esa-sceva/satcom-chunk-collection huggingface dataset:
# A Linear Discriminant Analysis and Classification Model for Breast Cancer Diagnosis
## 3 Materials and Methods
### Improved Linear Discriminant Analysis  
Linear discriminant analysis is a method for simplifying classification problems in supervised machine learning. Using it requires distinguishing between two or more categories; thus, it is employed for modeling set differences. It is a tool for bringing a feature from one dimension down to a lower-dimensional space. The method's goal is to ensure optimal class separability by transforming characteristics from a high-dimensional space into a lower-dimensional space in which the proportion of between-class variation to within-class variance is maximized [31; 32]. Because of its widespread use in the preprocessing of machine-learning classification applications and its ability to transform features into a lower dimensional space by minimizing the ratio of the between-class variance to the within-class variance, linear discriminant analysis (LDA) is the primary feature extraction technique used in this work. An enhancement of LDA (Algorithm 1) is presented herein by simply entering the sample's independent variables and getting the class means and prior probability calculated. We compute the average vector dimensions for each group in the dataset. To determine the scattering matrix requires Eigenvectors (e1, e2,..., ed) and their corresponding Eigenvalues (1, 2,..., d), a dk matrix W can be created by sorting the Eigenvectors by decreasing Eigenvalues and picking the k Eigenvectors with the highest Eigenvalues. Then, the data is reconstructed in a new subspace using the Eigenvector matrix W produced above. Its representation in matrices is as concise as Y = XW. It estimates the pooled covariance matrix and computes the covariance matrices for each group. Following that, it determines the LDA discriminant function and labels the classes.  
```
1. \(En=\), xU \(j\) | \(y=cn\), \(j=1\),..., \(m-1\), \(n=1\), 2//class explicit subcategories
2. \(\mu i=mean(Ei)\), \(i=1\), \(2//\)class means
3. \(C=(\mu 1-\mu 2)(\mu 1-\mu 2)U//among class scatter mediums
4. \(Zn=En-1 mm\mu U\)i, \(n=1\), \(2//\)midpoint class conditions
5. \(Tn=ZU\)\(n\)\(Zn\), \(n=1\), \(2//\)class scatter conditions
6. \(T=T1+T2//\)in-class scatter medium
7. \(\lambda 1,\ x=eigen(T-1C)\)//calculate central eigenvector \(m\)
```  
**Algorithm 1** Improved Linear Discriminant Analysis
## 4 Discussion
### Firmness  
Firmness showed a consistent increase with cassava flour and ginger powder concentration (Table 5). Bread firmness depends on several factors, such as moisture migration and water distribution [52], the interaction of gluten and starch [53], and starch retrogradation [54]. During bread cooling, starch can retrograde and fill the intergranular spaces and provide rigidity, leading to loaf firmness [55].  
The inclusion of cassava flour could produce higher firmness values in bread since increased dough viscosity values were reported with such bread samples [56]. Also, cassava flour addition at 20% has been reported to reduce dough extensibility and elasticity in cassava-wheat flour composite bread [57]. In the current study, bread samples with 40% cassava flour (CW40, CWG41, and CWG43) required more water during the mixing process and registered firmness values more than twice as much as the control sample. A similar result was reported in bread incorporated with 6% ginger powder, which was reported to have more than twice the firmness of bread with 100% wheat flour (control) [37]. The values obtained corroborated previous studies of increased firmness with cassava flour and ginger powder addition in bread [37; 56; 58]. Additionally, the results showed it is possible to replace 10% wheat flour with cassava flour and ginger powder (1 and 3%) without significantly impacting the firmness of bread.
