esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methods
### The Decentralized Policy and Its Novelty Exploration  
In the exploration phase of the current deterministic gradient strategy method, the actions of all other agents are sampled from the replay buffer, while each agent samples only its own actions from its current strategy. This approach, which independently optimizes individual strategies, is susceptible to the influence of relatively over-generalization and may result in agents converging to suboptimal strategies [53]. In this paper, all actions within the current strategy were sampled, and the parameters of the actor network were centrally updated across the entire joint action space. This enabled enhanced coordination and exploration among agents and maximized the utilization of centralized critics. The specific methodology for centralized updates is as follows:  
\[\
abla J(\pi_{\theta})=E\big{[}\
abla_{\theta}\pi_{\theta}\
abla_{\pi_{ \theta}}\big{(}-L\big{(}s,z,\pi_{\theta_{1}}(z_{1}),\ldots,\pi_{\theta_{n}}(z_ {n})\big{)}\big{)}\big{]}, \tag{8}\]  
Here, \(\pi_{\theta}=\big{\{}\pi_{\theta_{1}}(z_{1}),\ldots,\pi_{\theta_{n}}(z_{n}) \big{\}}\) represents the current policy sets of all agents, and \(L\big{(}s,z,\pi_{\theta_{1}}(z_{1}),\ldots,\pi_{\theta_{n}}(z_{n})\big{)}\) is the loss function for the actor networks. This loss function comprises two components: the first component involves \(D(\pi_{\theta}(z),\pi_{\theta}(z^{\prime}))\) metric function to measure the novelty of all agents' current strategies and previous strategies to facilitate agent exploration, while the second component pertains to the evaluation value \(Q_{tot}\) of all agents' current strategies:  
\[L(actor)=-Q_{tot}\big{(}s,z,\pi_{\theta_{1}}(z_{1}),\ldots,\pi_{\theta_{n}}(z_ {n})\big{)}-E\big{[}\alpha D\big{(}\pi_{\theta}(z),\pi_{\theta}(z^{\prime}) \big{)}\big{]}, \tag{9}\]  
To prevent the distance measure from becoming excessively high and leading to too much exploration and instability, it is clipped to a predefined constant. The parameter \(\alpha\) is associated with the distance measure. When the distance falls below the certain threshold \(\delta\), indicating that the current strategy is less novel, an increase in \(\alpha\) will subsequently elevate the actor's loss function. This mechanism encourages more exploration. The specific clipping method is outlined as follows:  
\[\alpha:=\begin{cases}1.01\alpha&\text{if }\text{E}[D(\pi_{\theta}(z),\pi_{ \theta}(z^{\prime}))\leq\delta]\\ 0.99\alpha&\text{otherwise}\end{cases} \tag{10}\]  
During exploration, the distance metric values for each task can vary significantly, and updating parameters based on gradients may result in substantial differences in the distribution of inputs at each layer within the network. This discrepancy becomes more pronounced with an increased number of layers. To ensure a more stable propagation from one hidden layer to another, the actor network undergoes normalization.
## Materials and Methods
### Delimitation of Influence Areas  
For the analysis of land use and cover and water quality, the MRW was subdivided into 17 sub-basins. For that, the 28 monitoring stations existing in the MRW were rearranged into these 17 Influence Areas (IA), where monitoring stations very close to each other along the same watercourse were grouped into the same IA. They were thus considered IA in relation to the location of water quality monitoring stations along the main watercourses of the basin. The spatial scale used in this study is the \"sub-basin\" scale. This scale was employed for analyzing variations in water quality parameters and their relationship with land use and cover (correlation coefficient). These IA or sub-basins were delineated considering the \"theoretical outlet\" as the 17 main water quality monitoring stations, indicating the upstream influence area of that point.
# Inhibitory Effect of Cold Atmospheric Plasma on Chronic Wound-Related Multispecies Biofilms
## 3 Results
### Morphologic Analysis of the Multispecies Biofilms by Scanning Electron Microscopy  
The biofilms morphology was analyzed by scanning electron microscopy (SEM) in order to confirm presence of biofilm and corroborate the reduction in cell viability observed in microbiological analyses. To assess the effects of CAP in biofilm, a polymicrobial culture comprised of a 1:1:1 ratio of MRSA, _E. faecalis_ and _P. aeruginosa_ was grown.  
