esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Case Report  
We present the case of a 6-year-old girl who was referred to the Department of Odon-tostomatology at the Polytlinic of Bari (University of Bari Aldo Moro) due to hemifacial swelling (Figure 1). Her primary dentist had advised urgent consultation at the emergency department of the Polytlinic of Bari, suspecting a maxillary phlegmon. The patient did not exhibit any tenderness upon palpation. At the intraoral examination, no necrotic dental elements were observed that could account for an infectious-inflammatory process. The patient had a confirmed diagnosis of PMS, established through genetic testing in 2020. Given the clinical presentation, hemifacial lymphedema was suspected, which is an atypical manifestation within the spectrum of PMS. The patient, born to non-consanguineous parents, had been referred for evaluation due to developmental delays and prominent swelling on the right side of her face.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Signs/Symptoms**} & **PMS Individuals with 22q13** & **PMS Individuals with** \\  & **Deletions (\%) (Without SHANK3** & **SHANK3 Variants (\%)** \\  & **Haploinsufficiency)** & **PMS Individuals with** \\ \hline Retrognathia & 25\% & 0\% \\ Macrocephaly & 17\% & 15\% \\ Microcephaly & 16\% & 10\% \\ Facial Lymphhedema & & \\ Physical and dermatological anomalies & & \\ Clinodactyl 5 th finger & 20\% & 35\% \\
2–3 syndactylyy of toes & 28\% & 45\% \\ Sandal gap & 54\% & 7\% \\ Lymphhedema & 11\% & 0\% \\ Eczema & 21\% & 30\% \\ Hypohidrosis & 37\% & 8\% \\ Hyper-extensible joints & 22\% & 60\% \\ Small/malformed nails [14] & 32\% & 45\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._The child's history revealed global developmental delay, the absence of verbal communication, and mild hypotonia. Behavioral assessments indicated traits consistent with autism spectrum disorder (ASD), including limited social interaction and repetitive behaviors. Further diagnostic evaluation included chromosomal microarray analysis, which confirmed a deletion in the 22q13.3 region encompassing the SHANK3 gene. Facial MRI revealed diffuse soft tissue swelling consistent with lymphedema, without evidence of vascular anomalies. Indocyanine green lymphangiography demonstrated delayed lymphatic drainage in the affected hemiface. Laboratory tests excluded infectious or autoimmune causes. The patient was managed through a multidisciplinary approach. A lymphedema specialist initiated manual lymphatic drainage therapy and recommended facial compression garments. Developmental interventions included speech and occupational therapies, while structured behavioral therapy was implemented to address ASD-related symptoms. Six months into treatment, the hemifacial lymphedema remained stable with improved cosmetic outcomes; however, developmental progress was gradual, and verbal communication remained absent. This case highlights hemifacial lymphedema as a novel manifestation of PMS, expanding the phenotypic understanding of the syndrome. It emphasizes the importance of comprehensive, multidisciplinary management to address the complex needs of patients with PMS.
# Quantifying Privacy Risks for Continuous Trait Data
## 6 Attack Validation
### Mathematical Analysis
#### 6.3.4 Overall Analysis  
From the experimental results and mathematical analysis above, to judge whether \(\mathbb{T}^{\prime}(\cdot,k^{\prime})\) in a continuous trait dataset \(\mathbb{T}^{\prime}\) can effectively match a SNP sample \(\mathbb{S}^{\prime}(\cdot,l^{\prime})\), there are three major aspects as follows:  
1. Both the SNP size \(m\) and the _data sensitivity score_ of the QTL dataset \(\mathbb{T}^{\prime}\) are most positively correlated with matching accuracy. The larger the \(m\) and _data sensitivity score_ Sen related to the \(r,z,p\) of each \(\mathbb{T}^{\prime}(i,\cdot)\), the higher the matching accuracy.
2. The discrimination rate of TP and FP matching between \(\mathbb{T}^{\prime}\) and \(\mathbb{S}^{\prime}\) by _F score_ is most positively correlated with the MAF value \(p_{\text{m}}\) and will be affected by \(m\) to some extent. The larger the MAF, the easier to distinguish between TP and FP matching.
3. The sample size \(n\) significantly affects matching accuracy, especially when the MAF value is small. The larger \(n\), the lower the matching accuracy.  
