esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reconfigurable SRAM CRP PUF with High Reliability and Randomness
## 3 Proposed SRAM CRP PUF  
### Proposed Precise Timing Scheme for Ideal Random Response  
Figure 2 depicts the proposed PUF cell structure and its timing diagram. The main objective is to eliminate the mismatch effect of the challenge input by exterminating its power-up function. Two revisions are introduced for this purpose. Firstly, a timing diagram revision is presented, as shown in Figure 2b, where the \"Configuration & Evaluation\" mode is divided into two separate modes. In the \"Configuration\" mode, the challenge input is set up to configure the PUF cell before the _PRE_ signal becomes high. Subsequently, the two cross-coupled inverter arrays power up to compete with each other, determining the output logic level in the \"Evaluation\" mode. However, this revision results in the PUF outputs dropping during the challenge input setup due to the discharge current to the VSS through the NMOS challenge transistors. Thus, the PUF cell remains powered on in the \"Configuration\" mode even when the _PRE_ signal is low. Consequently, a second revision is proposed. An adjunct NMOS transistor is added into the PUF cell structure, operating as power-up transistor, as shown in Figure 2a. This transistor is controlled by the PRE signal to disconnect the PUF cell from the VSS during the \"Pre-charge\" and \"Configuration\" modes; it then powers it up in the \"Evaluation\" mode. An appropriate sizing selection must be considered to provide an adequate current-carrying capability for this transistor. The size of this transistor must be sufficiently large to carry the maximum current when all the NMOS challenge transistors are turned on. Thus, the size of the PUF cell is increased compared to the conventional structure.  
Additionally, the input signal, WL, is set to a high level after a sufficient time has elapsed since the start of the \"Evaluation\" mode to avoid capacitance load changes at the two inverter array outputs during their conflict. This ensures that the two outputs of the SRAM core exhibit fully diverse values before the data are read out to the bit-line.  
Figure 2: (**a**) Unit cell schematic of the proposed SRAM CRP PUF and (**b**) its operational timing diagram.
# Numerical Analysis of Human Cancer Therapy Using Microwave Ablation
## 3 Results  
As mentioned above, the electromagnetic and bio-heat equations are coupled. The operating frequency of the microwave generated by the antenna was 2.5 GHz. The model is able to be solved using the finite element method. The results mainly concern:  
* The microwave power density absorbed by the liver tissue \(Q_{ext}\).
* The specific absorbed rate which is defined as:
* \(SAR=\frac{Q_{ext}}{\rho}\), where \(\rho\) is the density of the tissue.
* The temperature profile and the maximal values reached.
* The fraction of the tissue necrosis which is related to the tissue damage.
## 3 Results and Discussion
### Effect of the Shear Force  
As can be seen in Figure 8b, the shear force must be considered for a short period of time as the spade begins to move backward relative to the machine advancing direction. Because it happens approximately on the minimum of the spade-tip trace, the input of the _enabling_shear_force_ block comes as the \(\dot{y}\) turns to be positive. Figure 4 shows that the shear force will be enabled by the _bit_force_disabler_ block for a short time through an inverted logical signal's rising edge. In the MATLAB(r) model, the timer subsystem from [21] is used. The duration of the action can be set, and for the present investigation, the 0.02 s time parameter is used. The modulus of the shear force is calculated based on Equation (7) and applied in the _shear_force_equ_ block (Figure 11) as MATLAB(r) script. In the third block, the load is applied as a vector along the normal of the spade plane acting in the middle of the spade according to Figure 8a.  
The effects of shearing forces for working cases IV, V, and VI (Table 2) are presented in Figure 12. Because the load magnitude is proportional to the assembly's acceleration, the shear effect is considerably greater by higher revolutions of the crankshaft. Considering working case IV, the shear load is totally overlapped over the maximum torque for 0-depth working: It starts at 0.1316 s and lasts 0.02 s, raising the maximum torque by 21.3% until the value 2121.22 Nm. For the other two cases, the maximum torque is raised even more drastically by 78.85% (case V) and 239.25% (case VI), but the absolute value of the torques are reduced to 923.43 Nm and 686.10 Nm.
