esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
It is concluded that the earth-abundant dolomite materials (handpicked dolomite, labeled as hp-dolo) as obtained from Central Florida's Phosphate Mines have demonstrated the energy storage behavior comparable to the commercial dolomite. The automated SEM-EDX Mineralogy and X-ray Fluorescence experiments were performed to obtain the quantitative measurements in terms of various mineral phases in dolomites during its various stages. The BET results the surface area of handpicked shows about 36 m\({}^{2}\)/g which is larger than the commercial samples. The XRD of handpicked samples showed two phases which included dolomite and impurity like quartz present. The lattice parameters, the unit cell volume and the average crystallite sizes of the handpicked dolomites are lower than the commercial materials because of its natural abundancy with no further purification, The FTIR spectra of all samples exhibit typical dolomite stretches as previously observed in the literature. The activation energy during calcination for both the commercial and handpicked dolomites, has been calculated using ASTM E1641-16 method. The carbonation activation energy for the commercial dolomite was lower in value compared to the calcination process. This may be due to the effect of positive CO\({}_{2}\) pressure during the carbonation. The cyclic looping studies demonstrated stable cycling after the 2 nd calcination-carbonation loop and continued for longer durations. Overall, the handpicked, Earth-abundant dolomites in its pristine, or ball milled powder form, are very comparable to the commercial dolomites, in terms of thermal energy storage, at high-temperature and -pressure conditions.  
Conceptualization, S.S. and J.D.; Data curation, S.L.W.; Formal analysis, M.B.J.C.; Funding acquisition, S.S.; Investigation, S.S.; Methodology, D.D.; Project administration, S.S. and G.A.; Resources, A.K.; Supervision, E.S.; Validation, G.R.C.; Writing--review and editing, S.S., S.L.W., N.H. and E.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Florida Industrial and Phosphate Research Institute (FIPR) and Florida Polytechnic University, grant number GR-18000010.  
Authors are gratefully acknowledging the funding support from Florida Industrial and Phosphate Research Institute and Hinkley Center for Solid Waste Management. The automated SEM-EDX measurements support provided by Colorado School Mines, ([PERSON]) is acknowledged. Florida Polytechnic University's Provost and Executive Vice President, [PERSON] is gratefully acknowledged for his institutional support to this project.  
The authors declare no conflict of interest.
# ResGDANet: An Efficient Residual Group Attention Neural Network for Medical Image Classification
## Appendix A  
```
Input: X: Input feature tensor of shape (B, C, H, W) B: Batch size C: Number of input/output channels H, W: Height and width of feature map r_c: Channel attention reduction ratio (default = 16) r: Coordinate attention reduction ratio (default = 32) bias: Convolution bias flag (default = False) imp: Minimum preserved channels (default = max(8,C/r)) Output: Y: Enhanced feature tensor
1 Channel attention branch: // Global average pooling and max pooling F_avg = (1/(H \(\times\) W)) \(\times\)\(\Sigma\)_[i = 1]'H \(\Sigma\)_[j = 1]'W X(i,j) // Global average pooling F_max = max_[i,j] X(i,j) // Global max pooling // Shared MLP with reduction ratio r_c W1 \(\in\) R^{\textbackslash}{(C/r_c)\(\times\)C}, W2 \(\in\) R^{\textbackslash}{[C/r_c)\(\}\) // MLP weight dimensions F_avg_mlp = W2 \(\times\) ReLU(W1 \(\times\) F_avg) // Apply MLP to F_avg F_max_mlp = W2 \(\times\) ReLU(W1 \(\times\) F_max) // Apply MLP to F_max M_channel = Sigmoid(F_avg_mlp + F_max_mlp) // Channel attention map F_chan = ChannelAtt(X) = M_channel // Channel attention output
