esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Theories
### Fuzzy C-Means Clustering Algorithm  
An FCM clustering algorithm is an unsupervised dynamic clustering method which can divide a sample collection \(X=\{x_{1},x_{2},\cdots,x_{n}\}\) into \(c\) categories (\(2\leq c\leq n\)). The membership of the sample point \(x_{i}\), belonging to the \(j\)th (\(1\leq j\leq c\)) class, is defined as \(u_{ij}\), and \(\mathbf{U}=\{u_{ij}\}\) is the membership matrix, which can be used to denote the fuzzy cluster results. The membership matrix \(\mathbf{U}\) possesses the following properties:  
\[0\leq u_{ij}\leq 1,\sum_{j=1}^{c}u_{ij}=1,0<\sum_{i=1}^{n}u_{ij}<n, \tag{11}\]  
An FCM clustering algorithm is employed to minimize the objective function \(J_{\ell m}\), under the constraint conditions (11).  
\[J_{\ell m}(\mathbf{U},\mathbf{C})=\sum_{i=1}^{n}\sum_{j}^{c}u_{ij}^{m}d_{ij}^{2}(x_{i},c_{j}), \tag{12}\]  
where \(m\) denotes the fuzzy weighting exponent, \(c_{j}\) is the \(j\)th clustering center, and \(d_{ij}\) denotes the Euclidean distance between the \(i\)th sample and the \(j\)th clustering center, which can be described as \(d_{ij}=\|x_{i}-c_{j}\|\). The main iterative process can be expressed as follows:  
1. Provide the number of clustering categories \(c\), the fuzzy weighting exponent \(m\), the iteration stop threshold \(\varepsilon\), and the maximum number of iterations \(T_{\max}\). Then, initialize the membership matrix \(U^{(t)}\) and set the iterations number \(t=0\).
2. The clustering center \(c_{j}\) can be calculated as follows: \[c_{j}=\sum_{i=1}^{n}u_{ij}^{m}x_{ij}/\sum_{i=1}^{n}u_{ij}^{m},\] (13)
3. The membership matrix \(U^{(t+1)}\) can be updated as: \[u_{ij}^{(t+1)}=\left[\sum_{l=1}^{c}\left(d_{ij}^{(t+1)}/d_{il}^{(t+1)}\right) ^{2/(m-1)}\right]^{-1},\,l=1,2,\cdots,c,\] (14)
4. If \(\|U^{(t+1)}-U^{(t)}\|\leq\varepsilon\), then the iteration process terminates. Otherwise, set \(t=t+1\) and return to step (2).
5. Finally, an optimal membership matrix \(\mathbf{U^{*}}\) and clustering center \(\mathbf{C^{*}}\) can be obtained.  
The principle of selecting the near is adapted to recognize the unmarked object types. The Hamming near-degree \(H\) between the unmarked object \(A\) and each clustering center \(c_{j}\), is used to describe the similarity of the two fuzzy subsets. Suppose the number of variables in the sample is \(r\), and the mathematical formula of \(H\) can be given as follows: \[H(A,c_{j})=1-\frac{1}{r}\sum_{k=1}^{r}\big{|}A(k)-c_{j}(k)\big{|},\] (15) The larger \(H(A,c_{j})\) signifies that the two fuzzy subsets are more similar, and the unmarked object \(A\) with the largest \(H(A,c_{j})\) demonstrates that it belongs to the \(j\)th category.
# VFLD: Voxelized Fractal Local Descriptor
## 3 Proposal
### Voxelized Fractal Local Descriptor  
As stated earlier, VFLD is a local descriptor for 3D data represented by point clouds. Thus, the output of the method is an array of features for each point of an input point cloud. To compute the descriptor, a collection of search radii is set as a parameter of the method \(R=\{R_{0},R_{1},...,R_{n}\}\). As described in Figure 4, given a point \(P_{i}\) of the input point cloud P, all the neighbors inside each radius \(R\) are obtained (Figure 5a), thus yielding \(|R|\) subsets. The previously described box-counting method is then applied to each of the subsets, thus obtaining \(|R|\) fractal dimensions (Figure 5b,c), each corresponding to a subset that includes the neighboring points of \(P_{i}\) for each radius \(R_{j}\). The concatenation of the fractal dimensions \(D_{i}\) is the final descriptor \(D\) for point \(P_{i}\).  
It is worth noting that the set of search radii \(R\) and the box-counting iterations \(nIters\) are the only input parameters of the method.
