esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Basic Theory and Techniques for SOM
#### 3.3.1 SOM Algorithm Flow  
The SOM neural network obtains the winning node by outputting neurons in the competition layer, and the weight of the connection between the winning neuron and the input layer needs to be changed. The weight value is changed to make the difference between the input variable and the winning neuron smaller and smaller, so that similar input variables are like the weight of the output neuron connection, and similar input variables are clustered into the same class.  
The specific algorithm steps are as follows:  
(1) Initialize  
Set the initial value of learning rate \(\alpha(0)\), the initial value of neighborhood radius \(r(0)\), and the neuron weight vector \(w_{ii}\).  
(2) Look for winning neurons  
Calculate the distance \(d_{j}\) between the input vector and the output neuron to find the winning neuron.  
\[\mathrm{d_{j}}=\sqrt{\sum_{i=1}^{m}\left(x_{i}-w_{\bar{\mathrm{ij}}}\right)^{2 }}, \tag{12}\]  
where \(\mathrm{X_{i}}\) is the selected vector; \(\mathrm{W_{ij}}\) is the weight of the ith neuron in the input layer and the jth neuron in the output layer. The one with the smallest distance is the winning neuron. The neuron satisfies Equation (10).  
\[\mathrm{s(i)}=\mathrm{argmin}\big{|}\big{|}x_{\mathrm{i}}-w_{\bar{\mathrm{ij}}} \big{|}\big{|} \tag{13}\]  
(3) Update weights  
Figure 7: SOM Network Structure and Neural Lattice Array.  
The weight of the winning neuron within the radius of the neighborhood is updated, and the learning adjustment of the weight vector is shown in Equations (11) and (12).  
\[w(t)=w(t-1)+\alpha(t)A(x_{i}-w_{i}), \tag{14}\]  
\[A=\begin{cases}1,&c=q\\ 0.5,&c\in r_{q}(t)\\ 0,&\text{others}\end{cases}, \tag{15}\]  
Where c represents neurons in the output layer; q is the winning neuron; \(r_{q}(t)\) is the winning neighborhood.  
(4) Update learning rate and neighborhood radius  
\[\eta(t)=(1-t/T)\times\eta(0), \tag{16}\]  
\[r(t)=(1-t/T)\times r(0), \tag{17}\]  
(5) Judge whether the training times reach the preset value and, if so, end the training; otherwise continue training.
# Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements
## 6 System Design and Implementation
### Calibration of Scanner Device  
Since the BLE beacon's advertisement packets are received by the Bluetooth chipset located inside the scanner device, it is important to calibrate the device to account for different locations of the Bluetooth chipset on the printed circuit board (PCB) for different models of the smartphone. We provided a calibration option on the scanner application that allows the user to calculate the calibration offset, which is used for adjusting the measurements on every consecutive scan.  
The algorithm for estimating the calibration offset is the same as the DOA estimation algorithm but measured from one location. The calibration process can be started by placing the BLE beacon approximately 1 m away from the scanning location, pointing the scanner device directly at the beacon, and selecting the _'calibrate_' button on the application. Algorithm 1 provides the detailed step-by-step instructions of this process.  
```
Open application; Start calibration; Stand 1 m away from the BLE beacon; Point the scanner device at the beacon; Hold scanner device steady and rotate d 360\({}^{\circ}\); Stop calibration.
```  
**Algorithm 1**Calibration procedure  
At the end of the process, the scanner application is calibrated to mitigate the position of the Bluetooth chipset, and the offset angle is remembered for future scans.
