esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of Preprocessing on Arabic Document Categorization
## 5 Experiment and Results
### Experimental Configuration and Performance Measure  
In this paper, the documents in each category were first preprocessed by converting them to UTF8 encoding. For stop word removal, we used a file containing 896 stop words. This list includes distinct stop words and their possible variations.  
Feature selection was performed using chi-square statistics. The number of features for building the vectors representing the documents included 50, 100, 500, 1000, and 2000. In doing so, the effect of preprocessing task can be comparatively observed within a wide range of feature size. Feature vectors were built using the function _TFiDF_ as described by Equations (2) and (3).  
In this study, SVM, NB, and KNN techniques were applied to observe the preprocessing effect on improving classification accuracy. Cross-validation was used for all classification experiments, which partitioned the complete collection of documents into 10 mutually exclusive subsets called folds. Each fold contains 3262 of documents. One of the subsets is used as the test set, whereas the rest of the subsets are used as training sets.  
We have developed our application using JAVA Programming for implement preprocessing tasks on the dataset. We used RapidMiner 6.0 software (HQ in Boston MA USA) to build the classification model that will be evaluated by means of the testing data. RapidMiner is an open-source software which provides an implementation for all classification algorithms used in our experiments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category Name** & **Number of Documents** \\ \hline News & 6860 \\ Economy & 4780 \\ Health & 2590 \\ History & 3230 \\ Sport & 3950 \\ Religion & 3470 \\ Social & 3600 \\ Nutrient & 2370 \\ Law & 1240 \\ Technology & 530 \\ \hline
**Total** & **32,620** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics of the documents in the corpus.  
Figure 1: Distribution of documents in each category.  
The evaluation of the performance for classification model to classify documents into the correct category is conducted by using several mathematic rules such as recall, precision, and F-measure, which are defined as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{6}\]  
\[Recall=\frac{TP}{TP+FN} \tag{7}\]  
where _TP_ is the number of documents that are correctly assigned to the category, _TN_ is the number of documents that are correctly assigned to the negative category, _FP_ is the number of documents a system incorrectly assigned to the category, and _FN_ are the number of documents that belonged to the category but are not assigned to the category. The success measure, namely, micro-F1 score, a well-known F1 measure, is selected for this study, which is calculated as follows:  
\[\text{Micro-F1}=\frac{2*Precision\times Recall}{Precision+Recall} \tag{8}\]
## 4 Results
### Expected Risk to Life (ERL) and HOE Quantification  
The ASET-RSET analysis together with the risk assessment yields an overall ERL for technical risks for each building solution. A description of the ERL acceptance criteria, tenability, and ASET/RSET can be found in [19]. The results from T-H-O-Risk modelling for the trial designs for each case study are summarized in Table 10. These results are expressed in terms of ERL without HOE (noHOE) in the first column and ERL that includes HOE in the second column for each case.  
Figure 3 shows the ERL results for all 16 trial designs for Case #1. It is interesting to note that the influence of HOEs increases with the complexity of the system; a design with the full set of safety provisions (Trial Design 1) shows a +33% increase in ERL when considering HOEs, while the simpler design with only the sprinkler system (Trial Design 8) has a +20% increase in the ERL. However, even with HOE, Trial Design 1 has lower ERL without HOE in any other design.  
The HOE/ERL ratio (in %) as a function of number of active systems is depicted in Figure 3. The HOE/ERL ratio increases with an increase in the number of active systems. With one active system, the HOE values range from 8-13% and increased to 25-38% when all four active systems are present in the trial design.  
In Figure 4, a linear relationship exists between the variable HOE/ERL% and the number of active systems. A correlation analysis indicates that HOE/ERL% and the number of active systems are strongly correlated in positive direction with respect to all cases #1 to #7-HOE/ERL% increases when active systems increase from 1 to 4. It can be concluded that a strong correlation exists between those variables for each case and for all data when considered together. The magnitude of correlation for each case is summarized in Table 11. While Case #1 and Case #7 have the highest correlation, Case #5 has the lowest correlation.  
Figure 3: ERL and HOE impact on trial designs for Case #1 [log y-axis].
