esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Algorithms
### Practical Considerations about Proposed Algorithms  
In this paper, to reduce the error of \(\lambda(n)\) being \(1\) or \(0\), as shown in Figure 1 when \(C\leq 5\), we can appropriately extend the effective range of \(s(n)\) to \([-0.4,0.4]\). In addition, when \(s(n)=0.4\), the difference between \(\exp(-s(n))\) and \(1-s(n)\) is \(0.0703\). Ignoring such differences and applying the first-order Taylor series expansion to \(\exp(-s(n))\), we can use \(1-s(n)\) to approximate \(\exp(-s(n))\). Hence, (22) can be simplified as  
\[\lambda_{s}(n)=\frac{C}{2-s(n)}-\left(\frac{C}{2}-\frac{1}{2}\right). \tag{26}\]  
Based on (26), we get  
\[\begin{cases}s(n)=\frac{-2}{C-1}\rightarrow\lambda(n)=0\\ s(n)=\frac{2}{C+1}\rightarrow\lambda(n)=1.\end{cases} \tag{27}\]  
Therefore, injecting (27) into (26) yields  
\[\lambda_{s}(n)=\begin{cases}0,&s(n)<\frac{-2}{C-1}\\ 1,&s(n)>\frac{2}{C+1}\\ \frac{C}{2-s(n)}-(\frac{C}{2}-\frac{1}{2}),&elsewhere\end{cases} \tag{28}\]  
with \(C>1\). Also, applying (28) to CSS-APSA, we can get the simplified CSS-APSA (denoted as: SCSS-APSA).  
Comparing (22) with (13), one can find that (22) is a kind of scaling transformation of (13). Such a transformation can speed up the change rate of \(\lambda(n)\). Based on this idea, (22) can be modified as follows:  
\[\lambda_{\beta}(n)=\frac{C}{1+\exp(-\beta s(n))}-\left(\frac{C}{2}-\frac{1}{2} \right), \tag{29}\]  
Figure 1: The curves of \(\lambda(n)\) with different \(C\) values.  
where \(\beta\) is a positive constant, and \(s(n)\) is restricted as  
\[s(n)=\begin{cases}-ln(\frac{C+1}{C-1})/\beta,&s(n)<-ln(\frac{C+1}{C-1})/\beta\\ ln(\frac{C+1}{C-1})/\beta,&s(n)>ln(\frac{C+1}{C-1})/\beta\\ s(n)&elsewhere.\end{cases} \tag{30}\]  
Following the modification of (29), different \(\beta\) values can accelerate or slow down the change rate of \(\lambda_{\beta}(n)\). The change rate may result in a faster convergence rate of the proposed algorithm. Therefore, CSS-APSA armed with (29) and (30) is called a modified CSS-APSA (denoted as MCSS-APSA).  
Obviously, CSS-APSA is a special case of MCSS-APSA when the \(\beta=1\). In addition to (29), when \(C=1,s(n)\) is restricted as  
\[s(n)=\begin{cases}-4/\beta,&s(n)<-4/\beta\\ 4/\beta,&s(n)>4/\beta\\ s(n),&elsewhere.\end{cases} \tag{31}\]  
Based on (16) and (23), the range of \(\beta s(n)\) for MCSS-APSA is the same as that of \(s(n)\) for CSS-APSA. Therefore, according to the operations for (26), when \(C>1\) and \(\beta>0\), (29) can also be simplified as  
\[\lambda_{s\beta}(n)=\frac{C}{2-\beta s(n)}-\left(\frac{C}{2}-\frac{1}{2}\right), \tag{32}\]  
and \(s(n)\) is restricted as  
\[s(n)=\begin{cases}\frac{-2}{(C-1)\beta},&s(n)<\frac{-2}{(C-1)\beta}\\ \frac{2}{(C+1)\beta},&s(n)>\frac{2}{(C+1)\beta}\\ s(n),&elsewhere.\end{cases} \tag{33}\]  
Therefore, injecting (32) and (33) into CSS-APSA, we can obtain the simplified MCSS-APSA (denoted as SMCSS-APSA). Algorithm 1 summarizes the proceeding of the proposed algorithms.
