esa-sceva/satcom-chunk-collection huggingface dataset:
# Swarm of Drones in a Simulation Environment--Efficiency and Adaptation
## 2 The Drones and the Swarm of Drones
### Drone Swarm Control  
In the field of swarm drone control, various methods enable the efficient and coordinated operation of multiple units. Currently, two concepts of manual drone control are commonly employed. In the first, each drone is individually controlled. The second concept involves point-based control, which includes superior and subordinate drones [31]. This approach allows assigning a predetermined trajectory for each drone. Drone swarm management often draws inspiration from nature, using algorithms similar to those described by [PERSON] and [PERSON] [32], or employing biological concepts to decide where to place drone delivery hubs [33].  
Many researchers develop detailed approaches to swarm robotics, based on strong math [34]. A key part of this field is creating ways for drones to move safely in changing places, like crowded cities, without crashing. This solution requires carefully set coordinates for all drones. These ideas often come from watching nature (ants or birds movement) [35].  
Managing drone swarms is tricky, because it mixes various techniques and methods. The article in [36] discusses the problem of drone mobility. The importance of the problem of finding the best paths for drone swarms is presented in [37]. The problem of maintaining swarm structure while avoiding obstacles is described in the article in [38].  
The drone depicted in Figure 1 represents a standard configuration that is foundational for the development of our intended swarm formations. This model is not uniquely designed but is an example of the type of quadcopter utilized in our research. It is outfitted with a suite of sensors for environmental interaction, multiple motors that provide agile maneuvering capabilities, and an energy-dense battery to ensure prolonged operational periods.  
The heart of the drone flight control system is a mainstay in drone technology, due to its proven performance, the STM32 microprocessor. This microprocessor faithfully executes  
Figure 1: Specification of drones forming swarm formation.  
commands from the ArduPilot flight management software and enables manual control and autonomous flight patterns.  
The individual drone is integrated with a Raspberry Pi microcomputer, which runs specialized software. This software empowers the drone to communicate within a networked swarm, maintain its relative position within the formation, and autonomously execute collision avoidance strategies.
# Deterministic Propagation Modeling for Intelligent Vehicle Communication in Smart Cities
## 2 Materials and Methods
### Urban Scenario Description  
This subsection describes the selected urban scenario (google map ([[http://www.google.com/maps](http://www.google.com/maps)]([http://www.google.com/maps](http://www.google.com/maps))) with its 2D and 3D representation maps, reference points and main 3D-RL simulation parameters.  
Figure 1a shows the urban scenario aerial view, (b) is the 2D schematic (x, y) map and, (c) is the 3D frontal-view map used in the simulation. Table 1 identify the Cartesian coordinates (x, y, z) of interest points according its relative position from the origin (0, 0, 0).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Abbreviation** & **Coordinates (x, y, z) m** \\ \hline Main Avenues & AV1/AV2 & (x, 83, 0)/(x, 69, 0) \\ Streets & ST1/ST2/ST3 & (161, y, 0)/ (117, y, 0)/ (175, y, 0) \\ Transmitter antenna (TX) & TX1/TX2 & (146, 63, 3.5)/(281, 63, 3.5) \\ Receiver antenna (RX) & RX & (x, y, 1.5) \\ Buildings & B1, B2, B3, B4, B5, B6 & Not applicable. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Scenario reference.  
Figure 1: Urban scenario: (**a**) google map aerial view; (**b**) 2D-map schematic view; and (**c**) 3D-map frontal view.  
The modeled and simulated urban scenario is a replica of the Pio XII Avenue at the city of Pamplona, Spain (42\({}^{\circ}\)48\({}^{\prime}\)22.15 N, 1\({}^{\circ}\)39\({}^{\prime}\)39.14 W) and it was referenced using the Google Maps tool. The scenario encompasses an area of 1,320,000 m\({}^{3}\) (400 \(\times\) 150 \(\times\) 22) and includes the typical elements of an urban environment (buildings, trees, cars, streetlights, pedestrians, etc.). Each side of the avenue has three lanes, and vehicles park parallel to the pavement on both sides. The parked vehicles coexist with metal and fiberglass garbage containers. The frequency for simulation was set up at 5.9 Ghz. Simulation parameters for the radio frequency (RF) characterization by means of the RL algorithm are summarized in Table 2.  
