esa-sceva/satcom-chunk-collection huggingface dataset:
# Insights into PCDD/Fs and PAHs in Biomass Boilers Envisaging Risks of Ash Use as Fertilizers
## 3 Experimental Part
### Results and Discussion
#### 3.2.2 Chemical Composition, PCDD/Fs and PAHs in Ashes
Highly efficient ESP systems may keep PCDD/Fs air emissions below emission limit values (ELVs), however the collected ashes may become contaminated with PCDD/Fs. Concentrations of PAHs of ash blends of the units BFB1, BFB3 and GF are presented in Table 8. Most PAHs compounds were non-detected (ND) and many are close to EQLs of PAHs. Levels are relatively low, compared with the 16.8 mg/kg of PAHs found in wood ash tested as soil fertilizer [55]. Individual concentration of PAHs and \(\Sigma_{16}\) PAH of all BA are lower than those of FA blends. Only FABFB3, which also contains significant PCDD/Fs, reveals the presence of carcinogenic Cry and also BghiP, in low concentrations. LPAHs constitute 79-100% of total PAHs detected, of which the most prevalent is NaP, 52-93% of \(\Sigma\)PAHs, except in BABFB3. Flut and Pyr were reported as the two most abundant HPAHs in wood combustion [72], however, they are present only in FA blends, accounting for less than 24% of \(\Sigma\)PAH. Presence of the highly volatile NaP in ashes, similar to other works [61], might be a result of relevant generation, and/or its fate during and after combustion. NaP can be originated by several pathways, including sequential dechlorination/destruction of PCDD/Fs [248]. Presence of PAHs is usually correlated with unburned matter in ashes and PCDD/Fs, however, this trend was not found in this study; FAGF has similar PAH contents as other FA blends that contain much less C. Some PAHs formed might have been converted to PCDD/Fs, especially PCDF, by cracking and chlorination in the post-combustion zones [229]. The BaP-EQs obtained according to the US-EPA RPFs are about a third lower than those using the factors of Nisbet and Lagoy. This is expected, since RFPs do not cover the non-carcinogenic PAHs, and some RFPs are lower than those of Nisbet and Lagoy, namely for BkFA, DahA, and Chry. Differences between LB and UB of \(\Sigma_{16}\) PAH are low, indicating that ND handling has low impact for the PAHs results obtained. The same occurs when accounting for carcinogenesis, \(\Sigma\)/Carc., since the concentration of Chry is close to its EQL.
# Two-Step Classification with SVD Preprocessing of Distributed Massive Datasets in Apache Spark
## 5 Evaluation
### Higgs Dataset
#### 5.1.3 Discussion over Higgs Dataset  
To sum up the results in the binomial classification case, we could say that the proposed two-step classification method produced, in most cases, about 5% less accurate results. Taking into consideration the time needed to calculate the model, the results are not clear. There were cases that the proposed method performed better than the one-step (normal) classification method, like the case of the multilayer perceptron when we considered the total number of cases in which it took 15% less time, but there were also cases that it took it more time, like the decision tree classifier. There, we took into account limited number of features that required about 32% more time to calculate the mode, whereas there was a minor increase in the metrics, e.g., 2%. It must be noted though, that this output could be dataset specific, as the proposed method did not work as well as it worked in the case with all features taken into account.  
Before proceeding, we should note that, as a general rule of thumb, when we took into account the total number of features, the classifier performed better in terms of the \(F1\) metric, but it required more time to compute the classification models. To be more specific, in the case of the one-step classification method, in almost all of the cases, we lost about 7-10% in terms of the \(F1\) metric, while in terms of computational time, the following percentages are considered (for 200K rows). In the case of multilayer perceptron, it required 13% less time to compute the model, in following the gradient boosted tree classifier took 15% less time, the decision tree 20% less time, but in the case of random forest we had almost a tie (required 3% more time).  