## 5 Experiments
### Experimental Results  
To verify the feasibility of the method, optimize the data generation part, and improve the detection method, we conducted experiments on the flame combustion state model of a cement rotary kiln based on RE-DDPM and DAF-FasterNet. The initial DDPM and FasterNet model were evaluated, and the performance after improvement for these two parts was studied. Additionally, we conducted a large number of comparative and ablation experiments, comparing the results obtained with the proposed methods. The research results include SSIM, FID, and LPIPS for the image generation part and ACC, FLOPs, and latency for the detection part.
# Deployment Strategy of Shore-Based Cooperative Units for the Internet of Inland Vessels
## 4 Case Study
### Algorithm Performance Analysis  
The present study employed a genetic algorithm-based improved heuristic algorithm to address the ship-shore communication base station planning problem. In comparison to commonly used particle swarm optimization [30] and deep reinforcement learning (DRL) [31] methods, the proposed algorithm demonstrated a faster convergence speed. The convergence speed of the algorithm is directly related to the population size, as evidenced in Figure 7, where with an increase in the number of ships in the system, the convergence time of the algorithm also increased. However, the algorithm proposed in this study exhibited a significantly faster convergence speed compared to other algorithms. This advantage stems from the algorithm's inherent memory feature, wherein the genetic information retains the position and velocity information of the optimal solution from the current iteration. Consequently, it follows the trajectory of the current optimal solution during the search update process, leading to faster convergence speed. As depicted in Figure 8, the proposed algorithm surpassed particle swarm optimization and other algorithms in terms of accuracy, making it more suitable for meeting the low latency requirements of safety-related services in inland waterway vessel networking.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Base Station Type**} & \multicolumn{4}{c}{**Base-Station Configuration**} \\ \cline{2-5}  & **Bandwidth** & **Transmit Power** & **Tower/Upright Height** & **Antenna Type** \\ \hline
1.9 GHz 4G & 40 MHz & 47 dBm & 35 m & 11 dBi directional antenna \\
2.4 GHz WiFi6 & 60 MHz & 23 dBm & 20 m & 18 dBi directional antenna \\ \hline \hline \end{tabular}
\end{table}
Table 6: Solutions of base-station planning.  
Figure 7: Comparison of the convergence time of different algorithms.
## 3 Intrinsic and Extrinsic Factors Associated with Hair Graying
### Exhaustion of MSCs and Melanocytes  
Hair pigmentation can be affected by defects or depletion of MSCs, immature precursor cells, and mature melanocytes residing in the hair follicle bulge or bulb [44; 45; 46]. Loss of MSCs within the bulge niche preceded the loss of differentiated melanocytes in the hair matrix [47; 48]. B-cell lymphoma 2 (BCL-2) deficiency caused selective apoptosis of MSCs, but not differentiated melanocytes, while MITF mutation accelerated pigmentation, differentiation, and senescence of MSCs [47]. Expressions of melanocyte markers, such as MITF-M, SRY-box transcription factor 10 (SOX10), paired box 3 (PAX3), pro-opiomelanocortin (POMC), TYR, TYRP1, and tyrosinase-related protein 2 (TYRP2), also called dopachrome tautomerase (DCT), were absent or reduced in the bulbs of white (non-pigmented) hair compared to black (pigmented) hair [49; 50]. White hair shafts grew significantly faster than black hair shafts; the expression of keratins (KRTs) such as KRT6, KRT14, KRT16, KRT25, and KRT83 and keratin-associated protein (KRTAP) 4 isoforms was upregulated in white hair follicles compared to black hair follicles [51]. Therefore, the rapid growth of hair shafts may exhaust MSCs; hence, the incomplete self-maintenance of MSCs may be the causative mechanism for hair graying [47; 52; 53].  
Various cell signaling pathways are involved in maintaining melanoblasts and MSCs in hair follicles [54]. Hair graying was observed in mice deficient in Notch1, Notch2 receptor, or RBP-J kappa transcription factor due to the disappearance of melanocytes in the hair matrix [55]. Therefore, Notch signaling might play an essential role in the melanocyte population during hair follicle cycles [56]. Phosphatase and tensin homolog (PTEN) encodes a phosphatidylinositol-3,4,5-trisphosphate 3-phosphatase which lowers phosphatidylinositol-3,4,5-trisphosphate levels in cells, thereby negatively regulating the phosphatidylinositol 3-kinase (PI3K)-protein kinase B (PKB) signaling pathway [57]. PTEN-deficient mice were resistant to MSC exhaustion in the hair follicle bulge and hair graying induced by repeated depilation in mice [58]. The increased expression of KIT ligand (KITL), also called stem cell factor (SCF), as well as of hepatocyte growth factor (HGF), and endothelin 3 (ET3), which promote melanocyte proliferation of melanoblasts and MSCs, alleviated hair graying induced by repeated hair plucking and shaving in C57 BL/6 mice [59]. On the other hand, the expression of \(\beta\)-catenin in the anagen and telogen skin of aged mice was higher than that in young mice, and overexpression of Wingless and Int-1 (WNT) 10b promoted an excessive differentiation and depletion of MSCs, increasing hair graying [60]. Thus, the excessive consumption, ectopic differentiation, and aging of MSCs hinder the development of mature melanocytes in harmony with the hair follicle cycle, resulting in hair graying [61].