# Interferometric DEM-Assisted High Precision Imaging Method for ArcSAR
## 5 Accuracy Analysis
### The DEM Accuracy Analysis
#### 5.1.3 The Effects of Phase Errors on DEM Accuracy  
With reference to Equations (25), (26) and (28), we can derive the relationship between DEM accuracy \(\sigma_{h}\) and the above two types of phase error:  
\[\sigma_{h}=\frac{\lambda R_{\mathrm{sp}}}{4\pi\Delta z}\Big{(}\big{|}\sigma_{ \mathrm{ps}}\big{|}+\big{|}\sigma_{\mathrm{pd}}\big{|}\Big{)} \tag{34}\]  
According to Equation (34), Figures 13 and 15, the \(h\)-\(\sigma_{h}\) curve can be obtained, as shown in Figure 16.  
Figure 14: The \(h\)-\(p_{\mathrm{d}}\) curves.  
Figure 15: \(h\)–\(\sigma_{\mathrm{pd}}\) curve.  
Referring to the conclusion of Section 3.2, when the \(R_{\rm sp}\) = 300 m, the height difference threshold is 38.26 m. Therefore, if the accuracy of the DEM for assisting imaging is better than 38.26 m, the proposed imaging method can obtain high-precision ArcSAR imaging results. The curve in Figure 16 shows that the DEM accuracy does not exceed 0.5 m, which is much smaller than the height difference threshold. Thus, we can draw the following conclusion: Even if the SAR images used for interference appears defocused, the obtained DEM can still be used to assist ArcSAR imaging and acquire high precision image.
# Understanding Customers' Transport Services with Topic Clustering and Sentiment Analysis
## 3 Materials and Methods
### Document Clustering
#### 3.5.1 Clustering Algorithm: K-Means Description  
K-Means is an unsupervised hard and partitional clustering algorithm. It was first developed by [PERSON] in 1967 [32] and is one of the most widely used algorithms for grouping data.  
The basic idea of this algorithm is closely related to the optimization of the inertia criterion. At first instance, the K centroids are defined by randomly selecting K different instances [34]. Then, the remaining data points from the model will be assigned to the clusters whose inertia score is the minimum. After this cluster-data point association, centroids (\(\mu_{i}\)) will move to the mean of its distribution of data points S as explained in Equation (1).  
\[\mu_{i}^{(t+1)}=\frac{1}{|S_{i}^{(t)}|}\sum_{\mathbf{x}_{j}\in S_{i}^{(t)}} \mathbf{x}_{j} \tag{1}\]  
This process is repeated until the position of the cluster-centroids does not variate in each iteration, where we can affirm that the algorithm has converged to an optimal solution.  
The partitional nature of K-means will result in the creation of K groups where each instance will only belong to a single cluster.  
Scikit-learn [35] Python's library was selected to implement the algorithm due to its simplicity, good results, and efficient performance in computing time.
## 4 Self-Organizing Maps Applied to Driving-Behavior Classification
### SOM-Based Drivers Grouping Regarding Fuel-Consumption  
In Section 3.1, the most relevant features for fuel consumption characterization were selected: mean PGP, mean ERPM, mean GP, and the variance of positive XACC. Concurrently, window size and window shift were analyzed to preserve a high correlation between the above features and fuel consumption. The features were computed over 256-sample windows (i.e., 8 s) with 50% overlapping between consecutive windows. The number of available training windows per driver varies slightly between drivers, depending on their DS, traffic, and quality of the measurements. On average, there are 115 windows per driver, while the whole set of driving samples consists of 2290 windows (i.e., more than 2.5 driving hours). Three-quarters of the four-dimensional samples will be used to train an SOM, which is to say \(K=1717\), keeping the remaining quarter for testing purposes. The data were normalized before training in order to avoid distortion in the results due to the use of Euclidean distances (Equation (2)).  
The number of output neurons of the SOM was initially set using [PERSON]'s rule [56], which defines the optimal number of neurons as \(M=5\sqrt{K}\). Thus, a \(14\times 14\) SOM topology (i.e., \(M=196\)) was defined and repeatedly trained. However, because the corresponding U-matrices showed overly smooth maps, the size of the SOM was gradually reduced until a suitable map was obtained. Figure 8a depicts a meaningful U-matrix achieved with a 121-neuron map. The U-matrix shows a clear border (line of dark neuron connections) between potential clusters. Moreover, it shows two regions (upper left and lower right) where neurons are rather close (light colors). This implies that those neurons are associated with samples that are substantially similar to other samples in the dataset. However, a different number of clusters can be found, depending on the distance threshold. This aspect of the SOM will be exploited with the aim of delving into different DSs and developing a strategy for real-time improvement of fuel consumption.  
