esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning Models for the Classification of Histopathological Images of Colorectal Cancer
## 2 Materials and Methods
### Machine Learning Classification Algorithms
#### 2.3.6 Naive Bayes  
Naive Bayes (NB) classification, a probabilistic machine learning technique rooted in Bayesian inference, has garnered significant attention across diverse domains for its simplicity and effectiveness. Developed on the principle of conditional independence assumption among predictors, naive Bayes classifiers offer computational efficiency and scalability, particularly in scenarios with high-dimensional feature spaces [27]. Despite its inherent simplifications, empirical studies have demonstrated the competitive performance of naive Bayes classifiers compared to more complex algorithms, especially in text classification tasks [28]. Furthermore, the interpretability and ease of implementation of naive Bayes models render them particularly attractive for real-world applications, where rapid deployment and transparency are crucial factors [29].
# Numerical Simulation of Co-Barrier Efficiency of Air Curtains in Mine Refuge Chambers
## 2 Model Development
### Validation of Computational Models  
The CO2 concentration obtained using this numerical model is compared to the experimental measurement value of the concentration without the air curtain in the prior publication, namely Ref. [34], to demonstrate that the model is reliable and accurate. [PERSON] et al. [34] used a section of the outside environment in front of the transition room of the refuge to experimentally study the environment of a tunnel filled with toxic and hazardous gases. The outside environment is sealed with plastic film and the blast door between the transition room and the external environment is closed, at which point the outside environment is a confined space. Carbon dioxide was released into the outside environment through the dispersion pipe, and the fan is turned on for mixing. By detecting the CO\({}_{2}\) concentration in the outside environment, when the CO\({}_{2}\) concentration in the outside environment reaches 2% (20,000 ppm) uniformly, the release of CO\({}_{2}\) is stopped, and the fan is turned off. While opening the air curtain system, the explosion-proof door between the outside environment and the transition room is opened, and four carbon dioxide sensors are arranged in the outside environment to monitor the decreasing trend of CO\({}_{2}\) concentration, and three carbon dioxide sensors are arranged in the transition room to monitor the increasing trend of CO\({}_{2}\) concentration.  
The present numerical model is found better in accordance with both qualitative and quantitative terms, with deviations varying in the range of 10 to 15%, which indicates it can be effectively used for the following research study, see Figure 8.
## 5 Simulation Model and Results
### Performance at Varying Bit Error Rate (BER)  
We first select a random network configuration by randomly distributing five cooperative nodes in the cooperative region, whose topography is shown in Figure 8.  
Figure 7: Average Energy consumption per bit (in mJ) at various nodes for CARQ and C-HARQ protocols, with \(nCN=5\).  
The same as earlier, the BER of all the links in the network are obtained from the \"average BER _vs._ the inter-nodal distance\" graph [3]. Let's denote these obtained BER values of Source-to-Destination (S-D) link, _CNode_-to-Destination (CN-D) link and Source-to-_CNode_ (S-CN) link with _ber_S-D_, _ber_CN(i)-D_ and _ber_S-CN(i),_ respectively, where \(i\in\{1,2,...nCN\}\). These BER values are found to be of the order of \(10^{-3}\). Now, to see the effect of BER variation of a particular link on the performance of C-HARQ, while the other links maintain their original BER values, we use a multiplication factor \(\alpha\) such that it is multiplied to the original BER value of that particular link and varied over the required range. For example, the BER of the Source-to-Destination link can be varied, by varying the value of \(\alpha\) in the relation: _BER_S-D = \(\alpha\times\)ber_S-D_.
# Automated Evaluation of C-ITS Message Content for Enhanced Compliance and Reliability
## 4 Discussion  
The results indicate that the testing was conducted successfully. Both tools, C-ITS SIM and Karlos, fulfilled their roles and enabled the execution of the defined scenarios.  
During these testing scenarios, several warnings were noted for certain parameters in both CAM and DENM cases. The testing was carried out under laboratory conditions, and thus, some parameters in the CAM were set to \"unavailable\" for the unit. From a testing perspective, this is not a significant issue; however, in real-world deployment, it will be crucial to configure these parameters correctly according to the specific conditions of the vehicle.  
For DENMs, the testing of the third-party OBU revealed certain shortcomings that may impact real-world conditions. For instance, the headingValue parameter in DENMs was outside the permitted range, which is a critical issue that could affect interoperability and safety. Additional issues include missing information on speed, altitude, and direction, which are essential for accurate situational assessment in dynamic environments. Although these issues did not lead to critical errors under laboratory conditions, it will be necessary to adjust and complete these parameters to ensure appropriate performance and reliability of the system in practical use.  