The RST was chosen considering the receiver (RX) minimum sensitivity information provided for some manufactures of V2X (IEEE 802.11p) radio-communications products [29; 30; 31; 32].
Design and Analysis of a General Relay-Node Selection Mechanism on Intersection in Vehicular Networks  
[PERSON]  
1 School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China; [EMAIL_ADDRESS] (D.C.); [EMAIL_ADDRESS] (B.Z.)  
2 Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha 410114, China  
3 College of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China; [EMAIL_ADDRESS]  
4 Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA; [EMAIL_ADDRESS]  
5 Correspondence: [EMAIL_ADDRESS]; Tel.: +86-731-8525-8462515156  
Geographic Information System (GIS) service makes the access to the local road information no longer a limited factor.  
The existing relay-node selection methods based on geography information focus on the design of a straight road and the crossroad. However, these methods present a performance degradation in dense networks or sparse networks. In our earlier work [13], an exponent-based partitioning broadcast protocol (EPBP) is proposed. EPBP performs a significant improvement under high node density conditions in terms of message dissemination speed and packet delivery ratio (PDR). Based on exponent partition, we explore a robust relay-node selection in [14], which presents an acceptable performance in the adverse scenario and a high performance in general scenarios, even in sparse density where EPBP is not expert. In the two methods, the application of the exponent partition is just discussed on the straight road.  
Aiming at the intersection scenario, this paper develops a stable relay-node selection on a general intersection. The contributions of this paper are threefold: (1) building a model to describe the general intersection with \(N_{\text{b}}\) branches and any angle between adjacent branches; (2) exploring a general relay-node selection method on intersection based on the exponent partition, which presents a significantly improved and stable performance; (3) constructing the performance analysis of our proposal in terms of message dissemination speed and PDR for the evaluation and future optimization.  
The remainder of this paper is organized as follows. In Section 2, the related works are described briefly. After presenting a mathematical description for a general intersection, an exponent-partition-based relay-node selection method on the general intersection is discussed in Section 3. Additionally, the analytic models for the proposed method are developed in Section 4. In addition, Section 5 validates these models and evaluates the improvement of the proposed method by simulation. Finally, our work is summarized.
# DeepDCA: Novel Network-Based Detection of IoT Attacks Using Artificial Immune System
## 6 DeepDCA: Deep Learning Dendritic Cell Algorithm
### The SNN Signal Categorization  
The SNN module assign each selected attribute to specific signal category (see Figure 4). The guidelines for signal categorization are presented below:  
* _Danger Signal_: this signal indicates to the presence of anomalous situation or attack circumstances
* _Safe Signal_: this signal indicates to the presence of normal behavior or non-attack circumstances  
Figure 3: Flowchart of The DeepDCA Proposed Model.  
Generation of DCA Signals  
The SNN is designed to extract the signals as safe (SS) and danger (DS) signal form the features (f_1, f_2,..., f_N) as the following parametrized:  
\[SS\left(f_{1},f_{2},\ldots,f_{N}\right)=100*\text{sigmoid}\left(b_{S}+\sum_{i}w _{S,i}*\text{selu}\left(b_{S,i}+v_{S,i}*f_{i}\right)\right) \tag{4}\]  
\[SD\left(f_{1},f_{2},\ldots,f_{N}\right)=100*\text{sigmoid}\left(b_{D}+\sum_{i }w_{D,i}*\text{selu}\left(b_{D,i}+v_{D,i}*f_{i}\right)\right) \tag{5}\]  
where  
\[w_{S,i}\text{ and }w_{D,i}>=0 \tag{6}\]  
The **sigmoid** activation function at the output neurons assures that the signals are contained in the range 0-100. Where the **selu** activation function at the hidden layer neurons admits to cut off high or low values of the feature attributes--depending on the signs of the parameters \(v_{S,i}\text{ \ or }v_{D,i}\). The positivity restriction on the weights \(w_{S,i}\text{ \ and \ }w_{D,i}\text{ \ breaks the symmetry between the formulae for the two signals. It prohibits the SNN from choosing a solution where \(w_{S,i}=-w_{D,i}\). These steps illustrate using the Algorithm 1.  
