esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 4 Review of [PERSON]'s Scheme
### Mobile Device Registration Phase  
The mobile device chooses an identity (\(ID_{M}\)), password (\(PW_{M}\)), and random number (\(r_{M}\)). Then, the device computes \(B_{M}=h(h(ID_{M}\parallel r_{M})\oplus h(PW_{M}\parallel r_{M}))\) and sends \(\{B_{M},ID_{M}\}\) to the GCS via a private channel. The GCS confirms whether \(ID_{M}\) is in its database. If \(ID_{M}\) is in the database, the GCS transmits a message to the mobile user to choose a unique identity for the mobile device. Otherwise, the GCS selects two random numbers (\(r_{1}\) and \(r_{2}\)), computes \(C_{M}=h(ID_{M}\parallel r_{1}),D_{M}=h(C_{M}\parallel k\parallel r_{2}),E_{M }=h(C_{M}\parallel B_{M}\parallel r_{1}\parallel D_{M})\) and \(F_{M}=h(E_{M}\parallel C_{M})\), stores \(\{C_{M},D_{M},ID_{M}\}\) in memory, and sends \(\{F_{M},D_{M},r_{1},h(\cdot)\}\) to the mobile device for storage in its memory.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Definition** \\ \hline \(ID_{D}\) & Drone identity \\ \(r_{D}\) & Drone random number \\ \(k\) & Secret values \\ \(PK_{D}\) & Drone public key \\ \(ID_{M}\) & User identity \\ \(PW_{M}\) & User password \\ \(r_{M}\) & User random number \\ \(r_{1},r_{2},r_{3},r_{4},r_{5}\) & Random nonce \\ \(T_{1},T_{2},T_{3},T_{4},T_{5}\) & Timestamp \\ \(h(\cdot)\) & Hash function \\ \(\parallel\) & Concatenation function \\ \(\oplus\) & XOR operation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation of [PERSON]’s scheme.
## 3 Experiment
### Simulation Experiment
#### 3.2.2 Circular Phantom  
In the circular phantom simulation experiment, we set the parameters of the algorithms as follows: \(\lambda_{\text{SIRT}}=1.6\), \(\lambda_{r\text{\tiny{ord}}}=0.995\), \(\varphi=1\), and \(N_{\text{iter}}=600\). As for the SIRT-TV algorithm, \(\lambda_{TV}=1.0\). As for the SIRT-WTDM algorithm, \(\omega=0.00085\). As for the SIRT-MWTDM algorithm, \(\gamma=0.6\) and \(k=90\). The reconstructed images obtained by using the five algorithms with different numbers of projections are shown in Figure 6.  
From Figure 6, we can see that in the case of the same number of projections, the images reconstructed by using the SIRT-MWTDM algorithm have the best reconstruction effect. The outline of each circle from the reconstructed image can be seen clearly by using the SIRT-MWTDM algorithm with 30 projections. Even if there is Poisson noise in 60 projections, we can still see the position and contour of each circle. From the visual point of view, the reconstructed image obtained by our algorithm is smoother and has less noise for 60+ projections.  
In order to quantitatively analyze the reconstructed results of the different algorithms, the histograms of three indexes (SSIM, PSNR, and RMSE) of the reconstructed image of the circular phantom are shown in Figure 7. From left to right, the histograms respectively represent the SSIM, PSNR, and RMSE of the reconstruction results of different algorithms for different numbers of projections. The SSIM and PSNR of five different algorithms show that the SIRT-MWTDM algorithm has the highest SSIM and PSNR in the same number of projections. The SIRT-MWTDM algorithm has the lowest RMSE in the same  
Figure 6: Reconstructed results of the circular phantom obtained by using different algorithms with different numbers of projections. Images from left to right show reconstructed results from 30, 60, and 60+ projections, respectively. Here, 60+ indicates that Poisson noise is added to 60 projections. Rows from top to bottom are reconstructed by using FBP, SIRT, SIRT-TV, SIRT-WTDM, and SIRT-MWTDM, respectively.  
condition of projections. Therefore, Figure 7 shows that the image reconstructed by using the SIRT-MWTDM algorithm has the strongest noise suppression and the best performance.  