Reflecting on the limitations of the current methods, future research should address the challenges posed by dynamic traffic conditions and the performance of automated tools in such environments as well.  
Before implementing the automated evaluation methods, reliance was heavily placed on the stack of the unit in determining compliance, which could lead to uncertainty if messages were misclassified. Our results showed a message that passed even though it had errors, demonstrating the necessity for automated tools in confirming compliance more effectively than traditional methods, like Wireshark, which can be cumbersome and inefficient.  
In addition to the test results themselves, it is also important to focus on improving the testing tools to achieve a higher degree of automated evaluation. For the Karlos tool, enhancements such as better filtering and message searching (i.e., parameter settings and their ranges) would be beneficial. For the C-ITS SIM, a potential future improvement could be the capability to send messages that do not conform to the standard (to check the response of the third-party OBU).  
The use of C-ITS technologies is anticipated to bring significant social and economic benefits by improving traffic efficiency and safety. The automation of the testing process helps save time, making it a more effective approach compared to traditional manual methods.
## 3 Results and Discussion
### The Base Constraint Ability  
The base constraint ability is essential for fabricating microstructures with accurate shapes and layouts. Herein, thermal reflow process of cylinder microlens array is simulated to explore the base constraint ability of PDMS assisted thermal reflow. Figure 3(a1-c1) and (a2-c2) show the velocity distributions at different times (0.2, 2 and 3 s) during the thermal reflow process conducted in the air and PDMS, respectively. In Figure 3(a1), most melting photoresist has a large velocity, which indicates rapid corner rounding and photoresist creeping at the beginning of the thermal reflow. Then, the velocity of the top melting photoresist decreases greatly, while the velocity of melting photoresist near the bottom surface still keeps relatively larger, as shown in Figure 3(b1,c1). It indicates that the dynamic substrate wetting drives the constant shape evolution after the initial fast shape transition. Similarly, the velocity of most melting photoresist is large at the beginning of PDMS assisted thermal reflow, as shown in Figure 3(a2). However, it is relatively smaller than that shown in Figure 3(a1) due to the introduction of PDMS. Figure 3(b2) shows the velocity distribution at the moment when there is a large viscosity gradient in the height direction. It can be clearly observed that the dynamic wetting of bottom melting photoresist is constrained, while the top melting photoresist keeps on creeping with low velocity. Figure 3(c2) shows the velocity distribution when all the surrounding PDMS is almost cured. The flow of all the melting photoresist, especially the bottom melting photoresist, is further restricted. During the whole PDMS assisted thermal reflow process, the dynamic wetting along the substrate is constantly constrained.  
Figure 4a,b are shape evolutions of cylinder microlens during the thermal reflow conducted in the air and PDMS, respectively. Comparing the shape evolution near the substrate, which is indicated by the dashed box shown in Figure 4a,b, it can be observed that the dynamic wetting along the substrate is constrained in Figure 4b. A typical cylinder microlens array with a little gap is simulated, and the comparison between the simulated  
Figure 3: The simulated velocity distributions of cylinder microlens array during thermal reflow process: at **(a1)** t = 0.2 s, **(b1)** t = 2 s and **(c1)** t = 3 s conducted in the air, at **(a2)** t = 0.2 s, **(b2)** t = 2 s and **(c2)** t = 3 s conducted in the PDMS. The black lines in figures represent surface and interface, respectively.  
and experimental profiles is shown in Figure 4c. It shows that the simulated profile and the experimental profile have a good agreement. The adjacent microlens do not merge together, which indicates that the base is constrained very well. Figure 4d shows the volume fraction contour and SEM image of the cross-section of the cylinder microlens, respectively. The simulated formed shape is in good agreement with the experimental results.
# Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data
###### Abstract  
Texture as a measure of spatial features has been useful as supplementary information to improve image classification in many areas of research fields. This study focuses on assessing the ability of different textural vectors and their combinations to aid spectral features in the classification of silicate rocks. Texture images were calculated from Landsat 8 imagery using a fractal dimension method. Different combinations of texture images, fused with all seven spectral bands, were examined using the Jeffries-Matusita (J-M) distance to select the optimal input feature vectors for image classification. Then, a support vector machine (SVM) fusing textural and spectral features was applied for image classification. The results showed that the fused SVM classifier achieved an overall classification accuracy of 83.73%. Compared to the conventional classification method, which is based only on spectral features, the accuracy achieved by the fused SVM classifier is noticeably improved, especially for granite and quartzose rock, which shows an increase of 38.84% and 7.03%, respectively. We conclude that the integration of textural and spectral features is promising for lithological classification when an appropriate method is selected to derive texture images and an effective technique is applied to select the optimal feature vectors for image classification.  