Figure 4: SSN Architecture for Signal Categorization.  
```
1:Features \((f_{1},f_{2},...,f_{N})\);
2:Signals as safe (SS) and danger (DS)
3:initialise number of hidden layers L, weights w;
4:for i in Number of inputs do
5: Add input \(f_{i}\);
6: Add neuron \(H_{S,i}\) SeLU activation & connect with \(f_{i}\);
7: Add neuron \(H_{D,i}\) SeLU activation & connect with \(f_{i}\);
8: Add \(AlphaDropout\) Layer for the \(SS\);
9: Connect it with \(H_{S,i}\), \(i=1,...,Number\) of inputs;
10: Add AlphaDropout Layer for \(DS\);
11: Connect it with \(H_{D,i}\), \(i=1,...,Number\) of inputs;
12: Add neuron S with sigmoid activation;
13: Connect it with the AlphaDropout Layer for \(SS\);
14: Add neuron D with sigmoid activation
15: Connect it with the AlphaDropout Layer for \(DS\);
16:endfor
17:while no of epochs not complete & condition not fulfilled do
18: Update weights;
19: Compute training and validation loss;
20: Evaluate model performance;
21:endwhile
```  
**Algorithm 1** SSN for Signal Categorization.
## 3 Results
### Senescence Inductive Effect of Hydrolyzed Riceberry Rice Bran Extract on Colon Cancer Cell Lines  
As with the effect of HRBE on cancer cell aging, morphological changes associated with cellular senescence were observed such as an enlarged and flattened cell shape and increased granularity and biochemical changes. For this particular experiment, the senescence-associated beta-galactosidase (SA-\(\beta\)-gal) staining was used, which revealed the senescence inductive ability of HRBE on HT-29 and SW-620 cells at concentrations of 0, 0.5, 1, 2.5 and 5 mg/mL compared to the treatment with mitomycin C at 0.05 mg /mL (Figures 3 and 4).  
Figure 3: Cellular senescence inducing effect of HRBE on HT-29 cells after being treated with 0 mg/mL, considered as negative control (**A**), 0.5 mg/mL (**B**), 1 mg/mL (**C**), 2.5 mg/mL. (**D**) and 5 mg/mL (**E**) for 24 h compared to 0.05 mg/mL mitomycin C treatment, considered as the positive control (**F**). The blue stained cells with flattened and enlarged cell morphology represent the cellular senescence status indicated by the white arrowheads. Data are represented by the mean \(\pm\) SD. Statistical analysis was performed using student’s \(t\)-test comparing each concentration with the control group (0 mg/mL) (\({}^{*}\), \(p\leq 0.05\)).  
From the result in the HT-29 cell line, the approximate mean values of the SA-\(\beta\)-gal stained cells were indicated as being at 27, 51, 59, 79 and 86 percent, respectively, after being treated with the different concentrations of HRBE (0, 0.5, 1, 2.5 and 5 mg/mL) and 95 percent after being treated with mitomycin C at a dose of 0.05 mg/mL (Figure 3), respectively. The number of SA-\(\beta\)-gal-stained cells gradually increased as the concentration of the extract increased. With the cellular senescence inductive effect on the SW-620 cell line, the approximate mean values of the dyed cells were 4, 6, 16, 8 and 32, respectively, after being treated with the different concentrations of HRBE (0, 0.5, 1, 2.5 and 5 mg/mL) and 13 percent after being treated with mitomycin C at 0.05 mg/mL (Figure 4).  