Figure 3 shows the micrographs obtained without treatment and after exposure to CAP. Untreated biofilm (control) showed the presence of high cell density of the cocci and bacilli. No differences were observed between the images from central and lateral fields.  
In the groups exposed to CAP, SEM images showed dispersed biofilm clusters with moderate differences in the amount of microorganisms according to the analyzed field. After 1 and 3 min of exposure to CAP (Figure 3C1,D1), there was a greater presence of diplococci in the central field, the cell density in the biofilm was lower than in the non-treated control. In the lateral field, the cell density was higher and the presence of bacilli could be noticed (Figure 3C2,D2). After 5 min of exposure to CAP, the images showed a reduction in the cell density in the biofilm mostly in the center field. On the other hand, 7 min exposure to CAP leads to a visible biofilm reduction over the entire analyzed area.  
Figure 2: Median and interquartile range of MRSA, _P. aeruginosa_ and _E. faecalis_ viable cells (CFU/mL) recovered after exposure of multispecies biofilms to CAP for 1, 3, 5 and 7 min. Kruskal–Wallis test and [PERSON]’s multiple comparison post hoc test * \(p\) < 0.05; ** \(p\) < 0.01; *** \(p\) < 0.001.  
Figure 3: Scanning electron microscopy (SEM) of multispecies biofilms formed by MRSA, _P. aeruginosa_ and _E. faecalis_ non-treated or treated for 1–7 min: (**A**) Collagen membrane without biofilm, (**B**) negative  
control (**C1**), biofilm after exposure to CAP for 1 min (central region), (**C2**) biofilm after exposure CAP for 1 min (lateral region), (**D1**) biofilm after exposure to CAP for 3 min (central region), (**D2**) biofilm after exposure to CAP for 3 min (lateral region), (**E1**) biofilm after exposure to CAP for 5 min (central region), (**E2**) biofilm after exposure to CAP for 5 min (lateral region), (**F1**) biofilm after exposure to CAP for 7 min (central region) and (**F2**) biofilm after treatment by CAP for 7 min (lateral region). Scale bare represents 5 \(\upmu\)m.
# Worldwide Connectivity for the Internet of Things Through LoRaWAN
## 4 How an End Device Joins a LoRaWAN(tm) Network
### Flow Chart of the Join Procedure in the Non-Roaming Case  
We are now providing a comprehensive example of the procedure an end device has to undergo in order to join its home network. The flow chart of such a procedure is provided in Figure 3, and it consists of the following steps.  
1. The end device sends via LoRa radio link(s), i.e., through GW(s), a join-request message over the air and, being under the coverage of the home network, the message will reach the Home NS.
2. According to the specification [8], the message contains the JoinEUI key and from that parameter and the DNS infrastructure provided by the LoRa Alliance (see [10]) the home NS can determine the IP address of the join server for that particular end device.
3. The home NS passes the request as a JoinReq to the join server previously identified, including the DevEUI and other relevant parameters.
4. The join server, having all the necessary keys, derives the session keys for the control data and provide the NS with them.
5. The NS replies to the end device with a join-accept (via the radio interface, i.e., via the GWs) and sets all of the radio parameters.
6. A user data packet is sent (via the radio interface, i.e., via the GWs) to the NS as a JoinAns.
7. The network server, being the received packet made of user data, forwards it to the application server.
8. The application server gets the session keys for user data from the join server and decrypts the message making it available to the application layer.