Examining the two-step classifier, in the case of decision trees, we lost about 5% in the \(F1\) metric, while in the case of random forest, the loss was 6%. In addition, in the case of logistic regression, the loss was 4%, in following, in the case of gradient boosted tree classifier, the loss was 5.5% and in the case of multilayer perceptron, we had the highest loss of 12%. In terms of computational time, we had an almost tie (about 1% to 3% difference) in all but the multilayer perceptron classifier, which took 13% less time to be computed. As expected, there is no point in comparing the computational time of the logistic regression classifier, since its computational time is very low.
## 2 Materials and Methods
### Data Analysis  
Ground reaction force data was filtered at a cut-off frequency of 50 Hz, using a fourth-order Butterworth, low-pass filter. A custom program, written in MATLAB (MathWorks, Natick, Massachusetts, USA), was used to process these data. Detailed information about data processing has been published elsewhere [22; 28; 29]. First, a point of interest (POI) was defined as the first point above 75% of a subject's body weight (BW), with a vertical ground reaction force slope less than 15 BW/s. These values were chosen to ensure that vertical load rates were calculated over appreciable vertical GRF changes, until the end of the loading phase [28]. The VILR (BW/s) was defined as the peak vertical loading rate between any two successive points, from 20-100% of the force at the POI. These values were chosen to ensure that the VILR was taken from the most linear portion of the rise in vertical forces during the early stance. PILR was calculated over the first 15% of stance, and medial/lateral loading rates were identified using the initial 25% of stance [22]. These values were chosen to ensure that these variables were captured as early in stance as possible. Ground reaction force peaks were calculated as the peak force across the stance phase [22]. Ground reaction force impulses were determined as the time integral of the ground reaction force over stance. Vertical stiffness at initial loading (VSIL) was measured over 20-80% of the POI, using the peak vertical force divided by the vertical change in center of mass (kN/m) [22]. The vertical displacement of the center of mass was determined from the double integration of the vertical force curve [30].
# Improvement in the Convolutional Neural Network for Computed Tomography Images
## 4 Discussion
[13], the authors used AlexNet and GoogLeNet [14] and created four types of image classification models with and without transfer learning, and the accuracy of the classification of tuberculosis was compared. Although the creation of multiple models and the accuracy comparison were similar to those in this study, [PERSON] et al. did not change the CNN parameters, such as the filter size of the convolution layer. AlexNet, GoogLeNet, and transfer learning are technologies developed for general images and are not specialized for medical imaging. Therefore, in this study, we improved theaccuracy by specializing the filter sizes of the convolution layer and max-pooling for medical images. This study has five limitations. First, the comparison target was only ResNet. The created CNN was compared with ResNet, which is a typical example of high-performance CNN; however, the latest CNN is more accurate [15; 16; 17; 18; 19; 20], so the created CNN should have been compared with the latest CNN. Second, we did not evaluate some parameters. However, considering that there is a limit to the number of parameters that can be evaluated individually and that we obtained a result of 94.40%, we believe that the number of parameters was sufficient. Third, we used the holdout validation. Ideally, k-fold cross-validation should be used, but we chose the holdout validation study because there were too many models to validate. In general, the ratio of data sets for holdout validation is 80:20 [5; 21], and the mean performance is obtained from multiple data sets. In this study, the ratio of data sets was not 80:20, and the number of data sets was one. However, since some papers use a 90:10 ratio of data sets [22; 23] and others use various ratios [1; 6; 24], this is not considered a problem. As for the number of data sets, it is not a problem since there are papers that validate with only one [3; 4; 25]. Fourth, only one generalization capability test was used in this study. By classifying small lesions, it is possible to verify the ability to respond to minute changes and clinical practicality. Since the accuracy in actual diagnosis is not verified, the classification of lesions and malignancy needs to be verified before it is used in clinical practice. However, being able to train with the original pixel size without resizing is useful, because it may capture more minute features. Finally, we tested the generalization capability only on the model with the highest overall accuracy. Testing with other models could reveal the relationship between accuracy and parameters. The CNN we created in this study can quickly create a model, even if it is trained with a large number of medical images. This feature has the potential to create an image classification model that can be updated daily by training with images taken on the same day. As a result, the created CNN can be optimized for the imaging method, rules, radiologist habits, and patient tendency for each facility and can contribute to the creation of a diagnostic support application specialized for each facility.