Figure 4: Cellular senescence inducing effect of HRBE on SW-620 cells after being treated with 0 mg/mL, considered as negative control (**A**), 0.5 mg/mL (**B**), 1 mg/mL (**C**), 2.5 mg/mL (**D**) and 5 mg/mL (**E**) for 24 h compared to 0.05 mg/mL mitomycin C treatment, considered as the positive control (**F**). The blue stained cells with flattened and enlarged cell morphology represent the cellular senescence status indicated by the white arrowheads. Data are represented by the mean \(\pm\) SD. Statistical analysis was performed using student’s \(t\)-test comparing each concentration with the control group (0 mg/mL) ( \(\star\)*, \(p\leq\) 0.01).  
The positive stained cells were found to have increased after being exposed to the higher extract concentrations, and HRBE had a higher senescence inductive effect on the HT-29 cells compared to the SW-620 cells.
## 4 The Proposed Reputation Management System
### Punitive Module
#### 4.3.1 Punishment Scheme  
After evaluating the total reputation value of monitored nodes in our network, the values are stored in a node table and updated at regular intervals. After a period of time, it is expected that the total reputation value of a monitored node should increase if the node is transmitting data and routing control packets for other nodes. We defined a threshold value of 0.75, which is just a 25% increase in the initial default reputation value (0.6) assigned to all nodes at the onset of the network operations. The statuses of all monitored nodes are checked at regular intervals. The computed total reputation value of a node is mapped with a grading criterion, which helps to determine the status of a node.  
Table 3 gives an overview of how the grading of the computed total reputation values is carried out by the nodes. When the status of a node is checked based on the computed total direct reputation value after a defined time interval, for example every 60 s, if the node status is flagged as \"undecided\", the node-ID of that node is placed on a watch-list within the punishment scheme. If subsequent node status checks still flag the node's status as \"undecided\", such that after two subsequent checks, the node status is changed to \"bad node\", the node is moved into a black-list within the punishment scheme. A node that is blacklisted will be denied network resources; all route requests that originate from it will be ignored. The node details are then passed on to the path coordinator, which checks the route cache and ensures that paths that contain that node are deleted, and an alternative route will be sourced for when needed. On the other hand, if a node is flagged as a \"good node\" or a \"very good node\", the node-ID is moved to a \"white-list\". Every node on the white-list is allowed to carry on with its normal network activities. Finally, when a node is flagged once as a bad node, the node is immediately blacklisted and its details passed to the path coordinator for immediate action.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Computed Reputation Value (R)** & **Grading Criteria** & **Node Status** \\ \hline
1.000–2.000 & Very good reputation & Very reliable node \\
0.750–0.999 & Good reputation & Reliable node \\
0.500–0.749 & Initial/normal/expected reputation & Undecided \\
0.000–0.499 & Poor reputation & Unreliable node/bad node \\ \hline \hline \end{tabular}
\end{table}
Table 3: Grading criteria for the computed total reputation values.
## 6 Discussion
### Future Works  
Although the three types of IRW's dynamics characteristics, derailment, hunting, and offset running, have been observed and presented during experiments, some potential areas are worthy of further exploration, listed as follows:  
* The critical speed between the convergent process and the hunting motion needs to be determined theoretically or technically. It affects the security boundary in practice;
* Rail profile irregularities are prevailing and inevitable in practice. Whether the dynamic characteristics can be significantly affected needs to be verified on a real-life rail track;
* Only 1/10 linear conicity tread wheels are adopted in the present study. If nonlinear tread wheels were adopted, as shown in Figure 37, the dynamics would become more complicated and diversified;
* In view of the IRW's complex dynamics, how to design a reliable bogie structure and robust control methods to conquer the derailment, hunting, and offset running at different longitudinal speeds and how to realize a stabilized centered running state urgently need to be explored.  
Figure 36: The experimental results of the critical speed \(\omega_{c}\) with variations in \(y_{\text{ec}}\).
