esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Database and Setup  
Many public databases are available with data sets of various images of internal organs for research purposes. Lung Nodule Analysis is a data set widely known as LUNA16, which has CT images of lungs. Another source is the Decathlon lung data set, having a lot of lung image data. NSCLC radio genomics are also helpful for this purpose. Data sets available with LUNA16 include three-dimensional CT images intended for lung nodule detection. The Decathlon includes three-dimensional CT images and detailed segmentation with labels. Images without pre-processing from the Decathlon lung data set were used to prepare three sets of training data sets. The NSCLC radio genomics data set includes non-small cell lung cancer CT and PET/CT images. Images with segmentation labels could only be used as test images in this system.  
The LUNA16 lung database consists of 888 CT scans with slice thicknesses less than 2.5 mm. In the LIDC/IDRI database, the presence of nodules has three categories--no nodule, less than 3 mm nodule, greater than 3 mm nodule--executed through an annotation process. GAN can be used to generate images for lung nodule detection. The same method can be used for generating a dataset for nodule segmentation. Due to the lack of labels of large true nodules, generation of data is not practical. Three-dimensional CT images corresponding to small lung nodules were considered for the generation of images having cancerous nodules.
###### Abstract  
Information about the trace elements content of goose carcass parts with or without skin can be important for consumers when making dietary choices. This study aimed to (1) determine the effects of popular heat processing techniques on the content of chromium (Cr), iodine (I), manganese (Mn), and bromine (Br) in goose breast muscle, and (2) estimate the extent to which 100 g of goose meat--both with and without skin--cover the Nutrient Reference Values (NRV) for Cr, I, Mn, and the Tolerable Daily Intake (TDI) for Br in adults. The heat processing techniques used in the study were water bath cooking (WBC), Owen Convection Roasting (OCR), grilling (G), and pan frying (PF). Grilled goose breast without skin had the highest Br retention (97.4%) and TDI (2.41%). Cooked goose breast (WBC) with skin exhibited the highest retention of Cr (73.8%) and I (73.6%). The highest Mn content was found in meat without skin after OCR processing and grilled meat with skin (0.170 and 0.191 mg/100 g, respectively). The iodine content in the meat decreased (from 0.020 raw to 0.003 mg/100 g after PF) with each heat treatment. The results of our study may provide helpful information for consumers when making dietary choices and using heat treatment techniques. Goose breast muscles, depending on heat treatment and the presence of skin, provide trace elements in the range of 2.21% of NRV (Nutrient Reference Value) for Br without skin to 740.7% of NRV for Cr with skin and may be a valuable component of a varied diet (apart from iodine). The Br content in the meat decreases after WBC treatment (1.29 without skin or 1.43 with skin mg/100 g). For this reason, it seems to be the most beneficial for the consumer's health because it minimizes the risk of exceeding the TDI value. Total hazard quotients (THQ) in meat (regardless of the treatment and skin presence) for Cr, Mn, and Br contents were \(<\)1, indicating a low risk to Polish consumer health.  
goose; meat; heat processing; trace elements; Nutrient Reference Values; Tolerable Daily Intake 2025156795
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 3 Results
### Intensive Small Area Mapping by UAV
#### 3.1.1 Reflectances of Each of the Classes  
Geometries identifying areas of water, land and weed were drawn on the 2 July C1 image. The value of vegetation indices within each of these labelled geometries was sampled. A histogram of the reflectance values for significant vegetation indices is shown in Figure 5. This indicates the potential separability of each of the classes. Clearly, it is quite straightforward to separate water from land and/or weed using any of the indices shown. It is more difficult to separate weed and land. Land includes both unvegetated areas (bare soil, road, structures, etc.) and vegetation areas such as trees and other plants. This is the reason for the bi-modal distribution of the vegetation indices of the land classes.  
These observations are quantified in Table 4, which shows the normalized mean distances (NMDs) between each of the classes. NDWI seems the best index to separate water from both land and  
Figure 4: Logger data from each of the sites. (**a**) water depth with dates on which UAV images were taken indicated with vertical lines; (**b**) the minimum and maximum daily air temperatures.
## 4 Data Processing  
In this session, the performance of the system is carried out in terms of velocity response by analyzing the signal acquired by the sensors. Theoretically, the notes generated by the organ pipe can be considered AM-modulated signals, where the carrier is the frequency generated, and the modulating signal is pressure \(P(t)\). Under this hypothesis, the audio signal generated by the organ \(s(t)\) can be written as  
\[s(t)=P(t)*cos(2*\pi*ft) \tag{1}\]  
This approximation is possible because the fundamentals of the notes generated by the organs are several dB over the harmonics. However, due to the non-ideality of the components composing the pipe organ, the actual signal modulating the carrier is a noisy and delayed version of \(P(t)\). It is defined as \(\overset{*}{P(t)}\) as this noisy and delayed signal that actually modulates the carrier. The more \(\overset{*}{P(t)}\) is similar to \(P(t)\), the more the signal generated by the organ will be similar to the desired one. For this reason, the analysis of  
Figure 11: The force applied on key recorded by oscilloscope.  