As the number of neurons in the map (\(M=11\times 11=121\)) is less than the number of samples (\(K=1717\)), most of the neurons in the map are the BMU or hit of several samples in the dataset (see Figure 8b). As can be seen, there are neurons across the map with 4 or fewer hits, which match the  
Figure 8: SOM organized into an \(11\times 11\) neuron grid. (**a**) Neighbor weight distances. The blue hexagons represent the output neurons, while the red lines are neuron connections. Darker colors represent larger distances between neighboring neurons, and lighter colors represent smaller ones. (**b**) Sample hits. This image shows how many training samples are associated with each neuron.  
regions with dark neuron connections in Figure 8a. These units could be considered to be interpolating neurons, smoothing the transitions between clusters.
# Significance of Single-Interval Discrete Attributes: Case Study on Two-Level Discretisation
## 2 Discretisation
### Entropy and MDL Principle in Supervised Discretisation Algorithms  
Supervised discretisation algorithms condition the process of finding cut points and constructing intervals based on their usefulness for the distinction of concepts. Candidate cut points are firstly located, each is evaluated, and the best one (optimal) selected. In this group of methods, entropy-based measures are often employed to evaluate cut points.  
The class information entropy of the candidate partitions is a measure of purity. It evaluates the amount of information needed to specify to which class an instance belongs. In the top-down approach, at the beginning, one range is considered containing all values of a discretised attribute. This interval is recursively divided into smaller bins until an optimal number of intervals is obtained or the stopping criterion is met. As a stopping criterion, the Minimum Description Length principle can be used, with roots in Bayesian inference and Kolmogorov complexity.  
The MDL principle states that among the given set of hypotheses, the one with the minimum combined description lengths of both the hypothesis and the error for a given set of data is the best approximation of the mechanism behind the data and can be used to predict the future data with the best accuracy. The principle was introduced by [PERSON] [20] and adopted for the induction of decision trees [21] and rules. This principle can be considered as a method of inductive inference and the basis of statistical modelling and machine learning. It can be used for model selection, prediction, and estimation problems, especially where the models under consideration are complex. The MDL principle is also used as an element of discretisation algorithms.  
The Fayyad and Irani [5] and Kononenko [6] algorithms are the most popular supervised discretisation methods. The first one evaluates the midpoint between neighbouring pairs of sorted values of an attribute as a cut point and selects this candidate cut point for which the entropy is minimal. This binary discretisation is applied recursively and refers to the MDL principle as the stopping criterion. In the Kononenko method, the MDL principle is applied in the framework of selecting the most compressive attribute.
## 3 Results  
### Accuracy  
Table 1 summarizes the mean differences of the position estimation error in different courses. The mean absolute error of all position estimations (n = 8344 samples) was 5.2 \(\pm\) 3.1 cm for the x-position and 5.8 \(\pm\) 2.3 cm for the y-position. This represents percentage of differences of 0.97% \(\pm\) 1% for the x-coordinate and 0.94% \(\pm\) 1.14% for the y-coordinate. The limits of agreement in the estimation  
of errors were established between 2.1 to 8.3 cm for the x-coordinate and 3.5 to 8.2 cm for the y-coordinate. No significant differences were found between the designed routes and between devices.
## 4 Role of sRNA in Antibiotic Resistance
### Biofilm-Mediated Increased Drug Resistance in Bacterial Pathogens
#### 4.4.3 SrbA  
The small RNA SrbA plays a crucial role in _P. aeruginosa_ biofilm formation. A _srbA_ deletion mutant exhibited a 66% reduction in biofilm formation while maintaining normal surface attachment in a _C. elegans_ slow-killing model. The complementation of _srbA_ restored wild-type biofilm levels, indicating SrbA's essential function. Genome-wide analysis (using TargetRNA2 and BLASTn) identified 61 putative mRNA targets of SrbA, includinggenes linked to biofilm formation, motility, metabolism, and stress response, suggesting broad posttranscriptional regulation. RT-qPCR revealed significant transcriptomic changes in the _srbA_ mutant during biofilm growth, with 12 genes downregulated \(\geq\)4-fold and 7 upregulated \(\geq\)2-fold. These findings suggest that SrbA modulates biofilm formation by influencing mRNA stability and translation efficiency, potentially via interactions with the ribosome binding site or RNase E recruitment [225].  