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Architecture** & **Fold** & **Sensitivity** & **Specificity** & **Accuracy** & **B. Accuracy** & **F1 Score** \\ \hline \multirow{8}{*}{CNNs} & VGG19 & fold\_5 & 0.7816 & 0.7538 & 0.7595 & 0.7677 & 0.5738 \\  & VGG19 & fold\_2 & 0.7586 & 0.8438 & 0.8262 & 0.8012 & 0.6439 \\ \cline{2-7}  & ResNet50 & fold\_5 & 0.7011 & 0.7868 & 0.7690 & 0.7440 & 0.5571 \\  & ResNet50 & fold\_2 & 0.7126 & 0.8679 & 0.8357 & 0.7903 & 0.6425 \\ \cline{2-7}  & InceptionV3 & fold\_2 & 0.8276 & 0.6907 & 0.7190 & 0.7591 & 0.5496 \\  & InceptionV3 & fold\_1 & 0.7931 & 0.8018 & 0.8000 & 0.7975 & 0.6216 \\ \cline{2-7}  & Xception & fold\_3 & 0.8851 & 0.6336 & 0.6857 & 0.7593 & 0.5385 \\  & Xception & fold\_1 & 0.7931 & 0.7538 & 0.7619 & 0.7734 & 0.5798 \\ \hline \multirow{8}{*}{ViTs} & Original ViT & fold\_4 & 0.8621 & 0.5000 & 0.5748 & 0.6810 & 0.4559 \\  & Original ViT & fold\_5 & 0.8161 & 0.7126 & 0.7340 & 0.7643 & 0.5591 \\ \cline{2-7}  & Swin Base & fold\_4 & 0.5862 & 0.7934 & 0.7506 & 0.6898 & 0.4928 \\  & Swin Base & fold\_1 & 0.7356 & 0.8174 & 0.8005 & 0.7765 & 0.6038 \\ \cline{2-7}  & Swin Tiny & fold\_5 & 0.8161 & 0.6048 & 0.6485 & 0.7104 & 0.4897 \\  & Swin Tiny & fold\_4 & 0.7356 & 0.7814 & 0.7720 & 0.7585 & 0.5714 \\ \cline{2-7}  & Twins-PCPVT & fold\_5 & 0.8046 & 0.5299 & 0.5867 & 0.6673 & 0.4459 \\  & Twins-PCPVT & fold\_3 & 0.7701 & 0.7844 & 0.7815 & 0.7773 & 0.5929 \\ \hline \multirow{8}{*}{Hybrid models} & DeiT & fold\_5 & 0.7931 & 0.6048 & 0.6437 & 0.6989 & 0.4792 \\  & DeiT & fold\_4 & 0.6552 & 0.8443 & 0.8052 & 0.7497 & 0.5816 \\ \cline{2-7}  & CaiT\_XXS36 & fold\_3 & 0.6437 & 0.8234 & 0.7862 & 0.7335 & 0.5545 \\  & CaiT\_XXS36 & fold\_1 & 0.7241 & 0.7814 & 0.7696 & 0.7528 & 0.5650 \\ \cline{2-7}  & CaiT\_S24 & fold\_4 & 0.6437 & 0.8353 & 0.7957 & 0.7395 & 0.5657 \\  & CaiT\_S24 & fold\_1 & 0.7701 & 0.8054 & 0.7981 & 0.7878 & 0.6119 \\ \cline{2-7}  & CeiT & fold\_4 & 0.7126 & 0.7246 & 0.7221 & 0.7186 & 0.5145 \\  & CeiT & fold\_5 & 0.7701 & 0.8054 & 0.7981 & 0.7878 & 0.6119 \\ \cline{2-7}  & ConViT & fold\_5 & 0.7931 & 0.6317 & 0.6651 & 0.7124 & 0.4946 \\  & ConViT & fold\_1 & 0.7471 & 0.7305 & 0.7340 & 0.7388 & 0.5372 \\ \hline \multirow{8}{*}{Others inspired in ViT} & ResMLP\_12 & fold\_4 & 0.6092 & 0.7605 & 0.7292 & 0.6848 & 0.4818 \\  & ResMLP\_12 & fold\_1 & 0.5862 & 0.8922 & 0.8290 & 0.7392 & 0.5862 \\ \cline{2-7}  & ResMLP\_24 & fold\_4 & 0.7126 & 0.7964 & 0.7791 & 0.7545 & 0.5714 \\ \cline{1-1} \cline{2-7}  & ResMLP\_24 & fold\_5 & 0.7241 & 0.8683 & 0.8385 & 0.7962 & 0.6495 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the results obtained by evaluating the different DL models on the Papila dataset according to different metrics. For simplicity, only the results corresponding to the folds with minimum and maximum balanced accuracy (B. Accuracy) are displayed.