## 1 Introduction  
The use of dental implants for the rehabilitation of missing teeth is increasingly popular because it resembles natural teeth both in terms of function and aesthetics [1; 2; 3; 4; 5; 6]. The success of dental implants is indicated by its survival rate as a consequence of the osseointegration, which is based on various factors originating from the patient, clinician, implant placement area, and characteristics of the implant [1; 4]. The success of a dental implant after placement can also be determined by the stability of the implant, minimal signs and symptoms, new bone formation, and healthy tissue around the implant [7].  
Dental implants used to rehabilitate missing teeth are derived from titanium (Ti), which has advantageous properties such as resistance to corrosion, high strength, and excellent biocompatibility [5; 8; 9; 10; 11]. However, the properties of Ti have not met the criteria for clinical use. Therefore, various surface modifications were performed on Ti, such as plasma spray coating, ion implantation, laser, sputtering, acid etching, sandblasting, sandblasting and acid etching, and dip coating, to improve the quality of its biological, chemical, and mechanical properties [12; 13; 14]. Surface modification aims to provide an implant surface that can be accepted by the surrounding tissue (a good bone-implant interaction) to create osseointegration leading to the successful placement of dental implants [6; 10; 15; 16; 17; 18; 19].  
Sandblasted, large-grit, and acid-etched (SLA) technique is a surface modification that has shown successful long-term clinical outcomes [11; 20]. However, dental implant failures still persist with failure rates varying from 1% to 19% due to inadequate osseointegration and the occurrence of implantitis after implant placement [21; 22]. Thus, various efforts were performed to minimize the risk of implant failure, such as performing the appropriate surgical technique, providing enough bone quality and quantity, minimizing the inflammation or infection after surgery, modifying the surface modification, and improving of patient nutrition by providing several dietary supplements that affect bone metabolism, all of which lead to osseointegration [19; 23]. However, the current study is focused on modifying the surface modification of Ti dental implants. In this study, the dental implants that have received SLA surface modification are followed by coating with pluronic F127 (PF127). Pluronic is an active polymer that has good biocompatibility, low cost, easy preparation, biodegradability, and is nontoxic, making it widely used in biomedical applications [24; 25; 26]. Therefore, this study aimed to evaluate the impact of the surface properties of the surface-modified Ti dental implants on osseointegration through surface morphology, contact angle analysis, hemocompatibility evaluation, and clinical evaluations. It is believed that the surface-modified Ti dental implant is able to create rapid osseointegration with good biocompatibility, and will lead to the clinical success of dental implants in terms of rehabilitation of missing teeth.
## 4 Simulation Results and Discussion
In addition, the red line is the average sum rate. The maximum sum rate line reflects the convergence of the ACO algorithm. The maximum sum rate converges to 4.017 bit/s/Hz when the number of iterations reaches 38. This is because the pheromone in the maximum sum rate route is updated after each iteration in the ACO algorithm. Then the ants have higher probability to find the optimal solution. The average sum rate line reflects the randomness of ACO algorithm for subcarrier allocation, which means the ants maintain the random search capability and avoid falling into the local optimal solution. _B. The Sum Rate of the System with 2 up to 16 Users_  
Figure 5 describes the sum rate _versus_ the number of users. The AWGN noise power density is equal to \(-80\) dB\(\cdot\)W/Hz, BER = \(10^{-6}\). According to Figure 5, as the number of system users increases, the relatively higher sum rate for all methods is achieved. This is mainly due to the effect of multiuser diversity: if there are more users in the system, the probability that a subchannel fades deep for all users will be lower. The proposed method ACO-SPA yields the higher sum rate than the root-finding and linear methods for all the number of users from 2 up to 16. This is mainly attributed to two factors. One is that the ACO algorithm for subcarrier allocation uses several iterations to search for the optimal solution. In addition, the second is that in power allocation, the algorithm has the controls that the user with higher effective subchannel SNR \(H_{k,n}\) in subchannel \(n\) is provided with more power \(p_{k,n}\). Figure 4: The sum rate _versus_ the number of iterations. _C. The Sum Rate of the System with BER Requirements from 10\({}^{-4}\) down to 10\({}^{-9}\)_  
Figure 6 shows the sum rate _versus_ system BER requirements. The AWGN noise power density is set to \(-80\) dB\(\cdot\)W/Hz, the number of users is 16. In Figure 6, the sum rate of OFDMA system for all methods declines as the BER declines. This is reasonable, for as the BER declines, the SNR gap \(\Gamma\) turn to be higher, and the system sum rate, in accordance with the optimization objective Equation (4), declines. It can be seen from the Figure 6 that by comparing the sum rate, the proposed method ACO-SPA performs better than other two methods.