# A Secure Link-Layer Connectivity Platform for Multi-Site NFV Services
## 2 Background on Multi-Site VNF Communications
### ETSI Network Functions Virtualization
Any real-world network deployment implies strong requirements in terms of topology (a network must connect given nodes in a graph) and pervasiveness (the graph must cover a wide number of physical locations), and these requirements translate into the need for deployments that are far from the current highly centralized practices common in the cloud infrastructures that constitute the inspiration of the NFV proposal [12]. Moreover, different segments in the network architecture (fixed or mobile access, core, optical transport, etc.) impose diverse requirements on the underlying infrastructure and their orchestration policies. Finally, if we add the organizational aspects, requiring the cooperation among different entities to provide most services, we have that multi-site [4], and even multi-MANO [13], solutions are essential for the provision of any network service intended to support end-user requirements. As a final remark, it is noteworthy the significant impact that the ETSI NFV architectural framework is causing within the telecommunications community where it has been widely embraced. In particular, a significant effort has focused on providing functional implementations of the ETSI NFV framework, aiming at accelerating, enriching, and sustaining the development and adoption of this technology [14]. On the other hand, these initiatives have also allowed NFV to become a key player in the development of novel concepts arising from 5G networks such as network slicing [15]. Moreover, the ETSI NFV reference framework has been adopted by 3 GPP in the realization of technical specifications and standards regarding 5G mobile networks and beyond [16].
# Coarsely Quantized Decoding and Construction of Polar Codes Using the Information Bottleneck Method
## 7 Conclusions  
This paper presented the application of the information bottleneck method for construction and coarsely quantized decoding of polar codes. The construction of a polar code requires computing the capacities or error probabilities of its bit channels, whose output alphabet grows exponentially in the codeword length. The information bottleneck framework was used in the discrete density evolution of these virtual bit channels in order to restrict their output alphabet to a small finite size. The quantized bit channels are suitable for capacity or error probability computations. To that end, the famous Tal and Vardy method for constructing polar codes turns out to be equivalent to a particular information bottleneck algorithm. However, they did not fully exploit the benefits offered by the framework. We showed that with the appropriate book-keeping, the hard work done during the discrete density evolution can also be used for decoding. More precisely, the intermediate results of the density evolution, in the form of discrete mappings or lookup tables, are use to replace the LLR computations of conventional successive cancellation list (SCL) decoding. All the operations, except the path metric update, in the information bottleneck SCL decoder are table lookups of unsigned integers. This computational simplicity of the coarsely quantized information bottleneck decoder causes only a small block error rate degradation. The number of lookup tables required in the information bottleneck SCL decoder increases with the codeword length \(N\). We used message alignment to reduce the number of translation tables required to translate the unsigned integer messages into LLR values for the path metric update. The aligned information bottleneck decoder requires only a single translation table regardless of the codeword length and fewer bits to represent the LLR values, which is a reduction in their space complexity. Moreover, the aligned information bottleneck decoders also facilitate a hardware-friendly implementation of the path metric update.  
Figure 25: Path metric increments computed for the aligned translation table of Figure 11 according to Equation (9). \(|\mathcal{T}^{*}|=16\) for the polar code with \(N=128\), rate = 0.5, and design \(E_{b}/N_{0}=3\) dB.  
**Author Contributions:** Conceptualization, S.A.A.S., M.S. and G.B.; software, S.A.A.S. and M.S.; validation, S.A.A.S., M.S. and G.B.; writing--original draft preparation, S.A.A.S.; writing--review and editing, S.A.A.S., M.S. and G.B.; visualization, S.A.A.S. and M.S.; supervision, G.B.; funding acquisition, S.A.A.S.  