To further investigate whether the performance of the proposed algorithm is better, we plot the profile of the red line position in Figure 2b as shown in Figure 8. From left to right, the number of sparse-view angles is 30, 60, and 60+. Here, 60+ indicates that Poisson noise is added to 60 projections. It can be seen from Figure 8 that the profile of the image reconstructed by using the SIRT-MWTDM algorithm is the closest to the original profile. In addition, the algorithm well protects the edge of the original image.
# Tourist Behaviour Analysis Based on Digital Pattern of Life--An Approach and Case Study
## 4 Tourist Behaviour Analysis Case Studies
### Time Series Prediction  
This case study describes a model that predicts events that occur at a specific time interval. One possible example in which this model can be used to predict regional budget replenishment based on the tourist spending, which can be extracted using the digital pattern of life. Another possible example is predicting the seasonal visit of a particular attraction.  
As a solution, it was decided to use a bidirectional long short term memory network [44; 45]. Long short-term memory neural networks are a sub type of recurrent neural networks that can predict the next values in sequences. However, recurrent neural networks may have a problem in the form of the impossibility of deep learning with large volumes of the initial sequence--the neural network \"forgets\" the previous states. Neural networks with long short-term memory are capable of learning long-term addictions due to a different arrangement of internal cells for storing data. A LSTM cell includes 4 layers--a forgetting filter (determines what information can not be considered in future layers), an input filter layer (decides what information will be remembered in the cell state), a state cell layer (stores calculated data) and an output layer (layer that passes the converted computed data to the next cell). Conventional LSTM networks work only with the previous internal state, bidirectional neural networks take into account both previous and future states in their work, thereby improving the overall predictive ability.  
The architecture of the model predicting temporal events consists of the following sequential layers: a bidirectional layer consisting of 256 LSTM cells; dropout layer that excludes 20% of random neurons at different learning epochs (necessary to prevent overfitting on the training sample) and the  
Figure 4: Tourist clustering results.  
final output layer, consisting of one neuron, which will store the prediction result. The function of the root mean square error is used as a loss function, and the adaptive moment estimation method is used as an optimization function. To train the model data on site visits from 2016 to 2020 was extracted, broken down by day. In total 1703 values were selected, of which the test sample was 1313 values, the validation was carried out on 170 examples, the final testing included 220 values. Before training the model, the value of the number of visits was normalized. The training was carried out for 200 eras. The result of model validation is shown in Figure 5. Blue color shows real values, red--model prediction. From this result it can be concluded that the model follows trends in changes in data, often following the data spikes.
# Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks
## 5 Performance Evaluation
### Experiment Specification  
Regarding the simulation scenarios and data, we mainly refer to the datasets of Next Generation Simulation and Mirror-Traffic, consisting of various vehicle types, road types, and road conditions. Accordingly, we then designed the simulation model suitable for this work by analyzing its data statistics and characteristics. Since it is mainly aimed at EVs crowdsourcing solutions in a finite geographical area, some of the simulation parameters are: the number of vehicles selected is \([50,200]\), the reputation threshold is \([-1,1]\), reputation fluctuation threshold is \([0.10,0.30]\), and the initial reputation record of the vehicle miner is randomly selected within the given reputation range.  
Furthermore, interaction parameters refer to operations (e.g., data reception and transmission), which are fed back communication parameters such as delay and throughput. Thus, the interaction parameters are quantified as system security performance and efficiency. In detail, the honest mining ratio is assessed based on the judgment of miners' historical behaviors and the reputation increment. While continuing to increase in difficulty, the act of solving crowdsourcing tasks (i.e., mining) is more efficient. It is utilized to express the probability of honest miners. In each mining round, reputation fluctuation mainly determines the upper and lower limits of the fluctuation value of reputation, which can reflect the reliability of the mining behavior within a certain mining period. Further, it can also reflect the extent of historical reputation changes. In addition, the level of spatial reputation is generated from comprehensive reputation, distance factors, etc., to express the degree of the integrity and reliability of transactions. We then define it as an indirect response to the authenticity. That is, higher reputation and reliability result in strong authenticity. Moreover, data performance can be obtained by the spatial reliability, historical data reliability and reputation.