# Process Parameter Optimisation in Laser Powder Bed Fusion of Duplex Stainless Steel 2205
## 1 Introduction
This technique analyzes the importance and impact of each variable on the results with a mathematical model, and then calculates an optimal parameter configuration, as has been achieved in studies by [PERSON] and others [13; 14; 15]. This model, apart from being characterized by its graphic and visual simplicity, is also ideal for analyzing experimental results, as it the case of this study. Through a mathematical model, this methodology analyzes the impact of various independent parameters on the variable's response, allowing the subsequent process of optimization. In this case, optimization refers to maximizing the mechanical properties of a duplex stainless steel. The material whose powder bed fusion parameters have been optimized is DSS-2205. It is composed of ferrite and austenite, with the proportion of either phase not less than 30%, ideally 50/50 [16; 17]. Its uniform duplex microstructure means that it has good mechanical properties and high corrosion resistance [18; 19; 20]. It is typically used in marine and other corrosive environments [21; 22]. Thanks to the research of [PERSON] et al. [23; 24], it is known that in duplex steel, dislocations tend to accumulate at the boundaries of the ferrite matrix and displacements occur between the boundaries of the austenite and ferrite grains. This behavior gives duplex steels their characteristic properties [25; 26]. Therefore, it is essential that the SLM-manufactured steel has the right phases and content proportion. However, analyses carried out on the SLM steel show that it has precipitated an almost fully ferritic phase content. This phenomenon can be explained as DSS-2205 has a high content of elements and due to the extremely fast cooling rate of SLM process, a complex precipitation phenomenon is inevitable in the forming process [27; 28]. In fact, some studies have taken advantage of this precipitation to develop ferritic-induced high-alloyed stainless steels from powder bed fusion DSS-2205, such as [29]. However, it changes many properties of the material, as it is no longer a duplex. Due to this fact, it is compulsory to subject the as-built steel to an austenitization process. It will be defined as the optimal heat treatment that results in a percentage of phases of DSS-2205 closer to the values that correspond to this type of steel, based on the research of [PERSON] and others [17; 30; 31; 32; 33]. In the end, the goal of maximizing DSS-2205 properties will be achieved through optimizing SLM parameters. This steel will acquire similar mechanical properties to the base material, obtained through hot rolling. These properties are the yield strength (\(\sigma_{0.2}\)), the ultimate tensile strength (\(\sigma_{\text{uts}}\)), the elongation at fracture (\(\varepsilon_{\text{r}}\)), the reduction in the specimen's cross-section area at fracture (A), and additionally, the density (D).
# Machine Learning for Bankruptcy Prediction in the American Stock Market: Dataset and Benchmarks
## 6 Task TI: Default Prediction with Historical Time Series  
The first task we performed using the dataset is training each ML model presented in Section 4 for default prediction with historical time-series accounting data. Our first step was to perform the most classical task of predicting a company's health status for the next year based solely on data from the previous year. Furthermore, we attempted to answer the open question in the literature regarding the number of years that should be considered in order to maximize the performance of the bankruptcy prediction model. To achieve this, we define a Window Length (WL) variable that refers to the number of fiscal years considered as a temporal window for the prediction. We trained all the models using data between 1999 and 2011, and we made the first comparison using the Validation set (2012-2014) in terms of AUC.  
Finally, we report the results of the test set using the best models identified on the validation set. This last step is necessary to verify the ability of the models to generalize. The average number of years available for each company in the dataset is 8 years. However, we evaluated a WL ranging from one to five years for two main reasons:  
* Five years is the general maximum number of years found in the literature to be useful.