2 Coordinate attention branch: // Height-wise and width-wise pooling  xh = (1/W) \(\times\)\(\Sigma\)_[i = 1]'W X(h,i) // Height-wise pooling: (B,C,H,1) xw = (1/H) \(\times\)\(\Sigma\)_[j = 1]'H X(j,w) // Width-wise pooling: (B,C,1,W) xw = Permute(xw, (0,1,3,2)) // Adjust to (B,C,W,1) // Concatenate and reduce channels y = conccat(sh, xw, dim = 2) // Shape: (B,C,H + W,1) y = Conv2D(y, out_channels = mip, kernel_size = 1, bias = bias) // Reduce channels to mip y = ReLU(BatchNorm2D(y)) // Feature transformation // Split and restore dimensions xh, xw = split(y, split_sizes = [H, W], dim = 2) // Split back to H,W xw = Permute(xw, (0,1,3,2)) // Restore dimensions // Height and width attention ah = Sigmoid(Conv2D(xh, out_channels = C, kernel_size = 1, bias = bias)) // Height attention aw = Sigmoid(Conv2D(xw, out_channels = C, kernel_size = 1, bias = bias)) // Width attention M_coord = X \(\times\) aw \(\times\) ah // Element-wise multiplication F_coord = CoordAtt(X) = M_coord // Coordinate attention output
3 Adaptive fusion: \(\alpha\) = Sigmoid(F_coord + F_chan) // Adaptive weight Y = \(\alpha\)\(\times\) F_coord + (1 \(-\)\(\alpha\)) \(\times\) F_chan // Final output
4 return Y // Return enhanced feature tensor
```  
**Algorithm 1**Dual-Channel Attention Fusion Module
# Basic Approaches for Reducing Power Consumption in Finite State Machine Circuits--A Review
## 4 Saving Power by Clock-Gating and FSM Decomposition
The asynchronous approach allows a significant saving in power consumption compared with the synchronous approach. As shown in [70], the power consumption is 1.36 times less for the hand-over mode, 4.13 times less for the enable mode, and 5.9 times less for the disable mode. Also, the difference in power consumption is greater (for different modes). In [70], experimental results are shown based on the use of the proposed approach for benchmarks bbara, dk512, ex1, keyb, styr, donfile, tma, and scf. They show that for the rather simple benchmark dk512, the value of \(K=3\) provides the best solution. At the same time, the best result for the not too complex benchmark ex1 is connected with \(K=4\). The  
Figure 14: Decomposition of an FSM based on [70]. most complex benchmark is scf (\(M=121\), \(L=27\), \(N=56\)). But the best solution for this benchmark is provided by splitting it into only two interrelated FSMs (\(K=2\)). The same occurs for the simplest FSM represented by the benchmark bbara, having the following characteristics: \(M=10\), \(L=4\), and \(N=2\). Thus, the optimal value of \(K\) does not depend on the number of states, \(M\), or inputs, \(L\), or outputs, \(N\). The results in [70] show that the optimal value of partial FSMs, \(K\), depends on the probabilities of interstate transitions. Also, the results in [70] indicate that saving power is connected with the overhead. The conclusion is the same as for other discussed methods: the more complex an original FSM is, the smaller the relative overhead area added. The same is true for the decomposed FSM performance: the more complex the original FSM is, the smaller the impact of additional circuitry on the performance is.
# Spatiotemporal Analysis of Web News Archives for Crime Prediction
## 3 Methodology
### Data Collection and Data Processing  
Data has crawled through the news archives of almost all popular and authentic newspapers mainly from Dawn News, Dunya News, Ary News, The News, Daily Pakistan, Pakistan Press Foundation, The Nation, and Journalism Pakistan, with the help of a Data Miner Tool.  