# A Multi-Tier Security Analysis of Official Car Management Apps for Android
## 2 Related Work
Concerning mitigation to the various identified threats, the proposal addresses the following categories: \"Vehicle communication channels\", \"update process\", \"unintended human actions facilitating a cyber attack\", \"external connectivity and connections\", \"potential targets of, or motivations for, an attack\", \"potential vulnerabilities that could be exploited if not sufficiently protected or hardened\", \"data loss/data breach from vehicle\", and \"mitigations to the threats outside of vehicles\". Furthermore, the \"proposal for a new UN Regulation on uniform provisions concerning the approval of vehicles with regard to software update processes and of software update management systems\" [18], amongst others tackles issues regarding certificate of compliance for vehicle software update management, covers general specifications, including \"requirements for the software update management system of the vehicle manufacturer\" and \"requirements for the vehicle type\", refers to matters pertaining to the \"modification and extension of the vehicle type\" and the \"conformity of production\", and considers potential \"penalties for non-conformity of production\". All the previous proposals have been consolidated and approved last June as \"UN Regulation No. 155 on Cyber Security and Cyber Security Management Systems\" and \"UN Regulation No. 156 on Software Updates and Software Updates Management Systems\" by the World Forum for Harmonization of Vehicle Regulations, entered into force on late January 2021 [19]. Basically, both these regulations extend type approval of vehicles to lifetime management of cybersecurity and software. While all these regulation-oriented initiatives and advancements aiming at reinforcing the cybersecurity of vehicles are definitely on the positive side, as stems from the above analysis, the topic of connected cars apps is not tackled directly, but indirectly as potentially being part of other more generic categories of cybersecurity issues, say, \"external connectivity and connections\" and \"potential vulnerabilities that could be exploited if not sufficiently protected or hardened\".
## 3 SDN Performance Challenges
### Flow Rule Update Operation
RuleTris in [38] is the  
Figure 6: Example of TCAM Rule Update Operation. state-of-the-art in designing an efficient policy compiler; however, it is computationally expensive in terms of TCAM update scheduler, which results in large firmware time up to 50 ms for a single update in flow table with a capacity of 1000 entries [7; 37]. To overcome the shortcoming of [38], FastRule [7], RuleTris [37] reduces the flow entry update operation in terms of speed and time, 100\(\times\) faster than existing solutions with a flowtable size of 1000 and operation time by 15 ms and 60% respectively. The presence of a diversity of switch implementation is another factor that determines the performance of the flow update. There are a wide variety of switch implementations with different software control and hardware properties, which influence rule update operation efficiency. Most existing solutions overlook these differences and assumed all switches have the same hardware and software behavior. [PERSON] et al. [18] noticed this problem and FastRule [7], RuleTris [37] are not an exception for such a concern, as neglecting these differences can lead to substantial performance challenges [18]. Diversity can cause significant utilization challenge, which can contribute to programming complexity. For instance, consider two or more switches of the same TCAM size, but one adds software flowtable as part of the switch. Adding the same sequence of rules may lead to rejection in one switch (TCAM full). [PERSON] [39] noticed this problem and FLIP is an example for such a scenario [18]. This cannot be effective in practice. To address this problem while considering switch diversity, RuleTailor [18] proposed an optimization framework for the general TCAM-based switch rule update. RuleTailor aimed to reduce the rule update latency using techniques such as instruction type transformation, pseudo deletion, and match field distance, which effectively work for both control and data plane. Twelve Ms per rule update was achieved in the flow table with 1000 flow entries. In different efforts to further achieve less update, Balancer [10] achieved the least update cost by logically splitting TCAM into a reactive and proactive part with dynamic adjustment to efficiently replace unused rules. Although the schemes have made further efforts in reducing the update operations, it does not guarantee to maintain stable performance, especially with a large amount of network traffic flow. The dynamic nature of topological changes where the number of forwarding devices evolves rapidly over time, this can lead to significant performance bottleneck [3; 40]. More investigation is required for adaptive flow entries update operation with least latency and overhead in a large-scale network environment.
## 2 Materials and Methods
### Development of a Vision-Assisted Distal Upper Limb Rehabilitation Robot
#### 2.1.2 Assembly of the Robot  
The distal upper limb rehabilitation robot proposed and developed in this study consists of two parts, forearm and hand regions, each containing a motor. The first part is the forearm region, providing supination and pronation motion through the servo motor (Ezi-Servo Series, Fastech, Bucheon, Korea) placed at the back of the robot. The motor movement is translated to forearm supination/pronation motion through the use of gears. A small circular gear is placed at the tip of the motor and a larger circular gear containing the hand region is matched with the small gear. The motor movement is controlled using LabVIEW(r) program (LabVIEW(r) 2013, National Instruments, Austin, TX, USA) and can be manipulated automatically and manually through the use of buttons on the control panel. The motion was limited to a certain range (approximately \(\pm\) 30\({}^{\circ}\)) for safety.  