## 3 Technological Advancements in Bee-Based Biosensors/Biomonitors
### Scale Hives and Smart Hives for Bee Management and Monitoring  
Beekeeping equipment companies, as well as some scale-manufacturing firms, now sell Scale Hives. Several companies brought their scale hives or presented data from scale hives at the Montana Hive and Bee Monitoring workshop [47; 91; 92]. Established companies tended to use their own custom data logging systems. The Do It Yourself (DIY) attendees favored Arduino.  
The majority of the technologies at the 2 nd International Hive and Bee Monitoring Workshop brought scales for one or two hives placed on a hive-scale platform, with a single company offering four or six hive capacity. A few scale hive suppliers can now add other sensors to their scale hives, and a few offer communications, most using wireless or cellular systems. Some of the advantages and limitations of continuous hive monitoring were summarized at the Workshop and in a recently published article [93]. Primarily focused on a review of academic studies, the article did not address some of the most recent developments in technology. More and more companies have started introducing commercial versions of their technologies into the marketplace, and some of these investigators and companies only \"publish\" their results in their patents received or pending.  
A recent monograph, \"In Silico Bees\", edited by [PERSON] [60], provides a broader treatment of hive-based monitoring and sensors. There are chapters covering the use of the information obtained in exploring bee society organization, examining bee decision-making, addressing recruitment and allocation of honey bee foragers, modeling infectious diseases, viewing honey bee ecology from a landscape perspective, addressing Quantitative Structure Activity Relationships (QSAR) modeling of pesticide toxicity to bees with mathematical models for addressing chemical contamination into the hive, as well as agent-based modeling of individual insecticides and models for non-apis bees. As with the book he edited in 2002 on estimating the environmental impact of chemicals to honey bees [37], this book provides detailed case history studies and marks the advent of a new field of study.  
All of these new technologies offer opportunities to gather new types of data, often in continuous and real-time mode. That capability then provides a means of networking data sources for real-time monitoring using distance communications. Where available, cellular networks can provide the required communications. Arnia uses cellular networks in Europe. However, in many agricultural regions of the western US, in mountains and in large, isolated areas such as the outback of Australia, satellite communications are the only viable communications option. Here again, satellite communication has become far more affordable. We have found low-cost service packages that are competitive with cellular communication.
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 4 Limiting Factors of Qualification
### Observability of Parameters  
Observability of system parameters is important for monitoring of events during irradiation and assessment of the system health during or after irradiation. It is also crucial for tracking of failure root cause in the system (as it could be seen in the example of the PoL failing under X-ray: the source of failure could be tracked down to a failure of a single component thanks to high observability of parameters, see [22]).  
One of the limitations in the observability is intrinsic for system-level testing, as it comes from the system integration. We can imagine, e.g., OpAmp being part of the complex circuit--it is not possible (or really hard) to measure OpAmp's bias current and many other parameters, while it's embedded in the system. Component-level testing, giving access to all the part's interfaces, is not restricted in this way and enables characterization of a broader number of parameters. However, limitation in the observability resulting from the system integration is not always the drawback, because not all of the parameters of the components in the system are important for monitoring, and usually, the system designer enables monitoring of the most important parameters by providing dedicated interfaces.  
As it was discussed in Section 4.1, the radiation response of an electronic component may depend on the configuration used, such as supply voltage, operating frequency, etc. When testing at the component level, we are free to use any configuration that we want, because we are irradiating only a single part. When testing at the system level, we are limited to the configurations imposed by the system. It may be only one configuration or range of options to choose from but still limited by the design of the system. From this point of view, observability is reduced again by the system that not only limits access to component interfaces but also imposes specific allowed configurations that components may work--but this type of observability reduction should not limit capabilities of qualifying by system-level test, because there is only loss in observing components' performance in configurations that would never be used in the system.
