esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Applications of Game Theory in WSN
### Game Theory for Wireless Network Management in WSN  
The design of a wireless network and optimization of its performance is a non-trivial and complicated process. The WSN has to fulfill straight requirements imposed from a set of operation goals. Game theory thus plays a supportive and critical role in designing and operating a WSN. Figure 3 illustrates the relationship between various WSN elements and the way that game theory is employed.  
Representative requirements in a WSN include network management which is responsible for making the most economical use of power resources such that WSN can be put into operation for an extended period of time. A fundamental need for the WSN is to communicate with a centralized or base station that sensed data is fused and analyzed. In order to derive an effective WSN, one needs to consider the quality of service (QoS) specified, the topology used in the network architecture and how data is dispatched according to some preferred routing protocol. When the WSN is put into operation, it is always susceptible that the network may be attacked by hackers where data could be intercepted and retrieved illegally. To this end, the WSN has to be designed for deny of service (DoS) prevention and the incorporation of intrusion detection capability. With the desire to enlarge the application domain of WSN, attentions are also directed toward target tracking and data collection in the WSN design.
## 4 Proposal
### Proposed Algorithm
#### 4.2.1 Global Congestion Algorithm (GC)  
The proposed solution aims to assign the route of a vehicle based on the congestion state reported in the different streets of the vehicular network, so when a vehicle decides to follow a route, it experiments the best global congestion state in subsequent streets. Therefore, the future approximated congestion for the vehicle is considered to get the best route the vehicle should follow.  
For this, a node has the information of potential routes and is able to get the congestion state for each one in such a way that the offered route is that with the less global congestion. That means that the vehicle is moving not just in the streets with less congestion, but in the path with less congestion, so the use of any street affects global congestion state of the path, even if is not the next connected street. In this way, we expected the algorithm would not just reacts to congestion (that means, only re-assigning routes when congestion is detected), but he could prevent congestion, reducing it from the first route assignation.  
The potential routes are calculated before the execution of the algorithm, and the strategy is supported on the infrastructure that allows inter-communication node. This route calculation step generates an output that is a list of paths to any node in the topology. With that information, a node is capable to know all the existing paths to another node as long it exists. The route calculation method is not part of the scope of this paper.  
In addition, a node is capable of establish the global congestion state for each path exchanging messages with the nodes ahead, and it receives the recalculation messages triggered by the roadside sensors, or the vehicles ahead in its range. In both cases (a vehicle requests the route or a recalculation is needed), our route assign algorithm is executed by the intersection node to return the corresponding route for a vehicle.  
Figure 15: Route recalculation mechanismThe pseudo code for the Algorithm 1 is presented below.  
```
0: Vehicle's destination node \(d\)
0: All potential paths to \(d\): \(paths=\{p_{1},...,p_{n}\}\)
1: Sort \(paths\) (A to Z) by \(congestion_{p_{i}}\)
2: Path \(path=paths[0]\)
3:\(path_{v}=path\)
4:return\(path_{v}\)
```  
**Algorithm 1**Global congestion (GC)  
The inputs to the algorithm are, the destination node to the vehicle and the path list of potential routes to the destination node. The idea is the algorithms only returns the path, and the assignation takes place for a requiring vehicle, or for a set of vehicles. In the case a vehicle requires the route, the vehicle informs the destination node to the intersection node and the algorithm is executed. In the case a recalculation is needed the intersection node requires for the vehicles in its range, their destination nodes. After, it generates the corresponding paths for each different destination node reported with the algorithm, and informs the vehicles the new path according with their route intention. This avoids the node has to repeat the algorithm for each vehicle when they have the same destination.
# Siberia: A Self-Sovereign Identity and Multi-Factor Authentication Framework for Industrial Access
## 3 Results
### Ongoing Monitoring  
Once access to the service has been granted, continuous user verification is maintained through the integration of the behavioral biometrics module implemented within the service infrastructure. This module enables real-time monitoring of user behavior to confirm the identity of the authenticated holder throughout the session.  
The system captures and analyzes various behavioral parameters associated with user interaction, including mouse movement dynamics, click patterns, and keyboard input behavior. These parameters were detailed in Tables 4 and 5, which summarize the extracted features used to model user behavior through mouse and keyboard activity, respectively. The collected features are processed by a behavioral model specifically trained for individual user profiles.  
This personalized training enables the model to establish a precise behavioral baseline for each subject. During application usage, live input is continuously compared against this baseline, allowing for dynamic and adaptive identity verification. If the model detects significant deviations from the holder's expected behavior--indicating a potential account takeover--the system raises an alert within the service environment. If these anomalies persist over time, the system will automatically terminate the user's session, requiring a new authentication process to regain access.  