The data miner tool was selected for crawling because it gathers data from a specific website and represents it in tabular form. It classifies the news further in the title, description, date, URL, etc. The attributes of news records selected for this study were title, description, date, URL, location, latitude, longitude, type of crime, mishap, and the number of people affected. The archives of news data from the years 2011-2019 were mined for the development of the research model. In total, 920 records related to crimes from archives were extracted and were screened for duplication removal. Out of 920, it was found that twenty records were extracted multiple times and these were removed. The data consists of 900 records, and the number of records of each specific crime type is shown in Table 2. According to Table 2, the records of robbery are highest among all, i.e., 320 after that accident has a 230 number of records. Then blast has 150, Kidnapping has 90, Murder has 55, a shot has 20, suicide has 20, and arrest has 15 number of records. The data were further processed using various modules of Python such as Natural Language Processing Toolkit (NLTK), regular expression (RE), pandas, geo-py, etc. for data normalization.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Dataset** & **Techniques** & **Outcome** \\ \hline
[1] & Official site of the U.K Police & KNN and Naive Bayes & Prediction of crimes \\
[2] & Crime and land use data & Logistic regression & Exact hours of crime occurring \\
[4] & Local crime dataset & K-means & Clusters \\
[9] & Historical crash data of USA & Kernel density estimation and Kriging & Identify hotspot in roads network \\
[10] & Crime data on India & KNN and Naive Bayes & Prediction of crime type \\
[11] & Regional Crime Analysis Program (ReCAP) & Hotspot & Criminal event prediction \\
[12] & Surveys & KNN & Outlier detection \\
[19] & Real crime dataset recorded by police & Genetic Algorithm (GA) & Detection of crimes \\
[25] & Mentally ill offenders’ data & Random Forest & forensic-psychiatric risk-assessment \\
[26] & Denver crimes dataset & Decision Tree and Naive Bayes & Identify crime patterns \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summery of the state-of-the-art works.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Crime Type** & **No. of Records** & **Precision** & **Recall** \\ \hline Robbery & 320 & 74.32 & 69.88 \\ Accident & 230 & 67.48 & 55.29 \\ Blast & 150 & 52.09 & 45.68 \\ Kidnapping & 90 & 49.11 & 42.22 \\ Murder & 55 & 43.18 & 47.44 \\ Shot & 20 & 44.20 & 56.43 \\ Suicide & 20 & 43.33 & 55.21 \\ Arrest & 15 & 41.93 & 61.89 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of records of each crime category and their evaluation accuracy.
## 3 Materials and Methods
### Comparisons  
The proposed methodology will assess the effect of data augmentation techniques on SR. So, the speaker verification tasks were set up as one-vs.-all binary classifications, where the \"one\" is the class representative of the main speaker that needs to be verified, and \"all\" represents the rest of the subjects.  
Six speakers did not have a sufficiently large and/or balanced audio dataset and were thus used exclusively in the test set.  
For each comparison, one speaker was used as the main speaker, whose identity needs to be verified, whereas the rest were included in the \"all\" class, setting up 50 one-vs.-all comparisons (the test set includes 56 speakers); the results are then averaged by ID.  
The single speaker representing the \"one\" class will also be referred to as the main speaker or class 1, with class 2 being \"all\". The split into training and test sets was as follows:  
* We used 80% of the audio files for each emotion of the main speaker in the training set (class \"one\").
* The remaining 20%, balanced with respect to emotions, represented the test set for the main speaker.
* For each speaker representing the \"all\" class, one single audio file per emotion was picked and put in the training set. Different sentences were chosen for different speakers in order to avoid bias. This led to 8 files per speaker for a total of 440 files in the training set for the \"all\" class. The choice to use fewer samples per speaker was made to improve efficiency and generalization while retaining relatively more balanced datasets for the two classes and ensuring that there was a diverse dataset with a bias towards specific speakers or sentences.
* All the remaining utterings from the \"all\" speakers were used in the test set. The processing pipeline is as follows:
* The augmentation of test data with the generation of noisy and reverberated versions of the signals.
* The extraction of MFCC and RASTA features.
* Feature selection leading to smaller feature sets.
* The training of various classifiers based on non-augmented one-vs.-all binary comparisons. The chosen classifiers were SVM with a linear kernel, Random Forest (RF), and Adaboost with decision trees used as weak learners.  
* The validation of the classifiers, trained on non-noisy data, on all test data including augmented versions.