## 3 FRF Estimation
The transfer function from \(\mathbf{\Phi}_{\rm B}\) to \(\mathbf{\Phi}_{\rm out}\) is defined as:  
\[\mathbf{T}\mathbf{F}_{\rm B-out}=\mathbf{T}\mathbf{F}_{\rm C-out}\Big{(}\mathbf{I}-\mathbf{D}_{\rm wa} \mathbf{D}_{\rm ss}^{-1}\Big{)}^{-1}. \tag{34}\]The PSD output of the image motion \(\mathbf{\Phi}_{\text{out}}\) can be rewritten as:  
\[\mathbf{\Phi}_{\text{out}}=\mathbf{T}\mathbf{F}_{\text{B-out}}\mathbf{\Phi}_{\text{B}}\mathbf{T}\mathbf{F} _{\text{B-out}}^{\text{H}}. \tag{35}\]  
\(\mathbf{T}\mathbf{F}_{\text{B-out}}\) can be obtained by the \(\mathbf{H}_{\text{v}}\) algorithm, by taking into account both the input and output noises [28; 29; 30]. The following is a brief introduction to the \(\mathbf{H}_{\text{v}}\) algorithm. The augmented characteristic matrix \(\mathbf{G}_{\text{FFX}}\) can be compiled as shown in Equation (34). \[\mathbf{G}_{\text{FFX}}= \begin{bmatrix}\mathbf{G}_{\text{FF}}&\mathbf{G}_{\text{FX}}\\ \mathbf{G}_{\text{XF}}&\mathbf{G}_{\text{XX}}\end{bmatrix} , \tag{36}\]  
where \(\mathbf{G}_{\text{FF}}\) and \(\mathbf{G}_{\text{XX}}\) are the auto power matrixes of the input (disturbance force and tongue of RWA) and output (image motion in X direction), respectively. \(\mathbf{G}_{\text{FX}}\) and \(\mathbf{G}_{\text{XF}}\) are the cross-power matrixes of input and output. The singular value decomposition of \(\mathbf{G}_{\text{FFX}}\) results in a diagonal eigenvalue matrix \(\mathbf{\Lambda}\) and an eigenvector matrix \(\mathbf{V}\). \[\mathbf{G}_{\text{FFX}}=\mathbf{V}\mathbf{\Lambda}\mathbf{V}^{\text{H}}. \tag{37}\]  
Choosing the column of \(\mathbf{V}\) representing the eigenvector, corresponding to the smallest eigenvalue and normalizing it, such that its last element is \(-1\), the \(\mathbf{H}_{\text{v}}\) estimate can be obtained as shown in Equation (38):  
\[V_{\lambda_{\text{min}}}= \begin{bmatrix}H_{11}\\ H_{12}\\ \vdots\\ H_{16}\\ -1\end{bmatrix} = \begin{bmatrix}\mathbf{H}_{\text{v}}^{\text{T}}\\ -1\\ -1\end{bmatrix} , \tag{38}\]  
when the augmented characteristic matrix becomes \(\mathbf{G}_{\text{FFY}}\), we can obtain [\(H_{21}\), \(H_{22}\), \(H_{23}\), \(H_{24}\), \(H_{25}\), \(H_{26}\)] using the same method. In order to reduce the estimation error of the FRF, geometrically average the FRF at different speeds, as follows:  
\[\mathbf{H}_{ij}= \begin{bmatrix}2\\ \end{bmatrix} \prod_{k=1}^{k=21}\mathbf{H}_{ij-k}(i=1,2,j=1,2,\cdots,6), \tag{39}\]  
where \(\mathbf{H}_{\text{ij-k}}\) is the FRF \(\mathbf{H}_{\text{ij}}\) at 100 \(\times\) (\(k-1\)) rpm. The coherence function is often introduced to evaluate the reliability of FRF. It is defined as the measure of the causal relationship between two signals with the presence of other signals. For the \(i\)th output and the \(j\)th input, the coherence function is formulated, as in [28]  
\[\gamma_{ij}^{2}=\frac{\left|G_{\text{XF}}(i,j)\right|^{2}}{G_{\text{FF}}(j,j) G_{\text{XX}}(i,i)}. \tag{40}\]  
When the coherence function is equal to one, the two signals are completely related. If the coherence function between two input signals is 0, then, the two inputs are completely unrelated.
## 3 Data Analysis and Model Training
### Data Processing
#### 3.2.3 Sliding Window Approach:  
A sliding window approach was used to create time series sequences for LSTM. The window size was set to 10, meaning each input to the model consists of data from the past 10 days, and the step size was set to 1. This resulted in 600 data sequences for model training.  
Each sequence includes the daily energy values and frequencies from the past 10 days, predicting the risk of rock burst for the next day.