###### Abstract  
A soft decoding technique is discussed in this paper to improve the performance of long-distance optical networks (LDOTNs). LDOTNs are affected by phase noise and nonlinearities generated inside the fiber. The investigations of the proposed LDOTN were carried out by dual-polarization 16-quadrature amplitude modulation (DP-16 QAM), DP-64 QAM over single-mode fiber (SMF) and digital signal processing (DSP) methodologies. The improved performance of the presented mechanism is discussed over SMF based on constellation shaping (CS). The CS of the presented LDOTN is then compared to the standard 16-QAM and 64-QAM using international telecommunication union-telecommunication (ITU-T) standard G-652.D and G-657.A1 SMF. The soft detecting procedure enables the LDOTNs to attain significant outcomes.  
Keywords:dual-polarization 16- and 64-quadrature amplitude modulation; constellation shaping; nonlinear effects; long-distance optical transmission networks; soft decoding mechanism; nonlinear phase shift +
Footnote †: journal: 2022
## Dynamic Key Sharing and Fuzzy Adaptive Route Selection
### Goals
#### 3.2.1 Load Balancing  
Fuzzy adaptive selection of data forwarding path potentially balances the load between data forwarding paths. Load balancing spreads energy utilization across nodes in the network, potentially resulting in longer network lifetime [35].
## 3 Material and Assessment Method
### Assessment Method
#### 3.2.2 Objective Assessment  
In order to more objectively evaluate our proposed detection method, we use sensitivity, specificity, accuracy, F1-score, and the receiver operating characteristic (ROC) curve as the standard of objective evaluations. The definition is as follows:  
\[Sensitivity=\frac{TP}{TP+FN} \tag{21}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{22}\]  
Figure 9: Image with AD in Digital Database for Screening Mammography (DDSM): (**a,b**) are original images; (**a1**) and (**b1**) are the AD image marked by the expert.  
\[Accuracy=\frac{TP+TN}{TP+FN+TN+FP} \tag{23}\]  
\[F1-score=\frac{2\times TP}{2\times TP+FP+FN} \tag{24}\]  
Sensitivity indicates the ability of the expertly labeled AD area to be detected as AD area by method proposed. Specificity indicates the ability of the expert to label the background area of the mammography image as a background area by method proposed. Accuracy indicates the precision which our proposed method detects AD. F1-score indicates the similarity between the AD area we detected and the actual lesion area. Here, TP, FN, TN, and FP represent True Positive, False Negative, True Negative, and False Positive respectively. Their practical significance is shown in Table 4.  
Their values are the number of pixels in the true positive, false negative, true negative and false positive areas obtained by comparing the detected AD with the area marked by the expert. The above four assessment indicators have a numerical range between 0 and 1, and the closer to 1, the better result obtains.  
In addition, the ROC curve is used to evaluate the detection results. The True Positive Rate (TPR) is the ordinate and the False Positive Rate (FPR) is the abscissa. The definitions of TPR and FPR are as follows:  
\[TPR=\frac{TP}{TP+FN} \tag{25}\]  
\[FPR=\frac{FP}{FP+TN} \tag{26}\]  
The assessment index of the area under curve (AUC) is the area under the ROC curve, the AUC have a numerical range between 0 and 1, and the closer to 1, the better result obtains.
## 2 Materials and Methods
### Objective Functions  
Objective functions used by MOOGA algorithm represent relationships between \(T_{d}\), \(v_{d}\) (process variables; drying air temperature: 40, 50, and 60 \({}^{\circ}\)C, air flow velocity: 0.5, 0.75, and 1.0 m/s) and energy consumption (EC) for the drying process and quality characteristics of the dried material obtained (CD, \(\text{A}_{\text{loss}}\), \(\text{TAC}_{\text{loss}}\), \(\text{TFC}_{\text{loss}}\), \(\text{TPC}_{\text{loss}}\)). Details about the used data are presented in Table 1.  
The relations were approximated using ANN which topology is shown in Figure 1 (three-layer NN). The ANN task was to map input variables: \(T_{d}\) and \(v_{d}\) on to six outputs (CD, \(\text{A}_{\text{loss}}\), \(\text{TAC}_{\text{loss}}\), \(\text{TFC}_{\text{loss}}\), \(\text{TPC}_{\text{loss}}\)) to obtain high correlation coefficient (R) and the lowest Mean Squared Error (MSE). The inputs and outputs of ANN were normalized (divide by maximum values, respectively) to obtain the range 0-1.  