**Funding:** This publication was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)--Projektnummer 392323616 and the Hamburg University of Technology (TUHH) in the funding program \"Open Access Publishing\".  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Short Text Classification Based on Enhanced Word Embedding and Hybrid Neural Networks
## 3 Methodology
### Enhanced Word Embedding Based on Word2 Vec
#### 3.1.2 TF-IDF-CDW  
TF-IDF is a commonly adopted term-weighting method in natural language processing, designed to evaluate the significance of a word in a specific document [38]. Term Frequency (TF) reflects how often a term \(t_{i}\) occurs within a document \(d_{j}\) and is computed as follows:  
\[TF(t_{i},d_{j})=\frac{n_{i,j}}{\left|d_{j}\right|} \tag{2}\]  
where \(n_{i,j}\) denotes the frequency of term \(t_{i}\) in document \(d_{j}\), and \(\left|d_{j}\right|\) is the total number of words in \(d_{j}\). IDF evaluates the rarity of a term across the entire corpus. The formula is given as follows:  
\[IDF(t_{i})=\log\frac{\left|D\right|}{1+\left|\left\{d_{j}:t_{i}\in d_{j} \right\}\right|} \tag{3}\]  
where \(\left|D\right|\) represents the overall count of documents within the corpus, and \(\left|\left\{d_{j}:t_{i}\in d_{j}\right\}\right|\) is the count of documents containing term \(t_{i}\). By multiplying TF and IDF, the TF-IDF score is obtained, indicating the significance of term \(t_{i}\) in document \(d_{j}\):  
\[TF-IDF(t_{i},d_{j})=\frac{n_{i,j}}{\left|d_{j}\right|}\times\log\frac{\left|D \right|}{1+\left|\left\{d_{j}:t_{i}\in d_{j}\right\}\right|} \tag{4}\]  
However, conventional TF-IDF does not account for the distribution of terms across different categories. As a result, some terms that appear frequently within specific categories but rarely across others may receive inappropriately low weights. To address this limitation, we propose an enhanced metric called CDW. It is defined as follows:  
\[CDW(t_{i})=1+\frac{\sum\limits_{c\in C}P(c|t_{i})\log P(c|t_{i})}{\log\left|C \right|+1} \tag{5}\]  
where \(\left|C\right|\) is the total number of categories, and \(P(c|t_{i})\) represents the proportion of documents containing \(t_{i}\) that belong to category \(c\). This method is designed to suppress terms that are uniformly distributed across categories, while emphasizing those with category-specific preferences. CDW is particularly effective at identifying terms strongly associated with specific categories. By incorporating CDW, the model assigns greater weight to category-specific terms.  
Finally, the TF-IDF-CDW score is computed by multiplying the TF-IDF value with the corresponding CDW value, thereby capturing both document-level and category-level term importance:  
\[TF-IDF-CDW(t_{i},d_{j})=TF(t_{i},d_{j})\cdot IDF(t_{i})\cdot CDW(t_{i}) \tag{6}\]  
This formulation allows the model to assign greater importance to terms that contribute more significantly to class distinction, thereby improving the semantic representation quality and classification performance.
###### Abstract
[18] established a model based on the composite shear failure surface of sandy soil for solution, without considering the contribution of clay cohesion to the pullout bearing capacity, as well as the increase in side friction resistance over time. [PERSON] et al. [19] investigated the pullout bearing capacity of pile foundations at different slenderness ratios, but this model similarly did not consider the cohesion as well as the recovery of side friction resistance over time. [PERSON] et al. [20] established a predictive model for pullout piles using the horizontal strip method. They initially assumed the type of failure mode for the pullout piles and then established equations based on the limit equilibrium method to derive a calculation method. This method considers the contribution of soil cohesion to the pullout bearing capacity, but it does not account for the increase in bearing capacity due to the recovery of soil strength around the pile. [PERSON] et al. [21] used software to simulate the variation of uplift bearing capacity of driven piles on offshore platforms over time. They proposed a time-dependent calculation formula for the uplift bearing capacity of piles, considering the influence of permeability coefficients, and validated the model's accuracy through field tests. However, the aforementioned calculation methods for pullout bearing capacity are mostly applicable to sandy soils and do not consider the effect of cohesion on pullout bearing capacity, nor do they take into account the impact of strength changes in the soil surrounding the pile on the pullout bearing capacity. Therefore, there is a significant error when applied to the structured clay of the Zhanjiang Formation. Additionally, current standards primarily calculate the pullout bearing capacity based on the compressive bearing capacity of piles and do not account for the time-dependent effects of thixotropic soil around the pile on bearing capacity. This presents a challenge in applicability for pullout piles in the structured clay foundation of the Zhanjiang Formation. This study investigates the thixotropic behavior of the cohesion and internal friction angle of remolded structured clay from the Zhanjiang Formation, as well as the ultimate bearing capacity of uplift piles under different resting times, through direct shear tests of surrounding soil and static loading tests on model piles under varying resting durations. Based on these findings, a time-dependent predictive model for the ultimate bearing ability of uplift piles in structured clay foundations from the Zhanjiang Formation has been established and validated through model tests.