# Boundary-Aware Concurrent Queue: A Fast and Scalable Concurrent FIFO Queue on GPU Environments
## 3 BACQ Proposal
### Overlapping In-Flight Enqueues/Dequeues on Same Array Element  
An operation is \"in-flight\" from the time it establishes priority until it is published. It is possible for an in-flight enqueue and dequeue to overlap while accessing the same array element. For such an enqueue operation, we find the index of such an enqueue request by:  
\[index=local\_tail\%N\]  
(Listing 2 Line 22).  
If this position is still empty or no dequeue operation has established priority there, the local tail pointer may lag behind the global head pointer (Line 24). In such a scenario, BACQ prioritizes the enqueue operation over the dequeue at that index (Line 24). On the dequeue side (Listing 3), we similarly check if the local head pointer surpasses the global tail pointer (Line 25). If an enqueue is in-flight, this condition will be true, and we delay the dequeue by adding extra weight to the ticket (Line 27), effectively giving priority to the pending enqueue.  
Conversely, an in-flight dequeue happens when an enqueue request arrives at the same index where a dequeue has already established its order but has not yet published the item and ticket. In such cases, BACQ prioritizes the dequeue operation, as long as it remains within the valid boundary (Listing 2 Lines 29-31). This ensures a balanced approach where operations are properly prioritized based on their state and the specific conditions they encounter.
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 8 Simulation Set-Up and Results
### Results
#### 8.2.2 UAV @ 100 m  
In this section, we discuss the results when the UAV is flying at a height of 100 m. This is a higher altitude; therefore, the number of interfering elements is relatively higher.  
Figure 14 shows SINR values for UAV @ 100 m. When no link adaptation is employed, i.e., when the FPA method is used, the values vary from 3.45 dB to 7.16 dB. This represents an unstable link as the SINR values vary drastically. For example, at about the 32 s mark, there is an increase to 6.95 dB from 6.5 dB, but, from thereon, it drops sharply to reach its minimum possible value. It increases again and does not stay constant. As mentioned above, this is primarily because the FPA algorithm cannot take into account the changing interference conditions, and, at a higher altitude of 100 m, the increase in interfering elements leads to a change in the interference conditions. This also coincides with the latency values, which see delays of up to 55 ms, as shown in Figure 15.  
Q-learning starts with a lower value of 7.74 dB as compared to its 50 m performance. It maintains a steady performance value of close to 8.8 dB till about 88 s. This is indicative of good performance for the link; however, after about 89 s, it suffers a drastic dip in performance and undergoes a series of peaks and troughs till the end of simulation. At one point, specifically at the 104 s mark, it falls even below the  
Finally, DQL performs the best with average, minimum, and maximum values of 10.51 dB, 9.74 dB, and 10.633 dB, respectively, which is indicative of steady performance with high SINR values. It also shows a really low latency value of 34.01 ms. Even though it shows a 12.58% decrease in SINR values and 32.6% increase in latency values as compared to 50 m, it is still the best-performing algorithm. It implements link adaptation absent from FPA and eliminates the need for Q-tables by the use of neural networks.  
It is to be noted that the overall performance of the UAV at 100 m deteriorates as compared to 50 m. The main cause for this is the fact that, due to the higher altitude, the number of interfering elements, i.e., gNBs that UAV has LoS with, increases.
# Combination Pattern Method Using Deep Learning for Pill Classification
## 3 The Proposed Pill-Classification System
### Pill Detection  
We utilize the YOLOv5 [S] architecture for pill detection, composed of four distinct subnetworks: the backbone, neck, head, and detect layer. YOLOv5 models--available  
Figure 4: Overview of proposed pill-classification system.  
Figure 3: Window movement in Harris algorithm.  
in sizes S, M, L, and X--differ in capacity and computational efficiency, each tailored to specific operational requirements. Our implementation in PyTorch version 1. 7 leverages the generality of the trained YOLOv5 model, making it highly adaptable across varied pill-identification scenarios. As depicted in Figure 5, the pill-detection workflow entails three primary steps:  
* Step1: Localization and Configuration: Initial detection of pill locations and configurations crucial for setting up the training dataset.
* Step2: Feature Extraction: The backbone network, utilizing a Darknet architecture enhanced by a Cross Stage Partial Network (CSPNet), extracts a broad spectrum of features from pill images, including edges, textures, and distinctive patterns.
* Step3: Detection and Classification: The combined efforts of the neck and head networks, which employ spatial pyramid pooling (SPP) and anchor boxes, refine the detection and classification processes. This stage culminates with applying non-maximum suppression (NMS) to accurately identify and categorize pill types based on the extracted features.