Figure 12: Displacement signal achieved during the test measurement.  
\(P(t)\) allows to estimate the performance of the system. \(P(t)\) is the envelope of the generated signal, and by definition can be estimated by the magnitude of the analytic signal \(z(t)\) of \(s(t)\), where:  
\[z(t)=s(t)+jHT(s(t)) \tag{2}\]  
where \(HT(s(t))\) is the Hilbert transform of \(s(t)\). What has just been stated makes it possible to estimate PP directly from the audio signal generated by the pipe organ computing the absolute value of the Hilbert transform of this signal.  
In Figure 13, the pressure signal P(t), the audio signal s(t), and finally \(P(t)\) are shown.  
The trend of the curves shown in the graphs confirms what was previously stated: \(P(t)\) is a noisy and delayed version of P(t). In particular, \(P(t)\) presents a certain delay compared to P(t) and the amplitude ripple.  
In order to evaluate the quality of PP, some quantities are introduced, which are the amplitude ripple (AR), the low-to-high delay (LHD), and the high-to-low delay (HLD). The AR is defined as the ripple amount of the envelope, while LHD and HLD are defined, respectively, as the time difference between the pressure's and the envelope's signals given that they reach 50% of their sustaining value starting from 0, and the time difference between these signals given that they reach 0 starting from their sustaining level. Analyzing in MATLAB, the signals generated by the pipe organ are estimated to have a mean value of LHD equal to 0.026 s, a mean value of HLD of 0.083 s, and a ripple of 16% (Figure 14).  
Figure 13: The first graph shows the pressure P(t), the second, the audio signal generated by the pipe organ, and finally the last one, \(P(t)\) estimated by the computation of the absolute value of the Hilbert transform.
## 5 Conclusions  
Based on results from this experiment, the first three flights have the highest number of unique tags scanned. The time required to scan tags repeatedly is 0.04 s per tag (spt), and the minimum distance is 0.064 m per tag (mpt) at a speed of 1.7 m/s. New tags can be scanned in the first five passes at a percentage between 70 to 95 percent. The first pass results in the most readings (30%), and gradually decreases as the number of passes increases. The decline is attributable to a reduction in the quantity of new scannable tags.  
Finally, when scanning new tags, the pass number is essential. At least two flights equivalent to four passes will get a scan rate of 70% to 90%. It takes 0.578 to 3.467 spt to scan at a speed of 1.7 m/s with a minimum distance of 0.96 mpt. It is important to note that due to the placement of plants in the field at close proximity, this experiment showed that the drone must perform multiple passes on the same location to gather an accurate inventory. This limitation has been shown in the results of this work. The distance of the tags was based on the distance between the pots in a real nursery production.  
This technology is a potential tool in obtaining automated, areal based nursery inventory data that would reduce labor hours in the field for data retrieval. Once the pandemic has subsided, we will conduct the field testing in large nurseries.  
Conceptualization, J.M.M. and J.Q.; methodology, J.Q.; software, J.N.C. and J.Q.; validation, J.Q.; formal analysis, J.Q.; investigation, J.Q.; resources, J.M.M., J.R., J.O.J., R.T.F. and M.C.; data curation, J.Q.; writing--original draft preparation, J.Q., J.M.M. and J.R.; writing--review and editing, J.M.M., J.R., J.O.J., R.T.F. and M.C.; visualization, J.Q.; supervision, J.M.M.; project administration, R.T.F.; funding acquisition, R.T.F., J.M.M., J.R., J.O.J. and M.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partially supported by Horticultural Research Institute (HRI) Grant number 5935985 and is based on work supported by NIFA/USDA under project numbers MICL02473, and SC-1700609.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank Avery Dennison Corporation and R.A. Dudley. Nurseries Inc. for their support and assistance in this project.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Blockchain-Based Distributed Patient-Centric Image Management System
## 5 Evaluation
### Experimental Setup  
A POC design of the PCIM system was developed to test and evaluate its performance. The experiment was conducted using a Windows 10 desktop with an Intel(r) Core TM 15-600 processor at 3.30 GHz. PCAC-SC was implemented in the remix IDE using Solidity programming language [31]. We deployed the program within the private Rinkeby test network using MetaMask [31]. This test network allows us to verify and optimize the prototype before implementing in a public blockchain. We initialized IPFS using go-ipfs [32] and uploaded an encrypted medical image to the IPFS network from a local computer. This operation returned a unique hash value linked to the uploaded medical image. Thereafter, we updated transactions on the blockchain using _create_contract()_ function by defining the IPFS hash, patient Ethereum public key, and the basic medical image description. Once the block was approved, transactions were stored in the blockchain.  