As illustrated above, sRNAs contribute to antibiotic resistance in bacterial pathogens via diverse mechanisms. Notably, these regulatory RNAs also play critical roles in quorum sensing pathways, which can further potentiate multidrug resistance, as explored in the following section.
# Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption
## 5 Experimental Results and Analysis
### Security Analysis
#### 5.3.2 Structural Similarity  
The Structural Similarity Index (SSIM) is a widely utilized quantitative metric in the domains of image processing and computer vision.  
\[SSIM(X,\ Y)=\frac{\left(2*\mu_{x}*\mu_{y}+C1\right)*\left(2*\sigma_{xy}+C2\right) }{\left(\mu_{x}^{2}+\mu_{y}^{2}+C1\right)*\left(\sigma_{x}^{2}+\sigma_{y}^{2}+C2 \right)} \tag{24}\]  
In Equation (24), \(X\) and \(Y\) respectively denote two images, \(\mu_{x}\) and \(\mu_{y}\) represent the mean luminance of these two images, \(\sigma_{x}\) and \(\sigma_{y}\) represent their luminance variances, and \(\sigma_{xy}\) denotes the luminance covariance between the two images. The constants C1 and C2 in the formula are typically set as:  
\[\begin{cases}C1=\left(k1*L\right)^{2}\\ C2=\left(k2*L\right)^{2}\end{cases} \tag{25}\]  
In Equation (25), \(k1\) is a small positive constant (typically 0.01), \(k2\) is another small positive constant (typically 0.03), and \(L\) represents the dynamic range of pixel values (for 8-bit images, \(L=255\)). By assessing the similarity of images in terms of luminance, contrast, and structure, a comprehensive similarity metric is obtained, ranging from 0 to 1. A higher SSIM value closer to 1 indicates greater similarity between the two images, while a value closer to 0 suggests more significant dissimilarity. The experimental results are shown in Table 10. The SSIM mean value of ten sets of data is 0.01, which is significantly different from the original image and can serve as an encryption method.
## 2 Materials and Methods
### Mfa_R Processes
#### 2.3.1 Registration Process
Through this pseudocode, it is possible to notice some implementation details that are more easily presented in the form of code (i.e., function callbacks). ```
Result: result Functionregister(registration_data: Device) : booleanis \(result\) = FALSE;  connect_tls(regServ);  send_data(registration_data); wait(callback) receive(\(reg\_confirm\)); if\(reg\_confirm=-1\)then exit (1);  end if save_data(\(reg\_confirm\)); \(result\) = TRUE; returnresult;  end if
```  
**Algorithm 1**Device registration process--client-side  
In the first presented Algorithm 1, the client-side registration produces a simple flow of this process. However, it is important to highlight some details in the code, such as the secure connection (line 3) and the saving of record data at the end of the process (line 9). These are details that reinforce the need to use a secure channel at the time of registration as well as the need to save registration confirmation data (Section 2) on the client side as well. The second Algorithm 2 presents the process from the server's point of view (\(gw\)). This algorithm explains the processes of verifying the existence of a previous record (already registered--line 5), generating the record data (lines 8 to 10), and saving the data in a database on the server side (line 11). In this manner, it was possible to formalize the necessary steps on the server for the registration process. The registration process is the first step of all authentication and where data between client and server are exchanged. It is of paramount importance to fully understand this process, as well as the functions and components it will require (i.e., hash functions and data structure) for its proper functioning. Through this understanding, we will be able to better understand the details of the other authentication processes (Section 2.3.2), enforcement (Section 2.3.3), and data publication (Section 2.3.4). ```
Result: result Functionregister_device() : booleanis \(result=\) FALSE; receive(device_data); \(hash\_device=hash(device_data)\); ifexists(hash_device)then  return \(-1\);  end if\(timestamp=\)generate_timestamp(); \(user=\)generate_user(); \(passwd=\)generate_password();  db.save_device(hash_device_device_data,timestamp,user,passwd); return(timestamp,user,passwd);  end if
```  