## 3 Results and Discussion  
### Structure and Morphology by Means of XRD, HRTEM, and Raman Analyses  
#### 3.1.1 XRD Analysis  
The X-ray diffraction patterns of MoS\({}_{2}\)/TiO\({}_{2}\) n-sh obtained by in situ and ex situ methods are compared together with the XRD patterns of MoS\({}_{2}\) bulk (Fluka) and of the native TiO\({}_{2}\) n-sh powder (Figure 1). XRD diffraction patterns show peaks at 20 = 25.2\({}^{\circ}\), 37.0\({}^{\circ}\), 37.8\({}^{\circ}\), 38.6\({}^{\circ}\), 48.0\({}^{\circ}\), 53.9\({}^{\circ}\), and 55.1\({}^{\circ}\), corresponding to (101), (103), (004), (112), (200), (105), and (211) diffraction planes of anatase (PDF card #21-1272), and also match well with the native TiO\({}_{2}\) n-sh XRD peaks (black pattern). Together with the aforementioned XRD features, the XRD pattern after wet impregnation with the MoS\({}_{2}\) diluted dispersion in H\({}_{2}\)O/EtOH exhibits three additional weak peaks at 20 \(\cong\) 14.4\({}^{\circ}\), 32.7\({}^{\circ}\), and at 39.5\({}^{\circ}\), which correspond to the (002), (100), and (103) diffraction planes of hexagonal MoS\({}_{2}\) (gray pattern). XRD diffraction fingerprints of MoS\({}_{2}\) are not found for the in situ methods (red pattern) [41; 42]. Surprisingly, although this sample has a Mo content of 3% wt/wt, no XRD fingerprints of MoS\({}_{2}\) are present after reaction with H\({}_{2}\)S. At this stage, we can only assume that the sulfurization products of the Mo-based species present on TiO\({}_{2}\) n-sh are remarkably amorphous and/or very small in size (smaller than a few nm).
## 2 Materials and Methods
### Semi-Automated Hemodynamic Force Analysis
The HDF was then calculated in three directions utilizing short-axis (SAX) and long-axis views (2-chamber, 3-chamber, and 4-chamber). The directions were defined manually in the septal–lateral (S-L), basal–apical (B-A), and inferior–anterior (I-A) directions. The atrioventricular (AV) valve plane was placed for reference purposes. Finally, the HDF was quantified in the defined directions in the form of root mean square (RMS), transverse/longitudinal force (ratio), and average for one cardiac cycle (inclusive systolic and diastolic phases). Notes: The demonstrated HDF results are average HDF for one cardiac cycle in S-L (blue wave), I-A (green wave), and B-A (red wave) directions. In the SAX image, the Epicardium is shown as a green circle and the Endocardium is demonstrated as a red circle in the segmentation. The relative magnitude of transverse (inferior-anterior and septal-lateral) and longitudinal (basal-apical) forces were calculated using the ratio between transverse and longitudinal forces (Equation (3)), where RMS\({}_{\text{B-A}}\), RMS\({}_{\text{I-A}}\), and RMS\({}_{\text{S-L}}\) were the RMS force components in the B-A, I-A, and S-L directions, respectively. The ratio was computed for systole and diastole separately. \[Ratio\text{=}\frac{\sqrt{RMS_{\text{I-A}}^{2}\text{+}RMS_{\text{S-L}}^{2}}}{ RMS_{\text{B-A}}} \tag{3}\]  
The pressure field calculation was performed for visualization purposes where the Pressure Poisson equation (PPE) was solved following a previously published multigrid solver [36]. The problem is initially solved on a relatively coarse grid, and then the solution is transferred to progressively finer grids until it arrives at the grid of the input data, which is the original grid of the MRI velocity data. This pattern is then repeated until the finest grid is reached. The PPE is used to compute the pressure distribution in a fluid when the velocity field is known. The PPE was derived from the Navier-Stokes (Equation (1)) for incompressible flow (Equation (4)) to calculate the pressure field. \[\mathbf{\
abla}\cdot\mathbf{v}\text{=}\mathbf{0} \tag{4}\]  
where \(\
abla\) denotes the divergence operator and \(\mathbf{v}\) is the velocity field. Then, the PPE was derived by taking the divergence of the momentum equation leading to simplified Equation (5). Equation (5) relates the Laplacian of the pressure (\(p\)) to the velocity field (\(v\)), allowing the calculation of pressure distribution [35; 38]. \[\mathbf{\
abla}\text{ }^{2}\mathbf{p}\text{=}\mathbf{\
abla}\cdot\mathbf{(\rho}\mathbf{v}\cdot\mathbf{ \
abla}\mathbf{v}) \tag{5}\]
# Optimizing Routing Protocol Design for Long-Range Distributed Multi-Hop Networks
## 5 Implementation Method of Load-Balanced Routing Optimization
### Routing Optimization Process  
In establishing routes, a node load cost matrix has been developed to address the issue of specific end nodes in LoRa distributed multi-hop networks bearing excessive load. Nodes with an occupancy rate of 1 in the network will be removed during the route optimization process. No new requests or tasks will be assigned to these nodes, and the new traffic will be redirected to nodes with lower occupancy rates, dynamically adjusting resource allocation.The routing optimization process is shown in Figure 9.  