textural feature; spectral feature; Jeffries-Matusita distance; lithological classification; Landsat 8
# FedRDS: Federated Learning on Non-IID Data via Regularization and Data Sharing
## 3 Proposed Method
### Federal Regularization  
To obtain more benefits from the computational resources of the edge devices and increase the amount of light computation to reduce the performance divergence between different local clients caused by non-IID, the dynamic regularization strategy is employed to enable the local loss function to converge to the global model gradually. During the training process, the selected client \(k\in S_{r}\) uses the untrained local model \(\Theta\) and the global model \(\Theta^{l}\) sent from the server to regularize the loss function. Formally, we solve the following optimization problem:  
\[\Theta_{k}^{l+1}=\operatorname*{argmin}_{\Theta}l_{k}(\Theta)+\frac{\sigma}{2} \
orm{\Theta-\Theta^{l}}^{2} \tag{2}\]  
Take the first derivative of Equation (2) to obtain the following equation:  
\[\
abla l_{k}(\Theta_{k}^{l+1})+\sigma(\Theta_{k}^{l}-\Theta^{l})=0. \tag{3}\]  
Figure 2: Client drift under the non-IID setting.  
In the \(t\)-th round of training, the value of hyperparameter \(\sigma\) in the client regularization term \(\frac{\zeta}{2}\|\Theta-\Theta^{t}\|^{2}\) is calculated based on the similarity between the initial local model \(\Theta_{k}^{t}\) of client \(k\) and the global model \(\Theta^{t}\) sent by the server, as shown in the Equation (4):  
\[\sigma_{k}^{t}=exp(sim(\Theta_{k}^{t},\Theta^{t})) \tag{4}\]  
Among them, \(sim(\Theta_{k}^{t},\Theta^{t})\) represents the cosine similarity between \(\Theta_{k}^{t}\) and \(\Theta^{t}\).  
The global objective function needs to satisfy the following formula,  
\[\
abla\mathcal{L}(\Theta)\triangleq\frac{1}{K}\sum_{k=1}^{K}\
abla l_{k}( \Theta)=\sum_{k=1}^{K}\mathbb{E}_{(\mathrm{x},y)}\
abla\mathcal{L}(\Theta;( \mathrm{x},y))=0. \tag{5}\]  
We assume that \(\Theta_{k}^{*}\) is the optimal solution of the local loss function \(l_{k}\) satisfying \(\
abla l_{k}(\Theta_{k}^{*})=0\), \(\Theta^{*}\) is the optimal solution of \(\mathcal{L}(\Theta)\) in Equation (1), and \(\Theta^{*}=\frac{1}{K}\sum_{k=1}^{K}\Theta_{k}^{*}\) in FL. But, in the non-IID setting with data distribution \(\mathrm{p}_{i}(\mathrm{x},y)\
eq\mathrm{p}_{j}(\mathrm{x},y)\), there is \(\Theta_{i}^{*}\
eq\Theta_{j}^{*}\), which leads to \(\Theta_{k}^{*}\
eq\Theta^{*}\). Different client updates towards the local subproblem optimal solution \(\Theta_{k}^{*}\), causing \(\Theta^{*}\) to be inconsistent with the theoretical value, which leads to a decrease in global model accuracy and slow convergence. In Equation (2), we incorporate a regularization term into the initial loss function to slightly modify the local loss function and create a dynamic link between the global and local models that gradually aligns the local target with the global target, thereby minimizing the influence of the client drift.
# Efficient User-Serving Scheme in the User-Centric Cell-Free Massive MIMO System
## 2 Network Model and Spectral Efficiency
### Uplink Data Transmission  
In the uplink data transmission phase, all \(K\) users send their data to the APs. The same channel condition is used in this phase as in the downlink data transmission phase. In this paper, we do not derive the uplink data transmission. The details for uplink data transmission are shown in [8].