# Wearable Plasma Pads for Biomedical Applications
## 2 Experimental Methods
### In Vitro and In Vitro Biocompatibility Testing of the Plasma Pad  
For in vitro biocompatibility tests, cytotoxicity was assessed using green fluorescent protein (GFP)-transfected keratinocytes (HaCaT cells) and fibroblasts (L-929 cells), as previously described [24; 25; 26]. Briefly, the GFP-transfected HaCaT and L-929 cells were maintained in high-glucose Dulbecco's modified Eagle's medium (DMEM) with 10% fetal bovine serum (FBS) and 1% antibiotic solution. Each cell line was seeded on cover glasses (\(\varphi\) 12 mm) in 24-well plates (Coster Corp., Bethesda, MD, USA) at 1.5 \(\times\) 10\({}^{5}\) cells/mL and incubated at 37 \({}^{\circ}\)C in 5% CO\({}_{2}\) for 24 h. After incubation, the cover glasses were transferred into a non-coated 35 mm dish with 2 mL of DMEM media and the plasma pad was placed about 1 to 2 mm distant from the media in the dish. Each cell line on the cover glass was exposed to plasma by treating the media surface for 0 min, 1 min, 3 min, 5 min, and 10 min and the cover glass was transferred into a 24-well plate and incubated at 37 \({}^{\circ}\)C in 5% CO\({}_{2}\) for another 24 h. Cell viability was calculated using the absorbance value of each well at 450 nm after incubation with a Cell Counting Kit-8 (CCK-8) solution (Dojindo Laboratories, Kumamoto, Japan). To confirm morphological changes, each cell was examined with an imaging reader (Cytation 3; BioTek Instruments, Inc., Winooski, VT, USA). The data shown for biocompatibility in this study were obtained from two independent experiments (sample number \(n\) = 4) and were expressed as mean \(\pm\) standard deviation (SD) for quantitative data. Statistical comparisons were performed with a Student's \(t\)-test, and \(P\) < 0.05 was considered statistically significant.  
To evaluate in vivo biocompatibility, a skin irritation test was conducted on hairless mice using a method described in the ISO 10993-10 [27]. Briefly, 10 healthy hairless mice were used for in vivo experiments and were divided into 2 groups. Group 1 served as the control, and Group 2 was treated with plasma for 10 min. After plasma treatment, signs of edema and/or erythema in each mouse were checked at 24 h and 48 h, and any responses in the plasma-treated group were also evaluated according to the method in the ISO 10993-10. Histological analysis was also performed after staining with hematoxylin and eosin (H&E).
## 4 System Model
### Energy Consumption Model  
In IoT networks, the primary energy-consuming factor is the SNs using energy to communicate with their designated DFHs and the DFHs expending energy to communicate with the sinks or other DFHs (e.g., routing). Additionally, another significant source of energy consumption is the process of data fusion, where DFHs aggregate and process data received from multiple SNs to reduce the amount of data transmitted further up the network. This involves computational energy costs for operations such as filtering, compressing, and merging data streams, which add to the overall energy expenditure of the network. Consequently, radio energy and channel propagation models, which were previously described in [47; 48], are used in our simulations. An _R-bit_ packet requires the transmitter to expend the following energy in order to move over a distance \(d\)[47; 48]:  
\[E_{TX}(R,d)=\left\{\begin{array}{ll}R\times E_{dcc}+R\times\varepsilon_{fs} \times d^{2},&if\ d<d_{o},\\ R\times E_{dcc}+R\times\varepsilon_{mp}\times d^{4},&if\ d\geq d_{o}.\end{array}\right. \tag{14}\]  
When an _R-bit_ packet is received, the following energy consumptions occur [47; 48]:  
\[E_{RX}(R,d)=E_{dcc}(R), \tag{15}\]  
where the term \(E_{dcc}\), measured in nJ/bit, in Equations (14) and (15) refers to the energy consumed by the transmitter or receiver electrical circuits per bit. In the multipath fading channel model and the free space model, we utilize \(\varepsilon_{mp}\) and \(\varepsilon_{fs}\), respectively, to quantify the energy consumption per bit of the amplifier. The distance between the transmitter and the receiver is indicated by \(d\). The threshold distance, also known as the crossover distance (\(d_{o}\)), is the point of change between the two communication models previously explained. To determine the value of \(d_{o}\), we employ the subsequent method [47; 48]:  
\[do=\sqrt{\left(\varepsilon_{fs}/\varepsilon_{mp}\right)}. \tag{16}\]  
Moreover, the energy consumed during data aggregation (also referred to as data fusion) can be expressed as follows:  
\[E_{DA}=N\times E_{fusion}, \tag{17}\]  
where \(N\) represents the number of SNs whose data are aggregated by DFH, \(E_{fusion}\) is the energy required to process (aggregate) a single bit of data, measured in nJ/bit/signal, and \(E_{DA}\) stands for total energy consumed for data aggregation. Therefore, if \(R\) represents the size of a data packet (in bits) received from each SN, then the total energy consumed for aggregating \(N\) SNs' data packets is as follows:  
\[E_{DA}=N\times R\times E_{fusion}. \tag{18}\]
# Robust Algorithm for Calculating the Alignment of Guide Rolls in Slab Continuous Casting Machines
## 2 Assumptions and Considerations
### Indirect Relationship Between Angles and Positions  
Figure 3 illustrates, why the measured angles are only an indirect measure of the alignment condition of a CCM. It shows four outer rolls with only radial error components (red), and the tangent lines (purple), whose angles an SCM would measure. A naive approach would directly compare the measured angles to the ideal angles defined by the roll plan. However, this approach will not be able to determine the errors of, for example, rolls 52 and 53 since the angle between them is consistent with the roll plan. On the other hand, looking at roll pair 54 and 55, the approach would report a difference between the measured and the ideal angle, even though roll 55 is in the correct position. Solving this issue is the main motivation for the development of the algorithms in this paper.  