Algorithm 2 outlines the steps involved in the routing optimization process.  
Figure 8: Routing establishment process.  
```
0: End node business type, end node location information Output: Optimal transmission path Step1: Aggregating nodes' broadcast route requests to obtain network-wide topology information; Step2: Obtain the business type and location information of end nodes with transmission tasks, initialize node weight matrix, and determine the priority order for end node route planning; Step3: Execute the following process based on the priority order determined in Step2 for each \(i\in[1,\text{number of business nodes}]\)do if there are end nodes to be planned then go to Step4 else  routing optimization endif endfor Step4: Obtain the end nodes for which transmission paths need to be planned, calculate SF based on communication delay and link reliability constraints; Step5: With the calculated SF from Step4, determine the transmission range of end nodes, calculate the link loss matrix and adjacency matrix of end nodes, and establish a load cost matrix considering the load situation of end nodes in the network; Step6: Utilize [PERSON]'s shortest path algorithm to plan the transmission path for end nodes to reach the gateway; Step7: Update the load situation of all end nodes in the network if end nodes meet the load rate constraint then go to Step8 else  delete the path and search for alternative paths endif Step8: Remove end nodes with an occupancy rate of \(1\), return to Step3 for end node re-planning of transmission paths; return Optimal transmission path.
```  
**Algorithm 2** Routing optimization process.  
Figure 9: Routing optimization process.
# Food and Drinking Water as Sources of Pathogenic Protozoans: An Update
## 11 _Sarcocystis_  
Genus _Sarcocystis_ belongs to the clade Sar Alveolata, phylum Apicomplexa, class Conoidasida, subclass Coccidia, family Sarcocistidae [2]. It includes over 200 species of intracellular protozoan parasites, of which some species are zoonotic. The complete lifecycle is known for only 26 species and requires two hosts, generally an herbivore or carnivore intermediate host and a carnivore or omnivore definitive host [258]. The 18S rDNA sequence can differentiate members of the genus, while ribosomal ITS-1 and mitochondrial cytochrome oxidase (COI) sequences better highlight parasite diversity, relationships, and transmission cycles [8].  
Oocysts containing two sporocysts, each with four sporozoites, are formed in the small intestine of the intermediate host and are excreted with the stool into the environment either as oocytes or sporocysts when the oocytes rupture. The sporocyst ingested by the intermediate host releases the sporozoites in the gastrointestinal tract. These enter the endothelial cells of blood vessels and undergo schizogony, resulting in first-generation schizonts or merozoites that invade small capillaries and blood vessels. These become second-generation schizonts and second-generation merozoites that invade skeletal and heart myocytes and neurons. Second-generation merozoites develop into metrocytes and undergo internal mitotic divisions until becoming filled with bradyzoites and, finally, a sarcocyst resistant to digestion. Sarcocysts in muscles do not spread to new cells. Thus, there seems to be no risk of recrudescence [258].  
_Sarcocystis hominis_ and _S. heydorni_ produce either thick-walled cysts or thin-walled cysts in cattle tissue, preferentially in the heart muscle. Humans can become definitive hosts of these _Sarcocystis_ species after consumption of undercooked beef containing cysts [8].