**Algorithm 2**Device registration process--server-side
# Profiling Taste and Aroma Compound Metabolism during Apricot Fruit Development and Ripening
## 3 Discussion
TPS was considered the key enzyme for volatile monoterenes, which was consistent with the decrease found in apricot fruit throughout the development and ripening process and the correlation analysis. Thus, TPS may also be involved in monoterenes biosynthesis in apricot fruit. Likewise, significantly higher enzyme activities of aroma volatile metabolism were found in peels than in pulps, which suggests that peels have more strong aroma volatile synthesis abilities than pulps have. The majority of aroma volatiles in peels were significantly higher than those in pulps, which was consistent with their OAVs of each compound and the changes of metabolic enzyme activities. Furthermore, the contents of \(\beta\)-ionone, \(\beta\)-damascenone and \(\gamma\)-decalactone in LT and BXwere significantly higher than those in other cultivars, which suggested that white apricot presented stronger flowery and peach-like aroma than other cultivars. This change was consistent with CCD and ACX enzyme activities in LT and BX. A PLSR model was developed with the aim of identifying the main variables influencing consumer acceptability. This procedure allowed a rapid assessment of relationships between the dependent variable (Y) and a set of potentially explanatory variables (X). To determine the variables that attribute to consumer acceptance of fruit, a PLSR model for apricot (including cultivar and development) was developed. In the present study, panel test was used to assess whether the changes in flavor compounds could directly affect aroma, taste and consumer acceptance of apricot fruit. We found that LT and BX fruit had a markedly higher aroma, sweetness, flavor rating and consumer acceptance than other cultivars, along with lower sourness. These results suggest that LT and BX present superior fruit flavor quality to other cultivars, and that consumer acceptance depends on predominant characteristic flavor compounds. The PLSR model was used to identify the main contributors to flavor quality and consumer acceptance, and the result showed that \(\beta\)-ionone, \(\gamma\)-decalactone, sucrose, citrate, sweetness, aroma and flavor were significantly positive with consumer acceptance, but markedly negative with malic acid, hexanal, (E,Z)-2,6-nonadien and sourness, which were consistent with the changes of flavor compounds and organoleptic evaluation. Meanwhile, we found there were positive correlations between aroma, flavor and acceptability, which implied that flavor plays a central role in fruit quality and it triggers consumers' acceptance or choice.
# Streaming-Based Anomaly Detection in ITS Messages
## 2 Materials and Methods
### Data Generation, Pre-Processing and Transformation
#### 2.1.2 Data Pre-Processing and Transformation
The driver is deemed to have noticed the obstacle when a message comes within range of it (based on distance computation using latitude and longitude), and the data in the message are changed. The speed is lowered by a number chosen at random from a defined range of values. The subsequent messages will be updated as well, depending on the position and length of the obstacle. The data anomalies reflected incidents on the road when cars had to lower their speed as they approached the obstruction, maintain the reduced speed for the length of the obstacle, and then restore regular speed after passing the obstacle. The presented obstacles simulate road accidents (such as a stalled car, an accident, or road debris), allowing us to label the messages of the vehicles affected by this incident as anomalous. The remaining messages were not altered. Figure 2 presents the trajectories (in blue) on Boulevard Dauphinot (route N51) and the obstacle section, showing normal points in green with anomalies in red. Figure 1: Illustration of an accident scenario on the road. Figure 2: Area of interest; (**a**) trajectories on Boulevard Dauphinot (route N51); (**b**) obstacle section: normal instances in green and anomalies in red; (**c**) obstacle section showing only the anomalies. The vehicle ID, timestamp, latitude, longitude, vehicle speed, and heading are used to detect multivariate anomalies. The data are encoded as a data stream and fed into an ensemble-based technique for event detection that makes use of the windowing concept. Given that the focus is on non-recurring events, the anomalies of interest give rise to an imbalanced data set. Therefore, let \(\mathcal{A}_{m}\) be the set of CAM message anomalies. Then,  
\[\forall m,Pr(m\in\mathcal{A}_{m})<<1-Pr(m\in\mathcal{A}_{m})\]  
Further, let \(\mathcal{A}_{T}\), the set of (sub-) trajectory anomalies, then  
\[\forall T_{k},Pr(T_{k}\in\mathcal{A}_{T})<<1-Pr(T_{k}\in\mathcal{A}_{T})\]