The complete prototype code of PCAC-SC is published in our GitHub repository [33]. The contract deployed on the test network has the following address:  
\[0x5575805E19b4807974 Be0B77 Fd9d385D4A0e6d1E\]  
Transactions on each function can be seen using the above address at the Rinkeby Etherscan website [34].  
Figure 4 illustrates such parameters as the block/timeline, functions, and event sequence defined in the PCAC-SC protocol for granting and revoking permissions between a patient and image request \(IR\)s entities. To allow for better understanding of this access sharing sequence, we consider an example of two \(IR\)s: a doctor (\(IR_{1}\)) and a general practitioner (\(IR_{2}\)) who is interested in accessing a patient medical image.  
The patient executes _create_contract()_ function by signing the blockchain contents (see Section 4.4). This function allows \(IR_{1}\) and \(IR_{2}\) to participate by calling the request function in the PCAC-SC protocol defined by the patient. Each of the entities has its own Ethereum address to perform the operations.  
In Figure 4, blocks from 2-7 illustrate the access privilege scenario. \(IR_{1}\) and \(IR_{2}\) send a request to access medical images using _request_access()_ function that is represented by block 2 and block 3. block 4 and block 5 show that the patient is able to grant and deny the access by using the _approve_IRs()_ function. In Figure 4, block 6 depicts an event that \(IR_{1}\) authorized to access image, since in the block 4 \(IR_{1}\) image request accepted by the patient. Thus, block 6 represents the message sequence of _trace_authorization()_ function, which is used to trace the history of approved and disapproved events \(IR_{2}\) of the image requestors. In Figure 4, block 7 illustrates revoking the permission of \(IR_{1}\) by calling _remove_IRs()_ function, which can be executed only by the patient. The details on execution of each function are stored in the blockchain as an event to help the participants to keep track of their transaction details.
## 4 Experiments
### The Performance of User Preference Learning  
First, we compared the performance of three classification models: TextCNN, ImageCNN, and early fusion of the two. The training data in Dataset_1 were used to build the models, and the test data in Dateset_1 were used to evaluate the models. In the dataset, there were only user-defined hashtags in addition to texts and images in user posts. It is difficult to know user preferences without explicit user feedback. To obtain the ground truth for user preferences, the hashtag that was used to retrieve each post was set as the class label of that post. We assumed that users implicitly express their preferences through the use of hashtags in the posts and comments that they are interested in.  
In our ImageCNN model, the training procedure generally followed [PERSON] et al. (2015). In this model, we included three convolutional layers with the kernel size set to 5 and strides = 1. The training was regularized by weight decay (the L2 penalty multiplier was set to \(5\times 10^{-4}\)) and dropout regularization for the first two fully connected layers with a dropout rate of 0.2. The learning rate was initially set to 0.1. Rectified Linear Unit (ReLU) and Root Mean Square Error (RMSE) were used as the activation function and loss function. The final layer is the softmax layer. The performance of the ImageCNN model is shown in Figure 7.  
As shown in Figure 7, when the dimension was 4096, we could obtain the best performance with an accuracy of 98.89%. Since the pretrained model of VGG16 had dimensionality in multiples of 1024, models larger than 4096 could not be run due to the limitation of our computer hardware. In the subsequent experiments, we used the dimension of 4096 for the ImageCNN model.  
In our TextCNN model, the training procedure generally followed [PERSON] (2016). We used Word2 vec for embedding text contents as input. Three filters of window sizes 3, 4, and 5 were tested, the stride was 1, the dropout rate was set to 0.5, the L2 constraint was 3, and the mini-batch size was set to 50. The learning rate was set to 0.1. We applied a max pooling operation. Training was done through stochastic gradient descent over shuffled mini-batches with the Adadelta update rule ([PERSON] et al., 2015). RMSE and LeakyReLu were used as the loss and activation function, respectively. We chose an adaptive learning rate method for neural networks proposed by [PERSON] called RMSPROP as the optimizer. The performance of the TextCNN model is shown in Figure 8.  
As shown in Figure 8, when the dimension was 2100, we could obtain the best performing TextCNN model with an accuracy of 96.8%. In the subsequent experiments, we used the dimension of 2100 for the TextCNN model. Then, when texts and images were input at the same time, we applied early fusion as shown in Figure 3. The best performance with an accuracy of 80% could be observed when the dimension was 4096. According to our observations, the lower accuracy might have been due to the number of classes in multiclass classification.  