###### Abstract  
This paper addresses the problem of authentication and identification of wireless devices using their physical properties derived from their Radio Frequency (RF) emissions. This technique is based on the concept that small differences in the physical implementation of wireless devices are significant enough and they are carried over to the RF emissions to distinguish wireless devices with high accuracy. The technique can be used both to authenticate the claimed identity of a wireless device or to identify one wireless device among others. In the literature, this technique has been implemented by feature extraction in the 1D time domain, 1D frequency domain or also in the 2D time frequency domain. This paper describes the novel application of the synchrosqueezing transform to the problem of physical layer authentication. The idea is to exploit the capability of the synchrosqueezing transform to enhance the identification and authentication accuracy of RF devices from their actual wireless emissions. An experimental dataset of 12 cellular communication devices is used to validate the approach and to perform a comparison of the different techniques. The results described in this paper show that the accuracy obtained using 2D Synchrosqueezing Transform (SST) is superior to conventional techniques from the literature based in the 1D time domain, 1D frequency domain or 2D time frequency domain.  
authentication; identification; security; wireless communication; machine learning +
Footnote †: journal: Applied Physics Letters
## 2 ALBI Grade Related to Treatment Modalities
### Palliative Treatment  
#### 2.2.1 Tace  
TACE is recognized worldwide as a standard treatment for BCLC-B HCC, while a few reports have also noted that the ALBI grade is associated with OS in HCC patients treated with TACE. [PERSON] et al. found that only the ALBI grade showed a statistically significant association with OS (HR 3.06, \(p\) = 0.038) [37], and [PERSON] et al. noted that ALBI grade 2 was a significant prognostic factor for worse OS in a study of 192 BCLC-B HCC patients with good hepatic function (Child-Pugh score \(\leq\) 7) (HR 1.548, \(p\) = 0.048) [38]. The results of the OPTIMIS trial highlighted the negative influence of TACE on hepatic function as well as the importance of judging TACE failure in clinical practice [39]. In addition, with recent advancements in TKI treatment, the concept of the TACE refractory status [40] and switching to TKI therapy have been recognized as important for improving prognosis. Another report noted that the ALBI grade demonstrated a higher rate of deterioration of hepatic reserve function (grade 1 to 2: 18.4% to 21.4%/1 TACE) than the Child-Pugh classification (class A to B: 8.6% to 13.8%/1 TACE) during a clinical course that included repeated TACE procedures in 212 BCLC-B HCC patients with Child-Pugh class A [41]. [PERSON] et al. also reported the usefulness of the ALBI score as a statistically significant prognostic factor for acute chronic liver failure at 90 days after TACE (OR 3.99, \(p\) = 0.002) [42]. Thus, as compared to the Child-Pugh classification, the ALBI grade might also play a greater role in the assessment of hepatic reserve function for TACE used to treat unresectable HCC.  
[PERSON]'s criteria [9] and Kindai criteria [10] had been proposed as a subclassification for BCLC-B with the Child-Pugh score in order to help judge the possible effectiveness of switching therapy. However, recently, [PERSON] et al. proposed an ALBI-based model for the prediction of OS after TACE in BCLC-B cases [43], while a new subgrouping for BCLC-B (intermediate stage) HCC [Modified Intermediate stage of liver CANcer (MICAN) criteria] [44], in which the ALBI grade was used instead of the Child-Pugh score, has been proposed. More recently, the Asia-Pacific Primary Liver Cancer Expert (APPLE) group presented a consensus statement and proposed \"TACE-unsuitable\" criteria using mALBI [45]. Child-Pugh class A is not always a necessary and sufficient condition for TKI treatment in unresectable HCC, and a detailed assessment with ALBI and mALBI grades has begun to play an increasingly important clinical role in the treatment strategy for unresectable HCC.