The classification and severity of these alerts are determined by a systematic evaluation process. This process involves comparing the prediction values of user-extracted samples against a predefined threshold. To ensure a consistent and uniform scale, these samples are first normalized to the range [0, 1]. Based on the resulting value's deviation from the threshold, the system classifies the alert into one of three escalating levels of severity: (1) basic, for values between the threshold and the threshold plus twice the standard deviation of the error; (2) medium, for values exceeding the basic tier's upper limit but remaining below the midpoint of the remaining interval towards 1; and (3) high, for any value exceeding this midpoint up to the maximum scale value of 1.  
\[\text{Basic level:}\quad x\in[T,\ T+2\sigma)\]  
\[\text{Medium level:}\quad x\in\left[T+2\sigma,\ T+2\sigma+\frac{1-\left(T+2 \sigma\right)}{2}\right)\]  
\[\text{High level:}\quad x\in\left[T+2\sigma+\frac{1-\left(T+2\sigma\right)}{2 },\ 1\right]\]  
where:  
* \(x\) denotes a normalized prediction sample, with \(x\in[0,1]\);
* \(T\) is the predefined threshold;
* \(\sigma\) is the standard deviation of the prediction error.  
The incorporation of this alert-level system enables the activation of response mechanisms associated with each detected risk level.
## 2 A Quantitative Description of a Communication System
### Scale and Resolution  
We propose a quantitative concept of scale: the scale of a system equals the diversity of the language used for its description. Thus, for example, if a picture is made with all available colors in an 8-bit-color map of pixels, then the diversity of the color language of the picture would equal \(2^{8}\), and the scale of the picture description, considering each color as a symbol, would be also \(2^{8}\). Another example would be a binary language, a scale 2 communication system made up of only two symbols. Notice we have used the term \"communication system\" to refer to the media used to code information.  
Interestingly, the system's description scale is determined, in first place, by the observer, and in a much smaller degree by the system itself. The presumably high complexity of a system, functioning with the actions and reactions of a large number of tiny pieces, simply dissipates if (a) the observer or the describer fails to see the details, (b) the observer or describer is not interested the details, and prefers to focus on the macroscopic interactions that regulate the whole system's behavior, or (c) the system does not have sufficient different components, which play the role of symbols here, to refer to each type of piece. It is clear that any observed system scale implies the use of a certain number of symbols. It is also clear that the number of different symbols used in a description is linked with our intuitive idea of _scale_. There being no other known quantitative meaning of the word _scale,_ we suggest its use as a descriptor of languages by specifying the number of symbols forming them.  
Resolution specifies the maximum accuracy of observation and defines the smallest observable piece of information. In the computer coded files we used to interpret descriptions, we consider the character as the smallest observable and non-divisible piece of information.  
Let \(E\) denote the physical space that a symbol or a character occupies, and let the sub-index signal the object being referred to. Thus, considering a written message \(\boldsymbol{M}\), constructed using \(D_{\boldsymbol{M}}\) different symbols \(Y\) as \(\boldsymbol{M}=\{Y_{1},Y_{2},...,Y_{D_{\boldsymbol{M}}}\}\), we would say the message \(\boldsymbol{M}\) occupies the space \(E_{\boldsymbol{M}}\) and each symbol \(Y_{i}\) occupies the space \(E_{Y_{i}}\). We define the length of all characters equal one. Therefore \(E_{C_{i}}\equiv 1\) for any \(i\). Finally, if the number of characters in a message is \(N\), each symbol \(Y_{i}\) appears \(F_{Y_{i}}\) times within the message, and the symbol diversity is \(D_{\boldsymbol{M}}\), we can write the following constraints over the number of characters, symbols and the space they occupy:  
\[E_{\boldsymbol{M}}=\sum_{i=1}^{D_{\boldsymbol{M}}}F_{Y_{i}}\cdot E_{Y_{i}}= \sum_{i=1}^{N}E_{C_{i}}=N\;. \tag{2}\]
## 5 Discussion  
A calibration measurement of the receiver chain was performed before starting the observation campaigns, with the aim to investigate the received signal delay. In this way, the time error and consequently the range error, caused by the receiver chain from the P-band receiver to the dedicated backend based on the USRP-2954R board (see Figure 7), was compensated. After this important calibration, a set of 37 objects were detected between 13 December 2019 and 9 October 2020 to compare measured Doppler shift and slant-range with the data available from the associated accurate ephemerides and results of the TLEs propagation using SGP4. The transmitting power of TRF was set to 5 kW.  
As mentioned above, the EUSST program requires a distance error lesser than 100 m and a Doppler shift error up to 50 Hz. In order to meet these requirements, the ad-hoc system based on the USRP-2954R board has been developed for the BIRALET radar. In the validation measurement campaigns, 37 objects were detected at various distances in the range between 1200 km and 2600 km, as stated above. With the aim to validate the system, the slant-range error and the Doppler shift error have been evaluated considering the absolute value of the subtraction between the estimated value and the measured value.  