The sample size \(n\), the SNP size \(m\), and the _data sensitivity score_ Sen can be used to predict the approximate range of matching accuracy; \(m\) and MAF value \(p_{\text{m}}\) can be used to estimate the discrimination rate of TP and FP matching by _F score_. In the next subsection, we will verify the effectiveness of these three major aspects in predicting the privacy risk of a real QTL dataset.
# Material Characterization for Sustainable Concrete Paving Blocks
## 1 Introduction
In addition, using those recycled aggregates as fine aggregates, the replacement level shall not go beyond 40%. Recycled rubber particles were also used as fine aggregates in precast concrete paving blocks. [PERSON] [10] studied the properties of concrete tactile paving blocks made with recycled type rubber. The study also incorporated GGBS in the blocks as cement replacements. It was found out that using a certain amount of GGBS to replace cement can increase the compressive strength and tensile splitting strength of concrete blocks [17]. [PERSON] and [PERSON] [18] evaluated the effect of replacing the cement with GGBS on interlocking paving blocks, and the replacement levels of GGBS used to replace the cement in their research ranged from 10% to 60%. The test results show that the compressive and tensile splitting strength increase with the curing period increasing, and the best strengths are obtained when the GGBS replacement level is ranging from 20% to 60%. This research aims to investigate the properties' variations of sustainable concrete paving block incorporating different contents of construction wastes. Up to now, recycled materials, such as crumb rubber and crushed tempered glass, are rarely used in industry to replace the natural fine and coarse aggregate in manufacturing concrete paving blocks. Furthermore, the most current research about recycled concrete paving blocks does not cover the block's abrasion resistance and slip resistance. In this research, five different types of waste materials were used to replace the coarse aggregates, fine aggregates or cement in the concrete mixture with a 28-day target mean strength of 30 MPa. For the concrete paving blocks mixed with waste materials, five material properties such as compressive strength, tensile splitting strength, water absorption, abrasion resistance and slip resistance were investigated. Those results were compared with the related standard requirements to obtain the maximum replacement levels of recycled materials used in this study. Furthermore, the test resultsprovided a reference data for the further investigation about the concrete paving block mixed with multiple recycled materials.
# SBNNR: Small-Size Bat-Optimized KNN Regression
## 3 Preliminaries
### K-Nearest Neighbors  
K-Nearest Neighbors (KNN) is an intuitive and non-parametric machine learning technique that excels in both simplicity and effectiveness. In KNN regression, the primary objective is to predict the value of a query data point based on the values of its nearest neighbors within the training dataset [34].  
As illustrated in Algorithm 2, the procedure is as follows: For a specified query point, the algorithm identifies its \"K\" nearest neighbors from the training dataset. These neighbors are selected utilizing a distance metric, commonly employing Euclidean distance, although alternative distance metrics may be suitable based on the specific problem requirements.  
```
1:procedureKNN_Regression(\(X\_train\),\(y\_train\), \(X\_query\), \(K\))
2:for each x_query in X_querydo
3:distances \(\leftarrow[]\)
4:for each \((x\_train,y\_train)\) in \(zip(X\_train,y\_train)\)do
5:distance \(\leftarrow\) Euclidean_Distance(\(x\_query,x\_train\))
6:distances.append\(((y\_train,distance))\)
7:endfor
8:sorted_distances \(\leftarrow\) Sort_By_Distance(\(distances\))
9:nearest_neighbors \(\leftarrow\) sorted_distances[:\(K\)]
10:predicted_value \(\leftarrow\) Average(nearest_neighbors)
11:Output(predicted_value)
12:endfor
13:endprocedure
14:procedureEuclidean_Distance(\(x1,x2\))
15:Return \(\sqrt{\sum{(x1_{i}-x2_{i})^{2}}}\) for each feature \(x1_{i},x2_{i}\) in \(zip(x1,x2)\)
16:endprocedure
17:procedureSort_By_Distance(\(distances\))
18:Return \(sorted(distances,key=lambda:x[1])\)
19:endprocedure
20:procedureAverage(nearest_neighbors)
21:\(total\gets 0\)
22:for each \((y\_neighbor,\_)\) in nearest_neighborsdo
23:\(total\gets total+y\_neighbor\)
24:endfor
25:Return \(total/K\)
26:endprocedure
```  
**Algorithm 2** K-nearest neighbors (KNN) Regression  
Upon identifying the nearest neighbors, their known output values are utilized to approximate the output for the query point. In regression tasks, this generally entails computing the mean or weighted average of the output values from the neighboring data points. This approach is predicated on the notion that analogous data points generally yield comparable output values.  