## 3 Elastographic Methods  
According to international guidelines [1; 2; 4; 6], ultrasound-based elastographic methods can be divided into shear wave elastography (SWE) and strain elastography (SE). The SWE techniques measure the speed of the shear waves generated in the tissues by either an external mechanical push or an ultrasound radiation force impulse (ARFI). A greater speed indicates increased tissue stiffness which is known to correlate with the dynamics and severity of fibrosis. SWE techniques can be divided into transient elastography (TE) (FibroScan(r)) and ARFI-based techniques. These latter are either pSWE, including Virtual Touch Quantification VTO(r) from Siemens, ElastPQ(r) from Philips, SWM(r) from Hitachi, STQ(r) from Mindray, S-shearwave(r) from Samsung, QElaXto(r) from Esaote or 2D-SWE (first available on the Aixplorer system from SuperSonic Imagine, and later on systems from Siemens, General Electric (GE), Canon, Philips and Mindray) [4; 6; 44]. The speed of the shear waves is measured in meter/second (m/s); using [PERSON]'s modulus it can be converted into stiffness measured in kilopascals (kPa), assuming that the tissue is purely elastic and its elastic response is linear, and that the tissue density is always 1000 kg m\({}^{-3}\)[1].
# Experimental and Empirical Studies to Evaluate Liquefaction Resistance of Partially Saturated Sands
## 5 Proposed Empirical Functions
### Bulk Modulus  
As stated earlier, for partially saturated sands with occluded bubbles, the bulk modulus of the pore fluid (gas-water mixture) is an essential parameter that controls the liquefaction behavior. The bulk modulus of the pore fluid (\(K_{f}\)) was calculated using Equation (2) for a range of experiments conducted at varying degrees of saturation, confining stress, and back pressures. The bulk modulus of the partially saturated sands (\(K_{f-part}\)) was normalized with that of their saturated counterparts (\(K_{f-sat}\)) and plotted against the degree of saturation (\(S_{r}\)) and liquefaction-resistance ratio (\(LRR\)) as shown in Figure 13. A trend line was fitted to the data points that produced the relationship below:  
\[LRR=0.0017\cdot\left(\frac{K_{f-part}}{K_{f-sat}}\right)^{-0.9}+0.9983 \tag{9}\]  
This function has a dependent variable of the liquefaction-resistance ratio (\(LRR\)) and an independent variable of the normalized bulk modulus (\(\frac{K_{f-part}}{K_{f-sat}}\)) at its base. The exponent of \(-0.9\) determines the growth/decay rate and shape of the function. The scaling factor of 0.0017 indicates the amplitude of the decrease/increase in the liquefaction resistance ratio, and the constant of 0.9983 corresponds to the effects of other factors. The negative exponent  
Figure 13: Variation in liquefaction-resistance ratio with normalized bulk modulus of pore fluid.  
suggests the decay of the liquefaction-resistance ratio with the increasing normalized bulk modulus of the pore fluid.  
It can be observed in Figure 13 that the bulk modulus of the pore fluid significantly dropped as gas bubbles were generated in the nearly/fully saturated sand specimens. The reduction in the degree of saturation of the partially saturated sands was followed by a reduction in the bulk modulus of the pore fluid. This figure also reveals that the liquefaction-resistance ratio increased with a decreasing pore-fluid bulk modulus or an increasing-pore fluid compressibility, which was the reciprocal of the bulk modulus (\(C_{f}=\frac{1}{k_{f}}\)).