## 2 SMPS Overview
We have applied 60% of the rated load current using the programmable electronic load to the power supply and recorded the input current drawn by the SMPS. \begin{table}
\begin{tabular}{c l l l l l} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Manufacturer**} & \multirow{2}{*}{**Model**} & **Input** & **Output** & **Output** \\  & & & **Current** & **Current** & **Voltage** \\  & & & **AC (A)** & **DC (A)** & **DC (V)** \\ \hline
1 & ABB, Zürich, Switzerland & CP-E & 0.83 & 5 & 24 \\
2 & Dran, Chinfa Electronics, Taiwan & 120-24x & 0.63 & 5 & 24 \\
3 & Entrelec,Germany & System 2A & 0.30 & 2 & 24 \\
4 & Omron, Kyoto, Japan & S8 VS-03024 & 0.60 & 1.3 & 24 \\
5 & Siemens logo, Munich, Germany & 6 EP1331-1 SH03 & 0.70 & 1.3 & 24 \\
6 & Siemens, Munich, Germany & 6 EP1332-1 SH71 & 0.67 & 2.5 & 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Switch-mode power supplies (SMPS) characteristics. Figure 2: Current waveform drawn by SMPS (**a**) Type 1 (**b**) Type 2. The current harmonic limits are defined in the International Electrotechnical Commission (IEC) 61000-3-2. The SMPS less than 600 W comes under class D equipment. Table 2 represents the maximum allowed harmonic current and harmonic currents per watt for odd harmonics [32].
## 2 Materials and Methods
### Data Preprocessing  
#### 2.3.1 Reflectance Conversion Method  
The color values of four DRSPs were extracted from the UAV visible-light imagery for the red, green, and blue bands. These values were plotted against their known reflectance values, which were determined in the laboratory, and fitted to a suitable model. This model, which can vary depending on lighting conditions, is used to convert pixel color values to reflectance values. It is important to note that this conversion process must be performed individually for each image.
# Link Load Correlation-Based Blocking Performance Analysis for Tree-Type Data Center Networks
## 2 System Model  
The switch-centric two-layer tree DCN architecture shown in Figure 1 is considered, where the tree factor is \(K\), each core switch connects \(K\) edge switches, and each edge switch connects \(K\) physical servers. The total number of servers is \(N_{s}=K^{2}\), and the servers are separately denoted by \(H_{1},H_{2}\ldots,H_{N_{s}}\). For simplicity, the edge links between edge switches and servers are called the \(L_{0}\) layer links, while the aggregation links between the core and edge switches are called the \(L_{1}\) layer links. The \(j\)-th \(L_{i}\) layer link is denoted by \(l_{i,j}\). We assume that the \(L_{0}\) and \(L_{1}\) layer links separately contain \(M_{0}\) and \(M_{1}\) sub-channels, and the over-subscription ratio is \(F_{OS}\), i.e., \(M_{1}=KM_{0}/F_{OS}\). The logical sub-channels can be mapped to physical wavelengths/sub-carriers in optical networks [8] or time slots/cycles in time-deterministic IP networks [29].  
In recent years, the spine-leaf network has become one of the most popular commercial DCN architectures. It is assumed that the core and edge switchers are aware of the link states and can determine the optimal link in the next hop. Therefore, ideal routing and load balancing can be achieved when an arrived connection chooses the path. In this case, the multiple spine/core switches can be treated as an equivalent large core switch, and all of the links from one leaf/edge switch to all spine switches can be seen as an equivalent aggregated link from the leaf switch to the large equivalent core switch. Thus, a spine-leaf DCN can be treated as an equivalent tree DCN, where the blocking rate of this tree DCN will be the lower bound for the original spine-leaf DCN. Due to the significant performance improvement of commercial switch products, a two-level spine-leaf DCN is capable of connecting tens of thousands of physical servers, which is sufficient for the networking requirements of most small- and medium-sized data centers.  
In cloud data centers, each physical server can host multiple virtual machines (VM) or containers simultaneously, and end-to-end traffic transmissions may occur between VM or container pairs located in different physical servers. Assume that any two physical servers can generate multiple bi-directional end-to-end connections, and those connections between the same physical server pair are treated as having the same connection type. The DCN traffic matrix is the cumulation of all end-to-end connections. Each connection occupies one sub-channel, and different types of connections arrive independently. Assume that the \(i\)-th type connections are denoted by \(C_{i}\), they follow the Poisson process with arrival rate \(\lambda_{c,i}\), and their durations are exponentially distributed with mean \(1/\mu_{c,i}\). Admission control is adopted, and an arriving connection will be accepted if all links in this connection's path have at least one idle sub-channel. Otherwise, this new connection is blocked and discarded.