KNN is distinguished by its absence of prior assumptions regarding the data distribution or the functional relationship between features and target values. Rather, it utilizes the intrinsic structure of the data, rendering it an especially advantageous instrument for tasks involving complex or obscure data relationships. This text succinctly outlines the mathematics of this renowned model.  
Consider a training dataset (\(S=\{(x_{1},y_{1}),\ldots,(x_{n},y_{n})\}\)) where any \(x_{i}\) is a single input vector and \(y_{i}\) is the corresponding output value. For a test (unseen) instance such as \(x\), \(y_{i}(x)\) is the output value (\(y_{i}\)) of the \(i_{th}\) nearest neighbor to \(x\). The prediction is made generally using Equation (6) in regression tasks.  
\[\hat{y}=\frac{1}{\hat{k}}\sum_{i=1}^{k}y_{i}(x) \tag{6}\]  
\(\hat{y}\) is the final prediction.
## 5 Case-Study  
### Case-Study Description  
During this research we selected a specific case-study where two workers are cooperating with one cobot as it can be seen in Figure 10a. In this scenario, the customer initializes a production order by selecting the main features of the product and the required number of units. A centrifugal pump has been chosen as a customized product example. When the customer sends the production order to be executed, the cobot will pick and place the proper amount of the product parts from their storage space to the worker workstation, where the worker is able to assemble the product.  
The goal of the case-study is to implement the solution concept. Accordingly, the deployment CPROSA-holarchy via JADE is shown in Figure 10b, JADE framework contains four containers which  
Figure 10: (**a**) Case-Study Schematic; (**b**) JADE Multi-Agent System; (**c**) Customer Holon Interface; (**d**) Products Holons Interface; (**e**) Order Holon Interface; (**f**) Operation Resources Holons Interface.  
present the previously described CPROSA holons. Two CHs can be found in this case-study as it can be seen in Figure 10c. Both the CHs have a similar UI. The UI of the CH is providing a tool for ordering a specific product with certain features (i.e., parts). The customer selects the basic features and defines the needed amount of the product then sends the order to the PH. A customized centrifugal pump can be manufactured in this case- study. The UI of the pump holon can be seen in Figure 10d. The pump has four features which are the casing, the electrical-motor, the impeller and the shaft. When a PH receives a product order from the CH, it constructs the building plans for this product order as it will be discussed later in detail. The PH also has the ability to rearrange the orders or modify them before sending them to the OH.  
The OH is responsible for collecting the orders from the PH as it is shown in Figure 10e. Simultaneously the OH discovers the existence of the operation resources. Furthermore, it starts and stops the production process. Two WHs (WH1, WH2) and one Robot Holon (RH) can be found as operation resources in this implementation as it is shown in Figure 10f. The function of the workers within this case-study is to perform an assembly operation of the customized pump orders, while the function of the cobot is to pick and place the right amount of the pump features to the worker workstation. As we do not have a robot hardware during this implementation, we assumed that the cobot always takes two seconds to pick and place one pump. Therefore, the RH multiplies the number of pumps by two to obtain the overall time needed for the whole pick and place operations. Accordingly, the RH can have two statuses, either busy or free. Another status is required for the WH which is a reserve status. In the reserve status the WH is waiting the cobot to load at least one pump, therefore the worker can start the assembly operation and subsequently the WH status turns to be busy. The WH stays in the busy status till the worker presses the task-done button, then the WH status would be free.