Figure 3: Illustration of the indirect relationship between roll position errors and measured angles.  
Figure 2: Position error components and their directions. The component of the position error that is perpendicular to the passline is called “radial error” in reference to the CCMs casting radius (see Figure 1). The error component tangential to the passline is called “tangential”.
## 3 Methods
### Feature Completion Mechanism  
As shown in Figure 2, the generator adopts a Transformer-based structure for semantic feature completion, consisting of an encoder, a feature extractor, and a decoder. The encoder and decoder each contain four upsampling and four downsampling modules. The downsampling part uses convolution operations for four-time downsampling, and the downsampled feature layers are input into the TransformerBlock for feature extraction. Convolutions are then used for upsampling to achieve feature recovery. This structure combines the following advantages of convolutions and Transformers: the convolution operation avoids the inductive bias caused by large-scale pre-training [38], while the global modeling capability of the Transformer helps capture long-range dependencies and establish global context associations; the TransformerBlock compensates for the limited receptive field of convolutions, enabling the network to search for global information and expand the receptive field; and the convolution operation retains position information, which helps enhance the network's generalization ability, and helps it avoid overfitting [39]. Compared to pure Transformer architectures (such as SwinUNet [37]), the fusion of convolutions and Transformers provides the network with richer position information, which is beneficial for restoring image details during the upsampling process.
## 3 Results  
The mean, standard deviations, and minimum and maximum values of Ra and RMS measurements among the two test groups are presented in Table 1. The obtained data had normal distribution. RMS and Ra values of the unused control file were 1.37 and 0.607, respectively. Mean RMS in EDTA group (3.70 \(\pm\) 1.41) (Figure 1) were significantly higher from the control group (Figure 2) (\(p\) \(<\) 0.05). Similarly, mean Ra in EDTA group (2.89 \(\pm\) 1.41) also demonstrated significantly higher values than the control group (\(p\) \(<\) 0.05). Mean RMS in _S. mukorossi_ group (1.77 \(\pm\) 0.66) did not show any significant difference with the control group (\(p\) \(>\) 0.05). Ra of _S. mukorossi_ group (1.09 \(\pm\) 0.05) (Figure 3) exhibited significantly higher values than the control group (\(p\) \(<\) 0.05) (Table 1). The comparison of Ra and RMS among the control, _S. mukorossi_, and 17% EDTA specimens showed significant differences (\(p\) \(<\) 0.001) (Table 2).  
Figure 1: Atomic force microscopy image of F1 file treated with 17% EDTA.  
Figure 3: Atomic force microscopy image of F1 file treated with _Sapindus mukorossi._  
Figure 2: Atomic force microscopy image of control F1 file.  
Mean RMS was found to be significantly higher in EDTA-treated files (3.76 \(\pm\) 1.41 nm) compared to _S. mukorossi_ files (1.77 \(\pm\) 0.66 nm) (_p_ \(<\) 0.05) (Figure 4).  
Mean Ra was found to be significantly higher in EDTA-treated files (2.89 \(\pm\) 1.40 nm) compared to _S. mukorossi_ files (1.09 \(\pm\) 0.05 nm) (_p_ \(<\) 0.05) (Figure 5).
## 2 Background
### Feature Extraction Techniques  
The extraction of features is the process of extracting the characteristics and information that best represent the data. Using the proper feature extraction technique could facilitate feature selection and dimensionality reduction, and improve the efficacy of the machine learning or deep learning model applied to the classification process [16; 17]. According to the classification model that is being utilized, there are three techniques that can be applied.