## 3 Results and Discussion
### Thickness, Opacity and Light Transmittance of Films
\begin{table}
\begin{tabular}{c c c c c c} \hline Sample & Thickness & Opacity & T 280 nm & T 600 nm & Water \\ Names & (\(\upmu\)m) & (A \(\times\) mm\({}^{-1}\)) & & & Activity \\ \hline Pectin(control) & 102 \(\pm\) 2 \({}^{\text{a}}\) & 0.73 \(\pm\) 0.01 \({}^{\text{e}}\) & 6.49 \(\pm\) 0.89 \({}^{\text{a}}\) & 83.83 \(\pm\) 0.38 \({}^{\text{a}}\) & 0.43 \(\pm\) 0.01 \({}^{\text{e}}\) \\ \hline P/NE:1 & 63 \(\pm\) 1\({}^{\text{e}}\) & 1.42 \(\pm\) 0.04 \({}^{\text{c}}\) & 0 \({}^{\text{b}}\) & 11.51 \(\pm\) 0.82 \({}^{\text{b}}\) & 0.58 \(\pm\) 0.03 \({}^{\text{a}}\) \\ \hline P/NE:2 & 72 \(\pm\) 2 \({}^{\text{d}}\) & 2.37 \(\pm\) 0.07 \({}^{\text{a}}\) & 0 \({}^{\text{b}}\) & 2.15 \(\pm\) 0.25 \({}^{\text{e}}\) & 0.52 \(\pm\) 0.02 \({}^{\text{b}}\) \\ \hline P/NE:3 & 80 \(\pm\) 2 \({}^{\text{c}}\) & 1.67 \(\pm\) 0.05 \({}^{\text{b}}\) & 0 \({}^{\text{b}}\) & 4.58 \(\pm\) 0.50 \({}^{\text{d}}\) & 0.52 \(\pm\) 0.01 \({}^{\text{c}}\) \\ \hline P/NE:4 & 91 \(\pm\) 3 \({}^{\text{b}}\) & 1.21 \(\pm\) 0.03 \({}^{\text{d}}\) & 0 \({}^{\text{b}}\) & 7.93 \(\pm\) 0.46 \({}^{\text{c}}\) & 0.50 \(\pm\) 0.04 \({}^{\text{d}}\) \\ \hline \end{tabular} The data are presented as the average \(\pm\) one standard deviation (\(\mu\) = 3). Means that share identical letters show no significant differences (\(p<0.05\)).  
\end{table} 
Table 2: Physical properties of the film. showed a higher transmittance compared to the other samples, which may be due to the thinner thickness and the high concentration of TC. The high concentration of TC may saturate the incorporation capacity of the pectin matrix, resulting in the presence of TC on the surface of the P/NE:1 film during the drying process. The reduction in transmittance by the addition of the nanoemulsion in the UV range (at 280 nm) is higher than in the visible range (at 600 nm). This suggests that the effect on the UV barrier properties of the film was more pronounced than the effect on the transparency of the film. This is worth noting because UV rays can cause oxidation and food spoilage. Thus, films with UV barrier properties can be considered suitable packaging that can extend the shelf life of foods [46]. The incorporation of rosemary oil into blended films based on carboxymethyl cellulose and polyvinyl alcohol also caused the reduction of transmittance at 280 and 600 nm wavelengths due to the inhibition of light transmission by nanoparticles as reported by [46].
# Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains +
## 4 Results
### DTMC-\"Content Purchase\" Screens
#### 4.1.2 Expected Time to Payment-Screen A&B
It is also interesting to note that although there are more AOIs on screen A (6: A-F) than on screen B (4: A-D), the expected time to reach the payment screen on screen A is almost 2 s faster than B. Despite having additional options to choose from, the TV customer is more efficient in making their choice when there are \"Buy and Rent\" HD and SD options rather than when there are only \"Buy\" options available.
# A Variety of Choice Methods for Image-Based
## 3 The Proposed Methods
### The Post-Processing Framework  
For NST, users hope to improve three issues: the color scheme, the strength of the style of the strokes, and the adjustment of image contrast. To satisfy the demands of users, the \(S_{\rm C}\) image from modified UST method (UST_5) combined with the image fusion and color enhancement methods to design a good post-processing framework to tackle the above-mentioned three issues simultaneously. Equation (10) can use the blending technique to re-adjust the mixing ratio between the C and \(S_{\rm C}\) images. A possible approach is to integrate the blending technique as shown below  
\[\begin{bmatrix}R^{\prime}\\ G^{\prime}\\ B^{\prime}\end{bmatrix}=\frac{k_{1}\cdot I_{B}+(1-k_{1})\cdot I_{A}}{I_{B}-k_{ 2}\cdot a_{B}}\cdot\begin{bmatrix}R_{B}-k_{2}\cdot a_{B}\\ G_{B}-k_{2}\cdot a_{B}\\ B_{B}-k_{2}\cdot a_{B}\end{bmatrix} \tag{10}\]  
where \(a_{B}=\min(R_{B},G_{B},B_{B})\), the parameter \(k_{1}\) ranging from 0 to 1 is for adjusting the blend ratio between the C and \(S_{\rm C}\) images to adjust the stroke strength, \(k_{2}\) ranging from \(-1\) to 1 is for controlling the degree of saturation compression or stretch. \(I_{B}\) and \(I_{A}\) are the C or \(S_{\rm C}\) images to solve the color scheme. Based on Equation (10), six different modules are as follows  
1. The content-preserved (\(C_{P}\)) module when \(k_{1}=k_{2}=0\), \(I_{A}\) is the content image, and \(I_{B}\) is the stylized image.