## 1 Introduction
A key enzyme, _Is_PETase (EC 3.1.1.101), was identified as being responsible for PET depolymerization, which showed more activity than others when compared at mild temperatures (27-40 \({}^{\circ}\)C) [19; 25; 26]. Several _Is_PETase structural studies were published [17; 27; 28; 29], which set the basis for mechanistic studies [30; 31] as well as for the development of a series of mutants with increased activity [32] or thermostability [33; 34; 35; 36; 37]. Both thermostable and mesostable PET-active enzymes constitute a valuable toolbox for diverse applications. Mesostable PETases could be used in processes where high temperatures are not convenient. However, practical applications will most likely need the integration of waste plastic pretreatment processes (for example, micronization, amorphization, or others) and the use of engineered enzymes or whole cells. Several commercial polyesters underwent biodegradation studies [38; 39; 40]. However, to our knowledge, apart from PET, little is known about the biodegradation of aromatic polyesters. _Is_PETase has also shown some activity against a novel synthetic AB-type indole-base polyester [16]. In this work, _Is_PETase activity against various aromatic polyesters with terephthalate bound to diols of different lengths and rigidity is assessed. Semicrystalline forms of PET, PBT, PHT, and AkestraTM, which is amorphous, are used as potential substrates for _Is_PETase, and molecular docking is performed to explore the structural implications. On the other hand, the production of recombinant PETases is an efficient type of technology that can be scalable to industrial levels. However, one of the main drawbacks is the production of a soluble enzyme, and a significant fraction of _Is_PETase produced in _Escherichia coli_ can precipitate as inclusion bodies, losing their activity and negatively affecting the yield. A common strategy to enhance the solubility of recombinant proteins is the use of fusion partners, such as thioredoxin (Trx) [41]. Therefore, the Trx domain effect both in production and _Is_PETase (Trx-_Is_PETase) activity is also evaluated in this work. This article focuses on understanding the fundamental aspects of _Is_PETase and Trx-_Is_PETase activities; more practical aspects will be addressed in the future. The dissolution-precipitation of the polymers used here is not related to their recycling but with their preparation in a similar physical form, such as powders, for enzymatic studies. The pretreatment to reduce the degree of crystallinity, as mentioned before, of PET and other polyesters facilitate the enzyme activity, even for thermostable enzymes, which have the advantage of working closer to the PET glass transition point [23].
# Feature Selection Algorithms as One of the Python Data Analytical Tools +
## 2 Background
### Hybrid and Ensembling Feature Selection Algorithms  
Nowadays, most scientists distinguish separate groups of feature selection algorithms: hybrids and ensembles. Basically, these types of algorithms implement consecutive and parallel approaches to combine feature selection algorithms.  
Hybrid feature selection algorithms try to combine traditional approaches consecutively. This is a powerful compromise between different traditional approaches. For example, to select features from high-dimensional dataset, a filter as a first step can be applied to drastically reduce feature set and after that a wrapper can be applied to the output feature set to get maximum quality from the extracted features.  
Alternatively, ensemble feature selection algorithms combine several feature selection algorithms in parallel to improve their quality or even get better stability of selected feature subsets. Ensemble feature selection algorithms work either with separately selected features subsets or with the models, built on them [17].
## 2 Handcrafted Representation-Based Approach
### Space-Time-Based Approaches
#### 2.1.3 Space-Time Features  
The space-time features-based approaches extract features from space-time volumes or space-time trajectories for human action recognition. Generally, these features are local in nature and contain discriminative characteristics of an action. According to the nature of space-time volumes and trajectories, these features can be divided into two categories: sparse and dense. The features detectors that are based on interest point detectors such as Harris3D [30], and Dollar [69] are considered as sparse, while feature detectors based on optical flow are considered as dense. These interest point detectors provide the base for most of the recently proposed algorithms. In [70], the interest points were detected using Harris3D [30], based on these points they build the feature descriptor and used PCA (principal component analysis)-SVM for classification. In [59], authors proposed a novel local polynomial space-time descriptor based on optical flow for action representation.  
The most popular action representation methods in this category are based on the Bag-of-Visual-Words (BoVW) model [71; 72] or its variants [73; 74]. The BoVW model consists of four steps, feature extraction, codebook generation, encoding and pooling, and normalization. We extract the local features from the video; learn visual dictionary in training set by Gaussian Mixture Model (GMM) or K-mean clustering, encode and pool features, and finally represent the video as normalized pooled vectors followed by a generic classifier for action recognition. The high performance of the BoVW model is due to an effective low level feature such as dense trajectory features [48; 75], encoding methods such as Fisher Vector [74], and space-time co-occurrence descriptors [39]. The improved dense trajectory (iDT) [48] provides the best performance among the space-time features on serval public datasets.  
