna, AstraZeneca, Sputnik V, Covishield, Ad26.COV2.S, Sinopharm, Sinovac, and Bharat are still in the development process and have authorization only for emergency use. Now it is time to make these vaccinations available to individuals worldwide. To control the pandemic, individuals in all countries need to get vaccinated. However, authorities worldwide face the challenge of vaccinating the public as rapidly as possible [2]. By January 2022, 9.42 billion doses of COVID-19 vaccines had been administered globally [3]. Although vaccination policies differ, in general, by the 8 th of January 2022, approximately 59.1% of the world's population had taken at least one dose of a COVID-19 vaccination [4]. However, this worldwide statistic conceals significant disparities between continents and income groups, as vaccine doses have been delivered unevenly with low-income countries lagging behind [3]; just 2% of individuals in low-income nations have received at least one dosage [4]. These statistics emphasize the need for an efficient vaccine delivery project and make it even more critical to reduce the global supply chain risks in these regions in order to save lives, prevent additional costs and minimize any potential for project failure [5].  
For the past 50 years, the supply chain methods for distributing vaccines have remained essentially unchanged, with only minor improvements [6]. The results are the suboptimal vaccination projects witnessed during the COVID-19 pandemic. Many countries have faced a multitude of difficulties at various stages of vaccine distribution [7]. Moreover, many regions have struggled to meet the required service level standards due to inadequate risk management and vaccine supply chain breakdowns [2]. Although vaccination is a medical intervention, successful public vaccination programs are impossiblewithout a reliable supply chain [8]. Lowering the risks related to vaccine delivery has never been more of a global concern than it is now [9]. The scientific community has recently shown an increasing interest in the logistical elements of vaccination, but the majority of published studies still focus on only a single logistical component [8]. Thus, the primary aim of this article is to propose a framework that conceptualizes and unites the related risks and concerns of the entire vaccine supply chain that may be beneficial to COVID-19 mass vaccination policymakers and authorities.
# Robust 3D Joint Inversion of Gravity and Magnetic Data: A High-Performance Computing Approach
###### Abstract  
One of the fundamental challenges in geophysics is the calculation of distribution models for physical properties in the subsurface that accurately reproduce the measurements obtained in the survey and are geologically plausible in the context of the study area. This is known as inverse modeling. Performing a 3D joint inversion of multimodal geophysical data is a computationally intensive task. Additionally, since it involves a modeling process, finding a solution that matches the desired characteristics requires iterative calculations, which can take days or even weeks to obtain final results. In this paper, we propose a robust numerical solution for 3D joint inversion of gravimetric and magnetic data with Gramian-based structural similarity and structural direction constraints using parallelization as a high-performance computing technique, which allows us to significantly reduce the total processing time based on the available Random-Access Memory (RAM) and Video Random-Access Memory (VRAM)and improve the efficiency of interpretation. The solution is implemented in the high-level programming languages Fortran and Compute Unified Device Architecture (CUDA) Fortran, capable of optimal resource management while being straightforward to implement. Through the analysis of performance and computational costs of serial, parallel, and hybrid implementations, we conclude that as the inversion domain expands, the processing speed could increase from 4\(\times\) up to 100\(\times\) times faster, rendering it particularly advantageous for applications in larger domains. We tested our algorithm with two synthetic data sets and field data, showing better results than standard separate inversion. The proposed method will be useful for joint geological and geophysical interpretation of gravimetric and magnetic data used in exploration geophysics for example minerals, one, and petroleum search and prospecting. Its application will significantly increase the reliability of physical-geological models and accelerate the process of data processing.  
joint inversion; gravity and magnetic anomalies; geophysical exploration; applied geophysics; 3D inversion +
Footnote †: journal: Applied Sciences