## Alternative Organic Solvents in RP-HPLC  
Mobile phases in RP-HPLC are classically mixtures of water, containing additives to adjust pH and ionic strength, and organic solvent. Acetonitrile and MeOH are the two organic modifiers most widely used by HPLC users in the RP. Unfortunately, both solvents are ranked hazardous due to their toxic effect and the great importance placed on the safe detoxification of their waste, even though MeOH is considered more environmentally friendly than ACN, and therefore, should be preferred whenever possible [5]. Since it appears difficult to develop a RP-HPLC method without an organic solvent, a strategy to make this technique greener is to replace ACN and MeOH with other less toxic organic solvents to minimize the environmental and health impacts. The greenness degree of an organic solvent is assessed based on its environmental, health, and safety (EHS) criterion and life-cycle assessment (LCA) [20]. The EHS assessment is composed of environmental indicators (e.g., water and air hazards, and persistency), as well as indicators related to human health (e.g., chronic and acute toxicity, and irritation) and safety (e.g., stability, reactivity, flammability, explosion, and release potential) hazards. It allows the assessment of possible hazards inherent to the solvent properties. The LCA method is used for a detailed assessment of emissions into the environment, as well as resource use over the full life cycle of a solvent. It includes the production, use, potential recycling, and disposal of a solvent. In other words, LCA allows the quantification of environmental impacts that are indirectly attributable to the use of solvents. An organic solvent will be preferable in terms of greenness if it combines good EHS and LCA assessments. Several general solvent selection guides (SSGs), based on EHS only or EHS combined with LCA, have been published [20; 21; 22; 23; 24]. The SSGs have mainly been developed by pharmaceutical industry companies, and they present some differences in the classification of solvents which can be considered as green [23]. These guides have been largely developed to be applied to organic synthesis, and thus, are not adapted to solvent selection for analytical chemistry applications [25]. The organic solvents commonly accepted as green, and which can be used in RP-HPLC, are ethanol, isopropanol, n-propanol, acetone, ethyl acetate, ethyl lactate, and propylene carbonate [25; 26; 27].
###### Abstract  
To improve electrical distribution network reliability, some portions of the network could operate in autonomous mode, provided that the related technical issues are addressed. More specifically, when there is not a path from those portions to the primary substation due to a fault in the network, such portions could be disconnected from the main network and supplied by local generation only. Such a mode of operation is known as \"intentional islanding\" and its effectiveness, in terms of adequacy, depends on the ability of the local generation to meet the island's load. In fact, the ratio between the available local generation and load demand can frequently change during islanding due to load variations and, especially, due to the strongly irregular behavior of the primary energy sources of renewable generators. This paper proposes an analytical formulation to assess local generation adequacy during intentional islanding, accounting for the aforementioned variations. More specifically, the fluctuations of load and green-energy generators during islanding are modeled by means of Markov chains, whose output quantities are encompassed in the proposed analytical formulation. Such a formulation is used by the analytical equations of load points' outage rate and duration. The evaluation of the reliability indices accounts for a protection scheme based on an appropriate communication infrastructure. Therefore, a brief overview on the telecommunications technologies has been presented with reference to their suitability for the specific application. In particular, distribution network safety issues have been considered as the main concern. The results show that neglecting load and generation fluctuations leads to a strong overestimation of the ability of distributed generators to meet the island load. Through a case study it is observed that the error on the load point outage rate is greater than the one affecting the outage duration.  
distributed generation; distribution network reliability; microgrids; renewable generation; smart grids; telecommunications technologies +
Footnote †: journal: Applied Soft 2016
# Overview of Mobile Communications in Colombia and Introduction to 5G
## 1 Introduction
### 5G in Colombia
Previously employed in 4G, the multiple-input multiple output (MIMO) technique simultaneously uses multiple antennas with multiple channels for signal transmission and reception, increasing channel bandwidth and maintaining antenna efficiency without requiring additional power [43; 44]. Antenna gains in the MMIMO (massive MIMO) version used in 5G compensate for channel propagation losses by providing robustness against interference and low latency [45]. OFDM (orthogonal frequency division multiplexing) is another technique used in 5G to reduce the spacing between multiple subcarriers. However, the shorter the spacing, the longer the OFDM symbol duration, and the higher the communication latency [39]. Currently, no operator in Colombia offers 5G mobile communication services. However, the national government, through MinICT, has drawn up a roadmap for the transition from 4G to 5G. With this objective in mind, in 2019 they launched the 5G Plan, which defines the strategies that will allow for the implementation of these networks in the Colombian territory. Figure 4 shows the objectives of the 5G Plan, as well as the lines of action defined in each of them.