## 5 IoRT Systems Based on Space-Borne NTN Networks
### Enhanced and Timely Data Acquisition
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **NTN Architecture** & **Description** \\ \hline
[113] & Generic & Development of an SLA-based automatic network slicing framework for ultra-dense SDN-based CubeSat networks. \\ \hline
[38] & LEO & Evaluating the feasibility of small-satellite swarms for IoT connectivity, focusing on reducing communication overhead and latency. \\ \hline
[67] & LEO & A study that demonstrates the feasibility of a satellite-enabled IoRT network for data retrieval in challenging Tibetan Plateau environments. \\ \hline
[114] & Generic & A metric is proposed to determine the optimal allocation of channel slots for data updates based on the timeliness of the information. \\ \hline
[115] & GEO & Development of a new packet scheduling algorithm for mixed traffic that improves throughput, ensures fairness, and maintains QoS for both delay-sensitive and delay-tolerant IoRT services. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: IoRT systems based on space-borne NTN networks: **enhanced and timely data acquisition**.
# Human Pose Estimation Based on Lightweight Multi-Scale Coordinate Attention
## 3 The Proposed Method
### Bidirectional Feature Pyramid Network
#### 3.4.2 Weighted Feature Fusion  
A familiar technique for fusing features with various resolutions is to initially resize them to an identical resolution and then sum them up. Most prior studies regarded all input features uniformly and without distinction. However, because the resolutions of the various input features vary, they typically provide unequal contributions to the output. To deal with this problem, BiFPN employs a fast, normalized fusion weighting method that boosts detection speed by adding extra weight to each input during feature fusion to make sure the network can understand the value of each input feature [41]. It can be written as follows in mathematical notation:  
\[O=\sum_{i}\frac{w_{i}}{e+\sum_{j}w_{j}}.l_{i} \tag{13}\]  
where \(w_{i}\) is guaranteed to be greater than or equal to 0 by using a Relu activation function after each \(w_{i}\). Based on experience, the learning rate \(e=0.0001\) is set as a small value to prevent numerical uncertainty. Equally, each normalized weight has a value between 0 and 1. This method is much more effective given that it does not use Softmax.  
In this way, BiFPN integrates two-way cross-scale connections and fast normalization fusion.
# Integrated Photonics on Glass: A Review of the Ion-Exchange Technology Achivements
## 3 Telecom Devices
### Wavelength Multiplexers  
A five-channel wavelength demultiplexer-multiplexer has been demonstrated as early as 1982 by [PERSON] et al. using silver multimode waveguides combined with a Bragg grating [56]. More advanced devices using single mode waveguides include Arrayed-Waveguide Grating (AWG) multiplexers, whose quite large footprint is compensated by their low sensitivity to the light polarization thanks to the use of silver based buried waveguides [38]. A good thermal stability provided by the thickness of the glass substrate is also reported but a fine thermal tuning of the AWG's response remained possible [57]. Add and drop multiplexing has been achieved by combining Bragg gratings with Mach-Zehnder interferometers or more originally with a bimodal waveguide sandwiched by two asymmetric Y-branches [58]. Bragg grating can be integrated on glass by etching [59], wafer bonding [60], or photowriting [61; 62; 63].  
Asymmetric Y-junctions are very interesting adiabatic devices that are well adapted to the smooth transitions between waveguides obtained by ion-exchange processes. Therefore, asymmetric Y-junctions have been used as stand-alone broadband wavelength multiplexers. For this type of applications, the asymmetry of the branches is obtained by a difference of the waveguide dimensions and a difference in their refractive index. Tailoring the refractive index of ion-exchanged waveguides can be achieved by segmenting the waveguide as demonstrated by [PERSON] et al. [64]. As can be seen on Figure 8, using vertical integration of deeply buried waveguides with selectively buried waveguides allowed obtaining a very broadband duplexing behavior while maintaining a relatively small surface footprint [36].  
Figure 7: 1 to 8 power splitter made by ion-exchange on glass (**a**) early demonstration in 1986; (**b**) qualified pigtailed and packaged commercially available product.
## 3 Fast Hyperbolic Hough Transform  
### Problem Formulation  
The measurement scenario is shown in Figure 1a. Let the sensors be denoted by \(S_{i},i=1,2,\ldots,N\); \(S_{i}\) is deployed at location \((x_{i},\,y_{i},z_{i})\). The event, emitted by emitter \(E\) at an unknown time instant \(t_{0}\) from an unknown position \((x_{0},\,y_{0},0)\) on a given plane, is detected by sensor \(S_{i}\) at time instant \(t_{i}\). Notice that this situation (often referred to as the 2.5D scenario) is commonly encountered in practice when the emitter is located at ground level, but the sensors are placed at positions of arbitrary height.  