The second part of the rehabilitation robot enables grasp motion of the hand. Similar to the forearm region, the hand region produces gripping motion from a stepping motor (PGM32-NK243, Robot Mart, Gimpo, Korea) by the use of a geared mechanism, where one part of the gripping plate connected to the motor's shaft is matched with the other for opening and closing motions. This motor is controlled by the buttons on the control panel and the information from the buttons is collected and transferred via Arduino (Arduino UNO, Arduino, Italy).  
The control panel for the rehabilitation robot consists of seven pressure sensors (400 FSR, Interlink Electronics, Inc., Camarillo, CA, USA) and the data from them are processed via Arduino. The buttons on the control panel consist of forearm supination/pronation motion, closing/opening of grasp motion,  
Figure 2: (**A**) Initial design for the two-axis distal upper limb rehabilitation robot is shown. (**B**) The developed system for this study is shown. The camera on the robot is indicated with red arrow.  
emergency stop, confirmation (measurement) button for image processing, and execution button for automatic movement according to the result of image processing (Figure 3).  
The LabVIEW control program collects data from the control panel via Arduino, visualizes the image from the camera, and controls the forearm supination/pronation motion both manually and automatically. The camera (LifeCam StudioTM, Microsoft(r), Redmond, WA, USA) is mounted on the bar connected to the grasping axis of the robot in parallel, so that the robot hand axis and the camera axis is the same. The camera image is displayed on a separate LCD display panel (Camel Co., Ltd., Seoul, Korea).
# A Novel Deep Dense Block-Based Model for Detecting Alzheimer's Disease
## 3 Materials and Methods
### Proposed Classification Methods and Techniques  
#### 3.3.1 CNN-Based Architectures  
CNNs are deep neural networks that employ mathematical or linear operations known as \"convolution\". Multiple hidden layers, pooling layers, and output or fully connected (FC) layers form the base CNN architecture. These hidden layers are structured as a sequence of convolutional layers that contain filters, also known as kernels, which enable the network to perform image classification and predict patient diseases [43]. The pooling layer sequentially reduces the spatial size representation and hyperparameters. The pooling layer effectively minimizes the computational cost in CNNs and thus resolves the overfitting problem. The network also consists of ReLU activation functions. The mathematical formula for the ReLU is given in Equation (2) [44].  
\[f_{\text{ReLU}}\ =\ max(0,x) \tag{2}\]  
The CNN models are composed of a sequence of layers that employ filters to execute dimension reduction and feature extraction. CNNs are consequently referred to as \"feature extractors\", owing to their ability to efficiently extract significant features, analyze them independently, and classify them [45]. To generate a result, the FC layer was utilized in conjunction with the requisite collection of classes to execute a nonlinear modification of  
Figure 3: Dataset creation process.  
the extracted features. This layer functions as an image classifier and analyzes and classifies the brain MRI images. The extraction of the feature vectors from the FC layers of the CNN was followed by their input into the units of the softmax layer for classification. The mathematical expression for the Softmax activation function can be represented as follows in Equation (3) [46]:  
\[\sigma(\overrightarrow{z})_{i}=\frac{e^{z_{i}}}{\sum_{j=1}^{K}e^{z_{j}}} \tag{3}\]  
where \(\overrightarrow{z}\) is the input vector of the Softmax function, \(Z_{i}\) is the input values of the input vector, \(e^{z_{i}}\) the exponential function enforced to each element of the input vector, \(K\) is the number of classes in the multiple classifiers, and \(\sum_{j=1}^{K}e^{z_{j}}\) is the normalization operation.
# A Multicast Routing Scheme for the Internet: Simulation and Experimentation in Large-Scale Networks
## 4 Experimental Platform
### Quagga Routing Suite  
Quagga is an open-source routing protocol suite providing implementations of different routing protocols such as BGP and PIM. It has a significant developer community that includes independent code committers, service providers, and academic institutions. The software is written in the standard C programming language and is compatible with Unix operating systems such as Linux, Solaris, and BSD.  