The actual values of the input variables were chosen randomly from a fixed set of data in each case. For this set of data, three levels of drying air temperature and three levels of air flow velocity were used (see Table 2). Seventy-three repetitions were performed for each level (3-\(T_{d}\):3-\(v_{d}\)). A total of 657 different results was obtained.  
Chosen cases (657 cases from the experiments) were randomly divided into the following sets: 98 samples (15%), 461 samples (70%) for testing, and 98 samples (15%) for validation sets. The network used the default Lavenberg-Marquardt (L-M) algorithm for the training procedure. L-M locates the local minimum of a multivariate function, expressed as the sums of squares of several of non-linear, real-valued functions as demonstrated in paper [45]. In this study, the maximum number of epochs to train, the initial momentum and \(mu\) increase factor term were: 100, 0.4 and 10, respectively. The minimum value of MSE was always reached well within that number. The training process was repeated several times in order to obtain the best performance of ANN. All trials were implemented in MATLAB Neural Networks Toolbox R2018a. Moreover, the optimal experiment that minimizes the number of ANN models trained and validated and maximized the model accuracy has been done. The architecture of ANN parameters such as the number of neurons in the hidden layer, activate function in hidden and output layers and statistical values MSE and R were estimated in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Data**} & \multicolumn{3}{c}{**Statistical Parameter**} \\ \cline{2-4}  & \multicolumn{2}{c}{**Mean \(\pm\) SD (Range)**} & **Coefficient of Variation** & **Skewness Coefficient** \\ \hline \multirow{2}{*}{\(\text{A}_{\text{loss}}\) \%} & 53.29 \(\pm\) 9.84 (41.00-80.09) & 0.19 & 1.35 \\  & CD\text  
It can be seen from Table 3, the lowest MSE = 0.000292 and high \(R\)-value = 0.9922 for Item 3 was obtained.
## 3 Types of Supplementary Dampers and Their Characteristics
### Base Isolation Systems
#### 3.6.4 Types of Base Isolation Systems  
Base isolation systems can be categorized into several types, each with unique properties:  
(a) Elastomeric bearings are a common type of base isolator. They include laminated rubber bearings (LRBs), which consist of alternating layers of rubber and steel plates.  
Lead-rubber bearings (LRBs) are similar but incorporate a lead core for additional energy dissipation. High-damping rubber bearings (HDRBs) use specially formulated rubber compounds to provide both isolation and damping.  
(b) Sliding systems represent another category of base isolators. Friction pendulum systems (FPSs) use a curved sliding surface to provide both period elongation and energy dissipation through friction. Double concave friction pendulum bearings are an advanced version of FPS, offering improved performance and stability.  
(c) Hybrid systems combine elements of both elastomeric and sliding systems, aiming to leverage the advantages of each type. These systems can be tailored to meet specific project requirements and site conditions.
# Extended-Reach Drilling (ERD)--The Main Problems and Current Achievements
###### Abstract
Operational challenges associated with drilling extended-reach wells from the drilling fluids' perspective typically include the following: hole cleaning and hydraulic management, equivalent circulating density (ECD) control, narrow mud weight/fracture gradient window, wellbore stability, stuck pipe, lost circulation, torque and drag and barite sag [18; 19]. The large number of independent variables influencing cuttings (debris) transport and efficient hole cleaning requires that a computer model is applied to make predictions [20]. Consequently, various computer packages and models have been developed for simulating cuttings transport and predicting hole cleaning in deviated and horizontal wells [21; 22; 23; 24; 25; 26; 27; 28]. They allow for an analysis of cuttings transport as a function of operating parameters, wellbore geometry, cuttings characteristics and mud fluid properties. Many researchers have used different machine learning (ML) methods (such as artificial neural networks (ANNs), the Markov rewards process (MRP), random forest regression (RF), linear regression and decision trees) to predict hole cleaning, cuttings transport, local axial velocity, torque and drag, well hydraulics, hydraulic friction factor and pressure losses [29; 30; 31; 32; 33]. A special challenge is the design of ERD wells in high-pressure and high-temperature (HTHP) conditions. [PERSON] et al. (2022) established a model to analyze the changing law of the temperature profile inside the production string of an HPHT gas well and recommended changing the well temperature profile by adjusting the production rate or by optimizing the thermal conductivity [34]. This manuscript has three interconnected goals: to give the reader a comprehensive overview of extended-reach technology from the beginning up until the latest technology improvements, single out the main designers and practical problems and solutions in extended-reach technology project implementation and to give detailed analyses of past and current projects and achievements. All of these should be the basis for a comprehensive discussion and answer on the question \"Have we reached the technological and economic limit in the implementation of extended-reach technology?\"
## 4 Performance Evaluation
### Evaluation Based on Testing Data  
We evaluated the performance of the proposed model in three classes using testing data and compared the results with other approaches on the same Kaggle datasets [7] to validate the advantage of our suggested technique. Table 2 displays that the accuracy of our suggested model is better than that of others, thereby demonstrating that our model has greater efficiency than others.