The signal propagation speed (e.g., the speed of light or the speed of sound, depending on the signal type and medium) is \(c\). The distance between \(S_{i}\) and \(E\) is \(d_{i}\):  
\[d_{i}=c(t_{i}+n_{i}-t_{0}), \tag{1}\]  
where \(n_{i}\) is the time measurement error of \(S_{i}\) and \(t_{i}+n_{i}\) is the ideal time measurement.  
The distance difference \(d_{i,j}\) is defined as follows:  
\[d_{i,j}=d_{i}-d_{j}=c\big{(}t_{i}-t_{j}+n_{i}-n_{j}\big{)}=ct_{i,j}+\Delta d_{ i,j}, \tag{2}\]  
where \(t_{i,j}=t_{i}-t_{j}\) is the measured TDOA between sensors \(S_{i}\) and \(S_{j}\), and \(\Delta d_{i,j}\) is the distance difference error:  
\[\Delta d_{i,j}=c\big{(}n_{i}-n_{j}\big{)}. \tag{3}\]  
Let us assume that the time measurement error \(n_{i}\) can be modeled by Gaussian noise \(n_{i}=\mathcal{N}(0,\sigma_{t,i})\), and \(n_{i},\ n_{j},i\
eq j\) are independent. Then,  
\[\Delta d_{i,j}=\mathcal{N}\big{(}0,\sigma_{i,j}\big{)}, \tag{4}\]  
where the variance \(\sigma_{i,j}^{2}\) of the distance difference \(d_{i,j}\) is  
\[\sigma_{i,j}^{2}=c^{2}\Big{(}\sigma_{t,i}^{2}+\sigma_{t,j}^{2}\Big{)}. \tag{5}\]  
Let the reference sensor be \(S_{1}\). Let us calculate the distance differences from the TDOA measurements \(t_{i,1}\):  
\[\overset{\sim}{d}_{i,1}=ct_{i,1}. \tag{6}\]  
The objective is to provide position estimate \(\left(\widetilde{x}_{0},\widetilde{y}_{0}\right)\), given sensor positions \((x_{i},y_{i},z_{i})\), measurements \(\overset{\sim}{d}_{i,1}\), and variances \(\sigma_{i,1}^{2}\). The estimation process is illustrated in Figure 1b.
# Energy Efficiency Optimization in Massive MIMO Secure Multicast Transmission
## 4 Simulation Results
Besides, it can be indicated that we can find a suitable power allocation to meet our requirement of high energy efficiency. At the same time, we can also find that the energy efficiency does not increase with the number of antennas. This is because under large antennas and low power constraint, the secure rate changes faster than power consumption at the BS with the increase of the maximum power constraint, while the dynamic power consumption of the BS antennas increases rapidly under the high power constraint, leading the situation on the contrary. Finally, when the number of antennas increases, the system needs more power to reach better energy efficiency. Besides, the energy efficiency performance of the Monte Carlo method is quite close to the proposed one, which confirms the accuracy of the analysis.
# Reversible Data Hiding in Encryppted Images with Extended Parametric Binary Tree Labeling
## 4 Experimental Results
### Security Analysis
#### 4.1.2 Entropy of Image  
The advantage of encrypted images is that the information patterns about the pixels cannot be inferred. Therefore, to further verify the security of the proposed scheme, we use information entropy to calculate the pixel randomness of the images at different phases. The information entropy can be obtained from Equation (7), where \(s\) represents the gray level of an image pixel, \(p(s_{i})\) represents the relative probability of a particular gray level \(s_{i}\) in the image, and \(2^{n}\) represents the total number of gray levels.  
\[H(s)=-\sum_{i=0}^{2^{n}-1}p(s_{i})\cdot\log_{2}(p(s_{i})). \tag{7}\]  
We conducted experiments using the test images in Figure 13 including Lena, Man, Jetplane and Baboon. Table 2 shows the information entropy of the test images at different stages, from which we can see that both the encrypted image and the embedded encrypted image are very close to 8. The theoretical maximum of the information entropy is 8, and the closer to 8 the higher the randomness of the image is. This shows that the proposed scheme security is reliable.