2. The style-preserved (\(S_{P}\)) module when \(k_{1}=k_{2}=0\), \(I_{A}\) is the stylized image, and \(I_{B}\) is the content image.
3. The \(S_{P}\) module with content/style blending when \(0\ \leq k_{1}\ \leq\ 1\), \(k_{2}=0\), \(I_{A}\) is the stylized image, and \(I_{B}\) is the content image.
4. The \(C_{P}\) module with content/style blending when \(0\ \leq k_{1}\ \leq\ 1\), \(k_{2}=0\), \(I_{A}\) is the content image, and \(I_{B}\) is the stylized image.
5. The \(S_{P}\) module with content/style blending and the contrast adjusting when \(0\ \leq k_{1}\ \leq\ 1\), \(-1<k_{2}<1\), \(I_{A}\) is the stylized image, and \(I_{A}\) is the content image.
6. The \(C_{P}\) module with content/style blending and the contrast adjusting when \(0\ \leq k_{1}\ \leq\ 1\), \(-1<k_{2}<1\), \(I_{A}\) is the content image, and \(I_{B}\) is the stylized image.  
The two most important modules of the proposed approach are (5) and (6). Modules (5) and (6) can not only combine the spatial details of the style/content image with the color information of the content/style image, but also control the compression or stretch of the image saturation. By making some adjustments the value of \(k_{2}\), we can solve the problem of low contrast. Thus, they can provide multiple-choice methods for automatic colorization in the new stylized image which will be shown in the next section.
# The Design and Simulation of an Astronomical Clock
## 8 The Set-Up of the Clock Mechanism Motion Study  
For the correct operation of the astronomical clock mechanism, the initial positions of the Sun, the Moon, the zodiac, the lunar phase and the lunar node pointers must be chosen first. The best way to accomplish this task is to adjust the pointers' positions for the date on which the Moon is located in its orbital perigee or apogee. This decision is reasonable since the modeled clock mechanism exclusively determines the mean motions and positions of the aforementioned astronomical objects and the true and mean position of the Moon in its orbital apogee or perigee is approximately the same. The Moon was in apogee on 18 May 2020 [14] and this date has been chosen for the astronomical clock set-up. The ecliptic longitudes for the Sun, the Moon and the lunar node \(\mathbb{N}\) are taken from ephemeris tables for 2020 [15] which are calculated for each day at Greenwich Mean Midnight. On 18 May 2020 at midnight 00:00 UT, the ecliptic longitude of the Sun was \(\mathbb{O}\) 27.52\({}^{\circ}\), that of the Moon \(\mathbb{P}\) 5.13\({}^{\circ}\) and the mean longitude of the lunar node \(\mathbb{N}\) was \(\mathbb{S}\) 0.93\({}^{\circ}\). If the clock is located in Belgrade, latitude 20\({}^{\circ}\)28\({}^{\prime}\), it is necessary to calculate the local mean solar time for this place. This is done by the following calculation [16]:  
\[LSoT=LST+4^{\prime}(LL-LSTM)+ET=1+4^{\prime}(20.467-15)+0=1:21:52 \tag{11}\]  
Figure 14: The assembly of the astronomical clock mechanism.  
In Equation (11), _LSoT_ is the local solar time, _LST_ the local standard time, _LL_ the local longitude, _LSTM_ the local standard time meridian measured in degrees which runs through the center of each time zone and _ET_ is the equation of time. Neglecting _ET_, the local solar time becomes local mean solar time. In accordance with these data, the initial positions of the Sun, the Moon, the zodiac, and the lunar node pointers are adjusted on the astronomical clock face. The correct lunar phase is determined by the angular distance between the Sun and the Moon pointers.  
After the clock pointers have been adjusted, the parameters of the mechanism motion study are also determined. A rotary motor with an angular velocity of 30 rpm is attached to the clock input shaft and the kinematic simulation is performed with 36 steps per second.