###### Abstract  
We conducted the first nutritional analysis of dairy products from the traditional Roja Mallorquina sheep breed. Samples of bulk raw milk were taken twice a month from December 2015 to March 2016 from sheep fed using a part-time grazing system, and fresh soft (FC, \(n=8\)) and ripened (RC, \(n=8\)) cheeses were made. The variability in vitamins, total phenolic compounds (TPC), total antioxidant capacity (TAC), and fatty acid (FA) content was influenced by the cheese-making process (differences between the cheese and the original milk) and by the type of cheese-making technology (mainly related to heating, the use of starter culture, and ripening). The most notable physicochemical characteristic of the cheeses was low fat content (24.1 and 29.6 g/100 g for FC and RC). Milk and RC were characterised by major concentrations of retinol (211.4 and 233.6 g/100 g dry matter (DM), respectively) and TPC (18.7 and 54.6 g/100 g DM, respectively), while FC was characterised by major concentrations of retinol (376.4 g) and \(\alpha\)-tocopherol (361.7 g). The fat-soluble components of the FC generally exhibited better nutritional value for human health than those of the milk and RC, with a higher level of retinol and \(\alpha\)-tocopherol; lower values for saturated FA, atherogenic, and thrombogenic indices; and higher levels of monounsaturated FA, polyunsaturated FA, n-3, and n-6. Acids, alcohols, and ketones comprised almost 95% of the volatile compounds detected. Acetoin and products of lactose and citrate metabolism played an important role in the development of the aromatic attributes of both kinds of cheese. This preliminary study can contribute to add value to these traditional products according to healthy nutritional criteria and supports the implementation of strategies to promote their commercialisation and obtain product labelling as \"pasture-fed\" or specific marks.  
antioxidant capacity; dairy product quality; n-3 and n-6 fatty acids; retinol; Roja Mallorquina sheep; topchroel; total phenolic compounds; volatile compounds +
Footnote †: journal: _foods_
## 2 Materials and Methods
### Materials Characterisation  
The X-ray diffraction (XRD) measurement was used to analyse the phase compositions. The analysis was made at room temperature by using XRD diffraction (Brucker D8 Discover X-ray diffractometer, Berlin, Germany), at 40 kV and 40 mA equipped with a Cu tube for generating CuK\(\alpha\) radiation, \(\lambda\) = 0.15406 nm), the incident beam in the 2-Theta mode over the range of 20-70\({}^{\circ}\), scan step of \(\Delta\Theta\) = 0.025\({}^{\circ}\), and \(\Delta\)t = 1.5 s.  
Surface topography, roughness, and fractal distribution of the films were investigated with atomic force microscopy (AFM), (NT-MDT model BL222 RNTE). The AFM images were taken in the semicontact mode with Si-tips (NSG10, force constant 0.15 N/m, tip radius 10 nm), and the results were processed with the dedicated software (NT-MDT, Nova Soft). The fractal analysis of the 2D AFM images of the film samples was performed using the SPIP software. Scanning electron microscopy (SEM) (Hitachi model S-3400N Type II) in BSE regime at low vacuum (50 Pa, 15 kV) was used to investigate the morphology of the sample. Energy-dispersive X-ray spectroscopy measurements (EDS) (Thermo, Ultra Dry, Noran System 7, NSS Model, NY, USA) were done for elemental analysis, based on energy values corresponding to each element in the samples.  
The point of zero charge of materials (pHp\({}_{\text{pzc}}\)) was performed by mass titration using 100 mL 0.1 M NaOH solution, and 0.2 g powder (representative for each sample) was added. In the second step, the solution was titrated with 0.1 M HNO\({}_{3}\) solution, and the pH values measured after 5 min of stirring. The pH of each suspension was then measured using a digital pH meter. Based on the curve obtained after plotting the pH versus mass fraction, the pHp\({}_{\text{pzc}}\) was validated. The method was previously developed and explain [49].  
Figure 1: Synthesis of ternary TiO\({}_{2}\)–Cu\({}_{x}\)S–fly ash (FA) heterostructures.
# Hybrid Positioning for Smart Spaces: Proposal and Evaluation
## 4 Hybrid Positioning Approach
### Managing Hardware Diversity  
Positioning systems using radio-map databases like fingerprinting have a notorious problem when different devices are used for both training and matching procedures, especially if RSSI-based matching algorithms are used. Under this situation, radio sensing varies with the hardware, so different RSSI levels are perceived by different devices. As not a single smartphone model is going to be used, it is necessary to scale measurements.  