* Each speaker brings 3 one-vs.-all tasks, with 1 for each feature family. Validation data consist of 11 sets (5 noise, 5 reverb, 1 raw) which are evaluated by re-extracting the feature sets selected in the training and passing them to the classifiers. Figure 2 details the abovementioned methodology, outlining the division between raw and augmented versions as well as the extraction of the three feature families.
# Improvement of Power Converters Performance by an Efficient Use of Dead Time Compensation Technique
## 2 System Modeling  
The model of power quality improvement involves the incorporation of high-level pulse width modulation, in this case, IGBT, into the electrical switching system while retaining the switching frequency at 2-15 kHz levels [21]. To cope with the non-ideality in a small delay, called dead time, is inserted in the operation to avoid short circuits. Meanwhile, the dead time causes various parameters deterioration, therefore power quality analysis is carried out which mostly focused on the reduction of the negative consequences of dead time. The proposed method consists of dead time compensation (DTC) strategy for stabilization of 3 phase induction motors in the open-loop system using variable frequency drive (VFD) to govern the speed required for AC motors besides the offsetting of the adverse effects of DTC. A schematic of the proposed model is outlined in Figure 2. It consists of \"volt-per-Hz\" drive the design of which is delineated in the following. A specially minted control mechanism is applied for maintaining a fixed level of magnetizing current. Additionally, variable stator voltage support is also implemented.  
Since dead time is directly proportional to PWM signal output; the increase or decrease of one directly decreases or decreases the other resulting in a closer-to-original voltage pulse. Accordingly, the current study employs two methods for correcting dead time induced distortion: full correction methodology which factors in the phase angle magnitude and initiates a novel s/w for enhanced output; and partial correction methodology which helps the PWM on-chip hardware. At least one of these correction methodologies is pertinent to improve the PWM inverter parameters. The increasing parameters of PWM inverter values used in pair register; need to be kept in check with the help of software. The value is dependent on the transistor and is important for the output voltage in DT. The  
Figure 2: Model structure with dead-time compensation.  
partial correction method essentially builds the basis for polarity detection, which is helpful in the improvement of load current waveform, the magnitude of fundamental voltage, phase angle, third harmonic distortion, and induction motor parameters such as current waveform, torque pulsation, and speed. Albeit many shortcomings are concomitant with this method; the current settles at zero at the point of disturbance.
## 2 Materials and Methods
### Harvesting and Delignification Kraft Process  
The _Tradescantia fluminensis_ samples were harvested in October of 2023 in the Umia Riverbank area. Before treatment, samples were cleaned with distilled water, cut into pieces 50-20 mm long and dried in the oven at 105 \({}^{\circ}\)C in the Wood Chemistry laboratory of the Forestry Engineering School of the University of Vigo. Samples of tradescantia demonstrated a moisture of 98% [14; 16], reducing raw matter weight before delignification process.  
Following the drying of the raw material, the delignification of biomass samples was carried out using the kraft process. The objective of delignification processes is to dissociate lignin from cellulose fibers within lignocellulosic biomass by disrupting the ester and ether bonds present in the natural lignin structure [24].  
The Chemical Wood Laboratory is an old cellulose class space suitable for developing handsheets and the physical testing of paper sheets; however, this process has always been developed after commercial pulp has already been delignified. It is because of this reason that it has been not possible to acquire a proper industrial digester and Kraft options had to be used. Following this process, dry matter was added to 5 L of distilled water with the addition of 20% of dry mass weight of NaOH (caustic SOSA) [16]. This condition has been confronted in other studies [25], which suggest an optimization of the process by adding 22& of alkali charge, 30% of sulfidity, and a liquor-to-wood ratio of 4:1 with a cooking temperature of 165 \({}^{\circ}\)C for 90 min. As developed in previous studies [16; 26], the liquid-to-solid ratio was 5:1 and there was a maximum cooking time of 90 min and a temperature of 160 \({}^{\circ}\)C. In this instance, the raw matter was from an herbaceous plant, so its material was easily disposed of lignin and the leaves and stem could be separated from each other without substantial problems (Figure 3).  