The slant-range error for each debris is reported in the graphical representation of Figure 12a. The red line represents the slant-range average error for all detected objects, which is equal to 100.189 m with a calculated standard deviation of 54 m, which is in line with the EUSST requirements. In detail, 20 known RCS objects were observed with a slant-range error lesser than the average error. This represents a significant result that certifies the acceptable performances of the BIRALET radar in the EUSST program. Regarding observed debris with a slant-range error greater than the average error, their TLE could be inaccurate at the moment of the forecast campaign with a consequently pointing coordinates calculation error. The positive aspect is represented by the detection of the objects and the consequently data collection about them, useful for a new characterization of the object position.  
The Doppler shift errors, on the other hand, are represented in Figure 12b. The average Doppler shift error is equal to 37.95 Hz, with a calculated standard deviation of 33.3 Hz, and it is depicted with the red line in Figure 12b. The majority of the observed objects were detected with a Doppler shift error that respects the EUSST requirements.  
Figure 12: _Cont._
## 4 Experimental Platform and Result Analysis
### Dynamic Test Analysis
It is observed that the proposed method has the lowest RMSE and the highest percentage of SNR gain for different amplitudes of mixed-frequency angular vibration signals. As with single-frequency signals, the percentage of SNR gain increases as the amplitude decreases in the improved algorithm. From the dynamic experimental results, it can be seen that the proposed method can effectively improve the SNR and the results are smoother for both single-frequency and mixed-frequency signals.
## 2 Model and Simulation Structure
### Ladder Iterative Technique  
In this study a ladder iterative power flow approach is used to simulate the power flows both in grid connected and islanded mode operation cases. The technique consists of two main steps which are forward and backward sweeps. The method is briefly explained as follows.  
The algorithm starts to work from the end nodes of the system and does the calculations based on Kirchoff Current and Voltage Laws up to the starting nodes. Then this computation order is reversed and computations are performed. The steps of the forward sweep are given as follows:  
* The voltage magnitudes at the end nodes are assumed to be same as the first node (see nodes \(k\) and \(l\) in Figure 2).
* Then the load currents at the end node(s) are calculated by using \(I=(\frac{S}{\Psi})^{*}\).
* The current flows on the lines are calculated (\(I_{jk}\), \(I_{jl}\)). Normally, these values are equal to the negative values of the currents on end nodes. However, for the junction nodes (see node \(j\) in Figure 2) this will be different: i.e., \(I_{ji}\) will be the arithmetic sum of \(I_{kj}\), \(I_{jl}\) and the node current \(I_{j}\) using Kirchoff Current Law.
* By using [PERSON]'s Voltage Law, the voltage on the next nodes are calculated (node \(i\)).  
Figure 1: Co-simulation structure.  
* This computation procedure continues until the initial node. Then the actual voltage magnitude of the initial node is compared to that of the calculated one. If this value is smaller than a predefined tolerance, the algorithm stops otherwise the algorithm continues.
* Perform the backward sweep calculations to compute the voltage magnitudes on the end nodes.
## 3 Materials and Methods
### Methods
#### 3.3.5 The Auxiliary Branch in CloudRCNN (Auxiliary Loss)  
The design of the auxiliary branch in CloudRCNN (auxiliary loss) is shown in Figure 7.  
In satellite cloud images, the foreground is easily differentiated from the background, while clouds look similar. Based on this characteristic, we design the auxiliary branch to additionally supervise the foreground and the background. We reconstruct all the ground truths in the dataset when we supervise the auxiliary branch. We set the pixels in the non-background to be 1, and set the pixels in the background to be 0, which builds up the ground truth of the binary classification of each image with only foreground and background, subsequently enhancing the distinction between the foreground and the background as well. The deconvolution decoder is the main component of the auxiliary branch. After the deconvolution decoder, the resolution of the feature map will be the same as the input image, and the pixel-by-pixel cross-entropy loss \(L_{aux}\) can be calculated. Stochastic gradient descent is used for backpropagation of the loss during training.  
The computation is explained in Formulas (6)-(8).  
\[L=L_{maskrcnn}+L_{aux}; \tag{6}\]  
\[L_{maskrcnn}=L_{mask}+L_{cls}+L_{box}; \tag{7}\]  
\[L_{aux}=\frac{1}{N}\sum-(y_{i}\times log(p_{i})+(1-y_{i})\times log(1-p_{i})) \tag{8}\]  
\(L_{maskrcnn}\) is the loss function of MaskRCNN. \(L_{aux}\) is an auxiliary loss function, used to additionally supervise the foreground and background using a cross-entropy loss function, where \(y_{i}\) represents the label of pixel \(i\), the foreground is 1, and the background is 0. \(p_{i}\) represents the probability that the pixel is predicted to be the foreground.