In our case, the application used to sense the radio environment is firstly configured with a simple test near well-known base stations or tags, in order to compute a correcting factor for the signal strength perceived. This scaling solution is proposed in [25], based on the assumption that the order of magnitude among RSSI measurements does not change between different smartphones. In other words, two different terminals at the same place will sense the signals of the same access points, not necessarily with the same intensity, but maintaining relative RSSI differences among measurements. Due to this, RSSI perceived from each access point or tag is only used to rank them. The fingerprinting matching algorithm uses the similarity of the rankings to estimate the distance of each fingerprint to the current position. An advantage of this approach is that the whole radio-map is valid for all devices, and only a minor calibration of the mobile device is necessary the first time the positioning system is used.  
Figure 2: Technology collaboration strategy to improve positioning accuracy.
## 2 Methodology and Materials
### Data Collection and Performance Monitoring
#### 2.5.2 Environmental Impact Monitoring  
Greenhouse gas (GHG) emissions and fuel consumption data are recorded to quantify the project's sustainability benefits. The designed system can significantly reduce the reliance on fossil fuels, lowering carbon emissions (\(E_{Carbon}\)) in percentage and enhancing environmental conservation using Equations (8) and (9) [33; 34].  
\[min\ E_{Carbon}=\sum_{i=1}^{n}\frac{C_{base}(t)-C_{cur}(t)}{C_{base}(t)}\times 100 \%; \tag{8}\]  
\[s.t.\ C(t)=\sum_{i=1}^{n}F_{i}\times EF_{i} \tag{9}\]  
Here, \(n\) is number of years, \(C_{base}\) and \(C_{cur}\), \(F_{i}\) and \(EF_{i}\approx 2.69\) are carbon emissions in the baseline year and current year, fuel consumption and emission factor respectively.
## 2 Preliminaries
### Decision Tree  
A decision tree is a general tool in data mining and machine learning used as a classification or regression model in which a tree-like graph of decisions is formed. Among the well-known algorithms such as CHAID (chi-squared automatic interaction detection) [28], CART (classification and regression tree) [29], C4.5 [30], and QUEST (quick, unbiased, efficient, statistical tree) [31], we use CART which is very similar to C4.5 since it uses a binary splitting criterion applied recursively and leaving no empty leaf. Decision tree learning builds its model based on recursive partitioning of training data into pure or homogeneous sub-regions. Prediction process of classification or regression can be expressed by inference rules based on the tree structure of the built model, so it can be interpreted and understood easier than other methods. The tree building procedure begins at the root node, which includes all instances in the training data. To find the best possible variable to split the node into two child nodes, we check all possible splitting variables (called splitters), as well as all possible values of the variable used to split the node. It involves an \(O(pn\log n)\) time complexity where \(p\) is the number of input variables and \(n\) is the size of the training data set [32]. In choosing the best splitter, we can use some impurity metrics such as entropy or Gini impurity. For example, the Gini impurity function: \(im(T)=1-\sum_{y}p(T=y)^{2}\), where \(p(T=y)\) is the proportion of observations where class type \(T\) is \(y\). Next, we define the difference between the weighted impurity measure of the parent node and the two child nodes. Let us denote the impurity measure of the parent node by \(im(T)\); the impurity measures of the two child nodes by \(im\Big{(}T_{left}\right)\) and \(im\Big{(}T_{right}\Big{)}\); the number of parent node instances by \(X_{T}\); and the number of the child node instances by \(X_{T,left}\) and \(X_{T,right}\). We choose the best splitter by the query that decreases the impurity as much as possible:  
\[\Delta im(T)=im(T)-\frac{X_{T,left}}{X_{T}}im\Big{(}T_{left}\Big{)}-\frac{X_{ T,right}}{X_{T}}im\Big{(}T_{right}\Big{)}. \tag{4}\]  
There are two methods called pre-pruning and post-pruning to avoid over-fitting in decision tree. The pre-pruning method uses stopping conditions before over-fitting occurs. It attempts to stop separating each node if specified conditions are met. The latter method makes a tree over-fitting and determines an appropriate tree size by backward pruning of the over-fitted tree [33]. Generally, the post-pruning is known as more effective than the pre-pruning. Therefore, we use the post-pruning algorithm.