* When increasing the WL considered, the most recent companies are excluded because they do not have available data. In general, considering more years leads to smaller training and test sets. This could introduce a statistical bias, causing the analysis to focus on only the more mature and stable companies that have existed for several years while ignoring the relatively newer, smaller companies that are riskier and have higher default rates. This could introduce a statistical bias forcing the analysis to consider only the more structured and stable companies that have existed for several years while ignoring the relatively new companies with smaller market capitalization and which are thus riskier and have a higher probability of default, particularly during periods of economic decline.  
Figure 4 shows the distribution of the two classes of bankruptcy and healthy samples for the training, test, and validation by selecting different Window Lengths (WLs).
# HCLE: Hybrid Clustering Approach for Extending WBAN Lifetime
## 4 Materials and Methods
### HCEL Routing Protocol  
In this subsection, we introduce three different phases of our proposed routing protocol (HCEL), namely, network deployment and initialization, parent node selection, and data transmission.
# tRNA and tsRNA: From Heterogeneity to Multifaceted Regulators
## 2 The Heterogeneity of the Mature tRNA Pool  
Traditional tRNA research has primarily focused on the physiological and biochemical functions of specific types of tRNA molecules or tRNA-related enzymes (such as modification enzymes and aminoacyl-tRNA synthetases). However, advances in high-throughput omics technologies have revealed a comprehensive profile of tRNA, encompassing the total abundance of tRNA pools, the composition of specific tRNAs within these pools, the charging levels of tRNAs, and their chemical modifications (see Section 3). These features of cellular tRNA pools have been found to exhibit unique patterns across different tissues and cell types (Figure 1). Such unique patterns of tRNA pools, namely, tRNA heterogeneity, operate globally to control downstream protein translation and potentially play roles in tissue-specific differentiation, stem cell fate determination, and the development and progression of diseases [31; 32].
## 4 Results and Discussion
### FCS-MPC: Variable-Switching-Frequency Operation with Switching-Frequency Minimization
(**a**) Linear load: load current with weighting factors (\(\lambda_{min}\)) ranging from 0 to 0.5; (**b**) linear load: load current with weighting factors (\(\lambda_{min}\)) ranging from 0.5 to 1.0; (**c**) non-linear load: load current with weighting factors (\(\lambda_{min}\)) ranging from 0 to 0.5; (**d**) non-linear load: load current with steps in the frequency reference with weighting factors (\(\lambda_{min}\)) ranging from 0.5 to 1.0. The latter is more accentuated in Figure 10d, as the weighting factor (\(\lambda_{min}\)) goes ranges from values of 0.5 to 1.0, and the excess of distortion is evident in the capacitor voltage waveforms. The transition from 0.5 to 1.0 in the weighting factor (\(\lambda_{min}\)), as illustrated in Figure 10d, appears to be excessive for the control algorithm that governs the voltages of the capacitors. Furthermore, the non-linear characteristics of the load, for this case, exacerbate instabilities within the controller, thereby increasing distortion in the resultant waveforms. The case of current in the load operates similarly to the case of capacitor voltages. Increasing the values of the weighting factor (\(\lambda_{min}\)) adds more distortion to the waveform (in Figure 11a-d, only one of the phases is shown, i.e., \(i_{a}\)). Figure 11a,b illustrate the current (\(i_{aload}\)) for values of \(\lambda_{min}\) ranging from 0 to 0.5 and 0.5 to 1.0, respectively, and using a linear \(RL\) load. The quality of the current progressively deteriorates as the level of distortion intensifies with an increasing weighting factor (\(\lambda_{min}\)). Analogously to the scenario involving a linear load, the waveform observed for the current in a non-linear load distinctly reflects its inherent characteristics. Furthermore, the influence of the switching minimization factor (\(F_{min}\)) becomes more pronounced within the cost function (\(g_{min}\)) of Equation (15), as demonstrated by an increase in the weighting factor (\(\lambda_{min}\)), which transitions from 0 to 0.5 in Figure 11c and subsequently from 0.5 to 1.0 in Figure 11d.