There are just a few other open source routing solutions that enable rapid implementation of additional protocols, features, and functionality. Bird ([[http://bird.network.cz](http://bird.network.cz)]([http://bird.network.cz](http://bird.network.cz)) acccessed on 15 September 2021), and XORP ([[https://github.com/greearb/xorp.ct](https://github.com/greearb/xorp.ct)]([https://github.com/greearb/xorp.ct](https://github.com/greearb/xorp.ct)) accessed on 15 September 2021) are the most popular, and, like Quagga, they all run on conventional PC hardware. We chose Quagga because of its extensive adoption and maturity, whereas Bird's inter-domain routing support is currently limited, and XORP is neither mature nor widely utilised for research. Quagga is also starting to establish itself as the standard reference platform for software-defined routers in production applications.  
The Quagga architecture consists of the Zebra daemon (i.e., the core module), which is an abstraction layer to the underlying Unix kernel. The Zebra daemon comes with a collection of client modules named Zserv, each of which implements a different routing protocol. Additionally, Zebra includes the Zserv Application Programming Interface (ZAPI), which allows routing protocol modules to access and send routing modifications to the kernel routing database and network interfaces. The Zebra daemon must be started first and provides the inter-communication between the kernel and the routing protocol daemons we would like to employ in the network. All daemons in Quagga include a command-line interface (named VY) for configuration, which has a CISCO OS-like syntax. It is also possible to use pre-defined configuration files.  
It is worth noting that neither Quagga, Bird, or even XORP are capable of completely implementing a router. They just supply the route engine (algorithms and protocols), so datagram transmission still requires a forwarding engine. The implementation of GCMR as well as the forwarding engine used in our prototype are described in the sections below.
## 3 Analysis of the Fault State and Establishment of the Fault Data Sample
### Analysis of Fault Signal
#### 3.1.4 The Intermittently Flameout of Engine  
The intermittent flameout of engine is simulated by continuously turning off the power supply of the igniter. In theory, the ECU will continue to inject the fuel when the engine is not ignited. At this time, the fuel cannot be burned, the oxygen concentration in the exhaust gas is reduced, and the air-fuel ratio signal is reduced. However, the ECU of the 3 W engine used in this platform receives the speed signal from the igniter, so once the igniter is de-energized, the ECU cannot find the speed signal, and the fuel injection pulse width will not be generated, resulting in the failure to inject oil. The air-fuel ratio will increase sharply compared to normal state.  
Figure 8a reflects the change of air-fuel ratio with time, in which the black line and the red line represent the air-fuel ratio under normal state and the air-fuel ratio under intermittent flameout failure state, respectively. It can be seen from the figure that at the moment when the ignition is turned off, the air-fuel ratio will gradually increase due to the loss of the injection pulse width. As the engine starts, the air-fuel ratio will gradually return to normal. Also, the rotating speed of the engine will be decreased sharply when the ignition is turned off and return to normal as the engine starts. However, it should be noticed that at the moment of restart, the engine's speed fluctuates greatly. Figure 8b represents the change of the speed with time in the two states of the engine at 3500 r/min.  
Figure 7: Air-fuel ratio diagram of exhaust pipe leakage and normal conditions. (**a**) Chart of air-fuel ratio changing with rotating speed, (**b**) Change chart of transient air-fuel ratio.  
The intermittent flameout of engine will cause the harmonic component of the engine vibration signal to increase. Figure 9 shows the frequency and time domain of the vibration signal under normal engine conditions and intermittent flameout failures. Among them, Figure 9a,c represents the time domain and the frequency domain waveform diagrams of the vibration signal under normal state. While Figure 9b,d represent the time domain and the frequency domain waveform diagrams of the vibration signal under intermittent flameout failure state. Through the comparison of the four images, it can be found that the waveform of the vibration signal is very irregular under the intermittent flameout state, and the acquired time-domain waveform contains a large number of irregular harmonics.  
Figure 8: Charts of speed and air-fuel ratio changing with time. (**a**) Chart of air-fuel ratio versus time, (**b**) Change chart of transient speed.