Figure 7: The accuracy of the ImageCNN model.
# Blockchain Technology for Secure Communication and Formation Control in Smart Drone Swarms
## 4 Materials and Methods
### Tools Deployed for the Experimental Setup  
All the tools used to test the functionality of the proposed smart contract are open source and are described briefly below.  
Ganache ([[https://trufflesuite.com/ganache/](https://trufflesuite.com/ganache/)]([https://trufflesuite.com/ganache/](https://trufflesuite.com/ganache/)), accessed on 20 July 2023) is a tool for the creation of local blockchain networks that helps in the development and testing of smart contracts. It can be used as an Ethereum blockchain alternative and can be deployed quickly, easily, and locally in the developer's test machine. It is lightweight and offers a variety of options that can be set according to the developer's needs (changing, for example, the block creation time). By adjusting these parameters, one can achieve a more realistic simulation.  
Gazebo ([[https://gazebosim.org/home](https://gazebosim.org/home)]([https://gazebosim.org/home](https://gazebosim.org/home)), accessed on 20 July 2023) is a 3D robotics simulator. Its accuracy regarding real-world physics makes it the best option for a developer  
Figure 7: Follower’s functionality flowchart.  
who wants to test a robotic system without risking breaking real hardware. Additionally, the library of tools it provides (sensors, lidars, cameras, indoor and outdoor world creation) makes the simulation easier and more realistic. The use of Gazebo for this work can provide us with a visual result of how the UAVs would coordinate in a real-life scenario.  
PX4 ([[https://docs.px4.io/v1.12/en/simulation/gazebo.html](https://docs.px4.io/v1.12/en/simulation/gazebo.html)]([https://docs.px4.io/v1.12/en/simulation/gazebo.html](https://docs.px4.io/v1.12/en/simulation/gazebo.html)), accessed on 20 July 2023) models were used for achieving a simulation of a real iris quadrotor drone in Gazebo for the needs of this work. Also, the MAVSDK ([[https://mavsdk.mavlink.io/main/en/python/quickstart.html](https://mavsdk.mavlink.io/main/en/python/quickstart.html)]([https://mavsdk.mavlink.io/main/en/python/quickstart.html](https://mavsdk.mavlink.io/main/en/python/quickstart.html)), accessed on 20 July 2023) Python library was used to control the behavior of the swarm and coordinate their actions. This particular library is typically used in real-world drone programming applications.  
In addition to these tools, Python was used for the development of the DApp, including the web3 module for interaction with the smart contract. A variety of options are available for drones through the DApp, such as reading data from the blockchain, submitting data, checking leader status, reading mission details, checking position availability in the formation, selecting and submitting the desired position, etc. (Figure 8).
## 4 Methodology and System Design
### VoteChain: Block's Structure  
The blockchain architecture of VoteChain is based on Distributed Ledger Technology (DLT) to ensure a secure, transparent, and immutable electronic voting process. Each block in the blockchain plays a key role in safeguarding the integrity and security of the voting process. These blocks contain several essential elements (see Figure 2) that collectively contribute to a tamper-resistant and verifiable voting system.  
Voter ID is a unique identifier randomly assigned to eligible voters, enabling accurate identification without revealing personal details, thus preserving voter anonymity. The vote represents the voter's choice and is securely recorded within the blockchain, ensuring that every ballot is cast and stored with the highest security measures. The vote's hash is signed using the voter's private key, which allows for verification of the vote while keeping the vote's content private.  
The timestamp records the exact moment each block is submitted, helping to resolve conflicts when multiple blocks have similar timestamps, ensuring fairness.  
The digest (Hash) of the Previous Block connects each block to the one before it using the SHA-256 algorithm, ensuring that the chain remains tamper-proof. Any alteration of a block would immediately be detectable due to the break in the cryptographic link.  
Figure 2: Voting Block.
# Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities
## 3 Hardware-Assisted Dynamic Information Flow Tracking
### Tagged Memory Architecture  
[PERSON] et al. in [PERSON] et al. (2018) proposed a complete tagged memory architecture to support information flow tracking and security analysis. A single-bit tag shadow space is used to record the metadata at runtime. An in-DRAM tag table and tag cache are utilized to manage the tag in the whole system. [PERSON] et al. in [PERSON] et al. (2018) presented a new tagged memory architecture to combine compartmentalization (e.g., Intel SGX (2018)) and isolated execution environments (e.g., TrustZone (2018)). Two extra tag bits are utilized to differentiate privilege levels and security domains. A dedicated piece of hardware serves as the trust root to enforce security policies in terms of tag and finish security domain switching. Additionally, extra tag-aware instructions are added to support flexible tag management.