## 4 Recent Advancements in Applications
### Mapping and Map Updating  
One of the primary applications of MMSs appears in the process of creating 2D and 3D maps [126]. The valuable potential of MMSs in providing accurate 3D spatial information makes them appropriate tools for the map generation chain. Usually, the initial outputs compiled from MMSs raw data do not comply with the typical cartographic and mapping standard requirements used in the 2D map generation process; however, these outputs are precise sources of information to either produce or update 2D cartographic maps. 3D maps usually result from processing point clouds into simplified 3D meshes usable in applications such as virtual reality, 3D GIS, and games.  
Three-dimensional maps gradually find their way into real estate, where detailed virtualizations help the sector to maintain buildings and present venues for selling or renting [127]. These maps are also invaluable resources of geometric data for other applications, such as wireless network coverage analysis that requires optimizing placements of antennas [128].  
On the civil side, many construction projects use MMSs to monitor and document the progress of projects, e.g., in the mining sector, 3D maps are used to ensure the correctness of projects [129]. In road construction projects, MMSs are employed to address tasks such as estimating cutting/filling [130] and project monitoring and management [131].  
Documenting cultural heritage is another important application of MMSs. Besides the artistic values that MMSs provide, precise measurements could help collect invaluable information regarding small- to large-scale sites that are prone to dangerous situations such as natural disasters. Therefore, it seems necessary to employ precise 3D maps generated from MMSs for documenting historical sites [132].
## 3 SOC Balancing
### Parameter Design  
The proportional coefficients \(k_{\text{ph}}\) and \(k_{\text{arm}}\) critically influence the SOC balancing speed across phases and arms. Increasing the proportional coefficient accelerates the balancing speed. Theoretically, the SOC balancing speed should be maximized to ensure optimal performance. However, with a constant proportional coefficient, the balancing speed declines sharply as the SOC converges. Employing a larger proportional coefficient to reduce the balancing time may inject excessive unbalanced circulating current into the system. This current may exceed the switches' peak current rating, compromising system operation. To address this issue, this paper proposes an adaptive proportional coefficient-based SOC balancing strategy.  
First, the phase balancing variance \(d_{\text{ph}}\) and the arm balancing variance \(d_{\text{arm}}\) are calculated separately to quantify the SOC consistency, as expressed in the following equations:  
\[d_{\text{ph}}=\frac{\sum\limits_{j}^{\text{a,b,c}}\left(S_{\text{soc},j}-S_{ \text{ave}}\right)^{2}}{3}, \tag{19}\]  
\[d_{\text{arm}}=\frac{\sum\limits_{j}^{\text{a,b,c}}\sum\limits_{i}^{\text{p,n} }\left(S_{\text{soc},rj}-S_{\text{soc},j}\right)^{2}}{6}. \tag{20}\]  
Next, the adaptive coefficient \(f(d)\) is formulated by jointly considering the SOC balancing speed and the switches' safe operating range. This function determines the adaptive proportional coefficients for phase and arm SOC balancing, as defined below:  
\[f(d)=ae^{-b(d-bd_{0})}, \tag{21}\]  
\[k_{\text{ph}}=k_{\text{ph}0}f(d_{\text{ph}})=k_{\text{ph}0}ae^{-b(d_{\text{ph}} -bd_{\text{ph}0})}, \tag{22}\]  
\[k_{\text{arm}}=k_{\text{arm}0}f(d_{\text{arm}})=k_{\text{arm}0}ae^{-b(d_{\text{ arm}}-bd_{\text{arm}0})}, \tag{23}\]  
where \(a\) and \(b\) are the scaling factors. To prevent excessive circulating current at the initial time, \(a\) and \(b\) are constrained within the range of 0 to 1. \(k_{\text{ph}0}\) and \(k_{\text{arm}0}\) are the initial constant proportional coefficients, while \(d_{\text{ph}0}\) and \(d_{\text{ph}0}\) are the initial balancing variances of phases and arms, respectively.  
With the adaptive coefficient, the proportional coefficients for both phase and arm SOC balancing increase during SOC convergence. This adaptation maintains rapid balancing speeds, consequently reducing the total balancing time. Additionally, the scaling factor constrains initial circulating currents, avoiding adverse impacts on system stability.