The morphology of the paper made with 100% of Tradescantia biomass was analyzed and, as is possible to observe in Figure 4, it is structured in both fibers and links. The average thickness of fibers is 9.311 \(\upmu\)m and their lengths vary depending on which part of the plant is predominant in the blend, with stems being associated with longer fibers and leaves with a higher presence of links.  
Figure 3: Dry delignified matter before the refining process (**left**) and refined matter after 30 s in PFI (**right**) from _Tradescantia fluminensis_.
# Biochar from Agro-Forest Residue: Application Perspective Based on Decision Support Analysis
## 1 Introduction
The main goal of the present study was (a) to carbonize biomass residue; (b) to characterize the obtained biochar; and (c) to explore its potential application as biofuel, catalyst, supercapacitors, or as CO\({}_{2}\) sequestration and soil amendment--since those are known to be probable efficient applications [47; 48; 50]. To this end, six different types of biomass residue (agro-forest) were studied, originating from two European countries with well-established agricultural traditions. The biomass residue was investigated prior to the present research, applying the following methods: proximate, ultimate, ash, lignocellulosic, and HHV analyses. The residue was carbonized in an inert atmosphere (nitrogen) using a Horizontal Tube Furnace (HTF), in accordance with [PERSON] et al. [54]. The derived biochar was examined through a set of physical and chemical analyses. In focus were the following global parameters: (i) the effect of feedstock on biochar yield; (ii) biochar thermal decomposition in air, its stability, and kinetics; (iii) biochar characterization using proximate, ultimate, EDS, and HHV analysis; morphology and specific surface area; technical parameters, such as bulk density, pH, EC, and salt content; (iv) application prospective, based on a multi-criteria decision approach. A previous study by the authors applied multi-criteria decision analysis to evaluate the best application of biochar produced from grape pomace biomass, considering the measured biomass characteristics and conversion process properties [54]. The decision focused on the carbonization conditions and the potential use of the products for biofuels, biofer-tilizer, or catalysts for biofuel production. In the present investigation, the authors used similar methods but assessed a larger number of process variables, thus increasing the accuracy of the methods. Moreover, a greater variety of biomass sources was examined herein. The chosen decision methods--Pareto front evaluation and compromise programming--are mathematical techniques typically used in multi-criteria, multi-objective decision analysis, as well as in the optimization field.
## 1 Introduction and Motivation  
Considerable studies have discussed the issues such as the capacity [1] and multi-hop routing [2-4] in wireless _ad hoc_ and sensor networks, among which connectivity is a fundamental property to be preserved and also provides design reference for upper-layer protocols. For example, we have to decide a minimum node density to ensure global connectivity and provide route diversity in routing decisions; when we do node sleep scheduling to save energy, the connectivity of active nodes has to be maintained and satisfy the application's QoS requirement.  
Pioneering works [5-8] dealing with network connectivity are mostly based on a simplistic Boolean model with the assumption that two nodes are connected if and only if their distance is less than a deterministic transmission radius. Unfortunately, the real low-power wireless links are unreliable and asymmetric, suffering from severe propagation impairments such as path loss, multi-path fading and shadowing, which have great impact on the global QoC. A few papers have dealt with connectivity of _ad hoc_ networks in the presence of channel randomness, fading and shadowing [9-11]. Important to our work is the contribution in [9], studying the node isolation probability, from which, under the assumption of dense networks, an approximation of one-connectivity probability can be obtained. In our paper, we adopt an analytical link model which best matches [PERSON]'s [12] real link state. Through an in-depth derivation, local property of individual links is connected to the global network connectivity. We have shown through both analysis and simulation how various parameters impact the QoC in wireless sensor networks.  
The rest of the paper is organized as follows. Section 2 provides a short overview of both wireless link model and network connectivity issue. The detailed analysis of node isolation probability under an analytical link model is presented in Section 3. In Section 4 we do extensive simulations to evaluate the analysis in Section 3 and also discuss the impact of various parameters on the global QoC. Finally we conclude the paper in Section 5 and point out the future direction.