## 3 Methodological Background
### Gradient Boosting Machine (GBM)  
Similar to RF, the Gradient Boosting Machine (GBM) is an ensemble learning method that uses decision trees as the base learning units. In contrast to RF, which uses relatively large trees and develops prediction through bootstrapping and aggregation, the GBM uses small trees and applies the concept of boosting which improves the learning strength of learner trees in a sequential manner. The GBM algorithm starts with a \"weak learner\" decision tree that fits the data using a simple regression model, and then calculates the error of prediction, e.g., as prediction residuals. It then develops new trees in a sequence, each growing more complex by focusing on the harder to predict examples in the data so as to reduce the prediction error. The prediction error is calculated using a differentiable loss function, and the algorithm searches for the best error reduction tree along the gradient of the loss function. Each tree in the sequence is given a certain weight (called \"learning rate\") equivalent to the step-size (multiplier) of the residuals along the gradient applied to the next tree. The number of trees and the learning rate are determined (optimized) along with other model parameters in the model tuning process. In the end, the algorithm produces a complex ensemble of trees, and the final prediction is made as a weighted prediction of all trees in the ensemble. The study presented in this paper uses the multinomial deviance as the loss function. The GBM has gained a lot of popularity in machine learning modeling due to recent advancements and improvements in the search algorithm and the sampling methodology.
# Gene Editing with CRISPR/Cas Methodology and Thyroid Cancer: Where Are We?
## 4 Gene Editing with CRISPR/Cas in Thyroid Cancer Research
### Targeting Coding Genes in Thyroid Cancer
#### 4.1.1 MAPK Pathway
The loss of expression or mutations in _NF2_ have been detected in thyroid cancer and are linked to Ras-induced tumorigenesis [44]. In this context, CRISPR/Cas9-mediated _NF2_ gene editing efficiently disrupted protein expression in PTC cells but without altering cell growth [45]. However, enforced expression of NF2 in ATC cells inhibited cell proliferation, migration and invasion. Interestingly, _NF2_ mutagenesis (nonsense mutation S288* and Q470*) restored cell growth in ATC cells, indicating a role for the _NF2_ mutations in BRAFV600E -mutated ATCs [45]. Similarly, NF1 is a negative regulator of receptors such as RAS and participates in resistance in aggressive tumors. The murine Hras-driven transgenic model (_TpcCre/HrasG12V/p53 flavor_) develops poorly differentiated thyroid cancer and ATC that are temporarily responsive to famesyltransferase inhibitor Tipifarnib [46]. Acquired resistance to this inhibitor is associated with the activation of Ras downstream signaling due to _NF1_ truncating mutations. Indeed, the CRISPR/Cas9-mediated biallelic loss-of-function mutation in _Nf1_ resulted in an approximate fivefold increase in resistance to Tipifarnib in a murine _Hras;p53_ PDTC cell line [46]. Additionally, _EIF1 AX_, is another gene that cooperates with MAPK, specifically with RAS mutation in advanced thyroid cancer, and is frequently mutated at a hotspot splice-acceptor site upstream of exon 6 (A113 splice) [47]. An _EIF1 AX_-A113-splice mutation with CRISPR/Cas9 knock-in and a donor template via HDR (reviewed in Section 2.3 Mechanism behind Gene Editing) induced cell transformation in normal thyroid cells and increased colony formation in ATC cells [47]. Interestingly, the correction of _EIF1 AX_-A113-splice mutation to wild-type sequence using CRISPR/Cas9 knock-in reduced the colony formation effect in ATC cell line C643.
## 3 Verification Method
### Six-Axis Heart Rate Sensing Device  
The three-axis accelerometer in the six-axis Heart Rate Sensing Device (six-axis HRSD) is a sensor that measures linear acceleration along three orthogonal axes: X, Y, and Z. Based on Micro-Electro-Mechanical System (MEMS) technology, it operates using a mass-spring system structure. When the accelerometer moves or tilts, the internal mass is displaced, causing a change in capacitance or resistance. This change is then converted into an electrical signal proportional to the acceleration. The sensor's output is calibrated relative to the gravitational acceleration (g \(\approx\) 9.8 m/s\({}^{2}\)), enabling the measurement of tilt and movement direction relative to the Earth's surface.  
The three-axis gyroscope in the six-axis HRSD measures angular velocity around the X, Y, and Z axes by utilizing the Coriolis effect. When the gyroscope rotates along a specific axis, the vibration direction of the internal micromechanical vibrating mass is displaced. This displacement is detected by the sensor and converted into an electrical signal proportional to the angular velocity. In other words, the gyroscope quantifies rotational motion, and its output is typically expressed in radians per second (rad/s) or degrees per second (\({}^{\circ}\)/s).