## 2 Literature Review
### Research on MOOC Learner Satisfaction Based on Course Review Data Analysis  
In analyzing the course review data regarding learners' satisfaction/dissatisfaction with their enrolled course [39], most studies have adopted qualitative manual coding methodologies (e.g., [40; 41]). For instance, by qualitatively analyzing 4466 course reviews, [41] recognized seven factors that contributed to learner engagement, including \"problem-centric learning, active learning supported by timely feedback, course resources that cater to participants' learning needs or preferences, and instructor attributes such as enthusiasm or humor\" (p. 1). However, the reliability of the results derived from qualitative analysis methodologies depends heavily on analyst expertise. Furthermore, as manual data coding is labor-intensive, only a small dataset can be investigated, making it difficult to deal with the constantly increasing number of course reviews.  
With the increasing availability of \"big data\" in MOOCs alongside the recent trend of applying machine learning and NLP techniques for educational purposes, there has been a rapid growth in studies that adopt text mining and machine learning to gain insight into the determinants of learner satisfaction based on course review data [42; 43]. For instance, ref. [34] utilized five supervised machine learning techniques to classify a random sample of 8274 MOOC review sentences into six major topical categories (i.e., structure, video, instructors, content and resources, interaction, and assessment), identifying gradient boosting trees model's excellent classification performance. By training machine learning classifiers based on K-nearest neighbors, gradient boosting trees, support vector machines, logistic regression, and naive Bayes for the analysis of 24,000 reflective sentences produced by 6000 MOOC learners, [44] found the satisfactory performance of gradient boosting trees in understanding learners' perceptions. However, ref. [34; 44] merely adopted machine learning, and deep neural networks that are widely accepted as preferred solutions for various NLP tasks were not considered. In addition, a comprehensive analysis of course review data from the perspectives of both topics and sentiments in an automated manner is lacking.  
To capitalize on the advantages of NLP-oriented text-mining methodologies, the present study incorporates different analysis methods such as TextRCNN and sentiment analysis to conduct a more thorough analysis of the textual content of learner review data. By uncovering learners' focal points and sentiments based on course review data, we aim to obtain an in-depth understanding of learners' perceptions of their learning experiences in MOOCs.
## 3 Results  
### Analysis of FAME Profiles and Contents  
The FAME profiles and contents of the in situ procedure were analyzed by GC and the reaction conditions of the in situ method were: methanol:biomass = 90 mL/g, HCl 5% by vol., 70 \({}^{\circ}\)C, 1.5 h. As shown in Table 1, the relative FAME profiles of _Nannochloropsis gaditana_ resolved by GC consist of methyl myristate (14:0), methyl palmitoteate (16:1), methyl palmitate (16:0), methyl cis-9-octadecenoate(18:1), methyl cis,cis-9,12-octadecadienoate (18:2), and methyl all-cis-5,8,11,14,17-eicosapentaenoate (20:5). Other peaks were observed but not resolved and may include alkanes and alkyl diols according to mass spectra. Fatty alcohols and alkyl diols are known in _Nannochloropsis oculata_ from other reports as well [25].  
_Nannochloropsis gaditana_ contained as much as 34.46% of its total fatty acids as EPA, which represented the most abundant FAME measured by in situ transesterification. Other FAME components in large amounts include methyl palmitate (16:0, 26.60%) and methyl palmitoate (16:1, 18.34%). Other FAME was detected in small quantity. The fatty acid profile of _Nannochloropsis gaditana_ was observed to be naturally high in EPA and devoid of DHA, thereby providing an opportunity to maximize the efficacy of EPA quantification with this method.