## 3 Vibration Localization Characteristics and Their Influence Factors
### Mode Localization  
#### 3.1.1 Mode Shapes Under Different Mistuning Strengths  
Let the number of resonant units be \(N\)= 10, with each resonant unit having a mass \(m_{n}\) = 17 g. The coupling relation between the units is denoted as \(R\)= 0.1. The mode shapes are depicted in Figure 2. It is evident that the cyclic tuned structure (CTS) does not display a mode localization phenomenon. Instead, the modes across all orders exhibit a relatively regular and uniform distribution throughout the entire structure.  
To analyze the influence of the mistuning strength on the mode shapes, the masses of the resonant units are assigned based on the parameters outlined in Table 1. In all four cases, the average mass of the resonant units remains constant at 17 g. The mass intervals between the resonant units are 0.2 g, 0.4 g, 0.6 g, and 0.8 g, respectively. The corresponding calculated mistuning strengths \(\varepsilon\) are 3.6%, 7.2%, 10.9%, and 14.8%. A weak coupling condition is maintained between the resonant units, with the coupling coefficient \(R\) = 0.1.  
The mode shapes of the cyclic structure under different mistuning strengths are shown in Figure 3. It is clear that the weakly coupled CMS suffers from mode localization. For each order of the vibration mode, there are resonant units with prominent amplitudes, which have a perturbing effect on the adjacent units. When the mistuning strength is relatively small, the mode localization of the CMS is weak, and the mode shapes of each order are characterized by multiple resonant units undergoing intense vibrations simultaneously. As the mistuning strength increases, the mode localization becomes more pronounced, and the mode shapes of each order are manifested by the violent vibration of a single resonant unit.
## 4 Results and Discussion
### Vs30 and Soil Classification  
The present study addresses verification of average seismic shear wave velocity from the surface to a depth of 30 m (Vs30) as a suitable proxy for a seismic amplification e.g., [67]. The Vs30 map (Figure 11) was calculated by applying Equation (1) at the surface layers derived from the shear wave velocity profiles estimated from the inversion of the HVSR and dispersion curves. The Vs30 values range from 240 to 680 m/s and are divided into two main classes (Table 3). Class C (very dense soil and soft rock), with Vs30 values between 360 and 680 m/s, is the dominant classification in the region, in accordance with NEHRP standards [52]. Class D (Stiff soil), with Vs30 values between 240 and 360 m/s, is also observed, particularly in the industrial zone where the Pliocene layer (mainly composed of sand) is present. It is also observed in the hospital zone and to the north of the hospital zone (AR03). The same soil classes were obtained in the city of Oran [45] and in the cities of El-Abadia, El-Attaf, and Ain-Defla in the Middle Chelif Basin [33].
###### Abstract  
Background: The use of bromelain for the removal of eschar in deep burns is considered to be effective because it does not affect the unaffected skin and leaves a clean dermis after use. The main objective of this study is to find out whether bromelain is a good alternative to surgical debridement. In order to achieve that, we aim to evaluate its indications, limitations, and safety measures. Methods: The current study was conducted on a group of 30 patients with deep burn lesions, aged 20 to 56 years, from which 15 underwent enzymatic debridement and 15 patients acted as a control group in which primary surgical debridement was used. The mixture of enzymes enriched in bromelain, meant to dissolve burn eschar, was provided by NexoBrid(tm). The inclusion criteria were in agreement with the manufacturer's protocols, but the application protocol was slightly modified in order to implement a better intern protocol and to assess its efficiency. Results: Complete eschar debridement was obtained in 13 of the 15 cases, from which 10 patients went through spontaneous healing and 3 needed to be covered with a skin graft. In the other 2 cases, partial eschar debridement was associated with surgical debridement and coverage with split-thickness skin graft in the same operation. The results obtained in the two groups were assessed with the Vancouver Scar Scale. Conclusions: Even though early excision followed by coverage with split-thickness skin graft remains the gold standard for the treatment of deep burns, enzymatic debridement can provide a series of advantages when the inclusion and exclusion criteria are respected. Bromelain is an alternative to surgical debridement that provides speed, tissue selectivity, safety, and less blood loss.  
bromelain; deep burns; split-thickness skin graft; enzymatic debridement; pineapple extract