# Organic Materials Used for Giant Buddhas and Wall Paintings in Bamiyan, Afghanistan
## 2 Materials and Methods
### Organic Media Analysis by GC-MS  
#### 2.3.1 Fatty Acids, Proteins, and Plant Gum Analysis Using GC-MS  
GC-MS analysis was carried out in collaboration with the Getty Conservation Institute to identify the organic compounds by using several protocols, each of which has been optimized specifically to be applied to oils, resins, waxes, protein, and plant gums in 2007 [17; 18]. Protein analysis was based on the measurement of amino acid composition by quantifying the tert-butyldimethylsilyl (TBDMS) esters of amino acids with GC-MS. Because the requisite silylation with \(N\)-(tert-butyldimethylsilyl)-\(N\)-methyltrifluoroacetamide (MTBSTFA) is adversely affected by silica, a white pigment commonly found in sand and wall paintings, protein analysis could also be achieved either by derivatization with ethyl chloroformate that minimizes the interference of pigments or by improving the protocols of other methods, such as LC-separation, at high temperatures and utilization of different reagents [19]. Nevertheless, some Bamiyan paint samples yielded amino acid compositions that matched protein binders reasonably by the present GC-MS of TBDMS esters, in spite of the possible adverse effect of pigments thought to be contained in the majority of samples.  
Drying oils were identified based on fatty acid ratios. The concentration of palmitic (P), stearic (S), and azelaic (A) acids were used to calculate the P/S ratio and identify the drying oil since palmitic and stearic acids are less prone to chemical reactions upon aging than unsaturated fatty acids [20]; typical values of P/S ratios and iodine values for several drying oils are listed in Table 1.  
Further, an A/P ratio of 0.6 or higher was required for a drying oil. Egg lipids generally have A/P ratios below 0.5 and contain significant amounts of protein [21].  
Dicarboxylic, suberic, azelaic, and sebacic acids are formed during the auto-oxidation of the polyunsaturated chain present in fresh drying oils, which can be considered markers for drying oils. The amount of azelaic acid is significantly lower in egg yolk than in drying oils due to the lower amounts of unsaturated fatty acids [17]. The azelaic (A) to palmitic (P) acid ratio (A/P) can be used to discriminate drying oils (A/P > 0.6) from egg yolk (A/P < 0.3) [20; 21].  
The characterization of proteins and plant gums by GC-MS was accomplished by comparing the amino acid composition of proteins and the carbohydrate composition of plant gums to those of standard reference materials using correlation coefficients [22]. A correlation coefficient of 1.0 represents perfect match, but for environmental conditions found in the Bamiyan caves, that of 0.93 is an acceptable match. The identification of these materials can be problematic, and there can be negative effects to profiles due to aging and pigments, while mixtures of media can significantly lower match quality [19; 22].
# Comparing Application-Level Hardening Techniques for Neural Networks on GPUs
## 3 Proposed Evaluation Methodology
### Error Models
#### 3.1.2 Neuron Bit Error Rate  
Typically, the most prevalent fault injection method for evaluating software-based HTs involves corrupting multiple neurons neglecting any error propagation pattern. We instead resort to a hardware-aware error model that simulates the faults in the FMA cores on a single SM that executes parallel tiling matrix multiplication algorithms on a graphical processing unit (GPU) device [21]. While the NN's weights are fixed, the neuron's output changes depending on the input FM to the target layer. Before the parameter corruption stage, the framework generates the fault list, which contains the layer, the tile size (TS), the block error rate (BIER), the neuron error rate (NER), and the bit location. All these fault features define the output FMs (i.e., intermediate NN output) portion to corrupt along with the specific bit locations to mimic the effect of permanent faults affecting the FMA cores in the GPU device [20; 21]. According to the TS, each channel of the intermediate feature maps is divided into several possibly overlapping blocks. Consequently, the rate of corrupted blocks is defined as \(BIER=\frac{\#target\_blocks}{\#total\_blocks}\). Within the block, the rate of corrupted neurons is defined as \(NER=\frac{\#target\_neurons}{\#total\_neurons}\). In the end, a bit-flip is performed on the target bit location, considering the binary encoding of the selected entries. Due to the fixed bit location for each experiment, the rate of parameters over all the FM entries represents the bit error rate that can be obtained as \(BER=NER\times BIER\).