The coding methods have played an important role in boosting the performance of these approaches. Recently, a new encoding method named Stacked Fisher Vector (SFV) [52] was developed as an extension of traditional single layer Fisher Vector (FV) [74]. Unlike traditional FV, which encodes all local descriptors at once, SFV first performs encoding in dense sub-volumes, then compresses these sub-volumes into FVs, and finally applies another FV encoding based on the compressed sub-volumes. For the detail comparison of single layer FV and stacked FV readers are encouraged to refer [52].
## 6 Simulation Study  
### Diagnosis at Fixed Loads  
Figure 9 shows the variation of FSF for an induction motor at no load under four different DE levels at the frequencies of \(f_{r}\) and \(2f_{r}\). The DE increases the amplitude of the characteristic harmonic component at a frequency of 24.17 Hz from 0.11649% to 0.15382%. In addition, the other frequency of 48.34 Hz improves the amplitude of the characteristic harmonic component slightly from 0.23165% to 0.23996%. Table 1 shows the FSF and the amplitude of a healthy induction motor and a motor with 2%, 6% and 10% DE at a frequency of \(kf_{r}\) under no load. Therefore, the considerable increase at a frequency of \(kf_{r}\) can be treated as a suitable index for fault diagnosis. Figure 10 shows the characteristic signal of apparent power in time domain using the proposed method at no load under healthy and faulty (2%, 6% and 10% DE) conditions. The increasing oscillation in the signal confirms the faulty state of the induction motor. However, for fixed load, the harmonic component at the frequency of 24.17 Hz (\(k=1\)) fluctuates. Hence, the apparent power signal at a frequency of \(f_{r}\) may not give an accurate result to detect faulty conditions under load conditions. In this paper, the harmonic component at a frequency around \(2f_{r}\) (\(k=2\)) is the main indicator for fault diagnosis.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{\(f_{r}\)} & \multicolumn{2}{c}{\(2f_{r}\)} \\ \hline
**DE (\%)** & **FSF (\%)** & **Amplitudes (VA)** & **FSF (\%)** & **Amplitudes (VA)** \\ \hline
0 & 0.11649 & 5.859 & 0.23165 & 16.96 \\
2 & 0.12031 & 6.026 & 0.23523 & 17.11 \\
6 & 0.13425 & 6.476 & 0.23548 & 17.46 \\
10 & 0.15382 & 9.39 & 0.23996 & 17.76 \\ \hline \end{tabular}
\end{table}
Table 1: FSFs and the amplitudes of the induction motor under four levels of DE.
# Regional Adaptability Analysis of Solar Roof Utilization Technologies in China
## 2 Methodology
### The Solar Utilization Systems
#### 2.4.2 The Solar PV System  
The solar PV system mounted on the building roof is used to produce electricity, which can be used to cover the demand of the building or can be fed into the electrical grid. The application of solar PV technology is also influenced by its location, available solar radiation resource, the roof area, and the length-width ratio. Electricity may be stored or directly fed into the grid; thus, it is not influenced by the function of buildings. In general, the power generation of a solar PV system is proportion to the available solar radiation on the PV panel. In addition, the location of the building will influence the inclination, spacing, and area of PV panels. The total PV power generation can be roughly estimated by the following equation:  
\[E_{\rm PV}=J_{\rm t}\eta_{\rm PV}A_{\rm PV} \tag{2}\]  
In the equation, \(E_{\rm PV}\) is the energy generated, kWh; \(J_{\rm t}\) is the annual average solar radiation, kJ/m\({}^{2}\); \(\eta_{\rm PV}\) is the efficiency of the PV panel, %; and \(A_{\rm PV}\) is the PV panel area, m\({}^{2}\).  
In this study, according to the relevant code on the PV power station [43], the solar power generation was calculated using the software SAM (Solar Advisor Model). The PV modules of 166-60 c with an area of 1.8 m\({}^{2}\) each were chosen and arranged in arrays on the available area of the roof. The PV modules face south and are tilted according to the local latitude. The minimum distance between the front and the rear rows without shading on the winter solstice was calculated. The TMY weather data are adopted to estimate the PV annual power generation. It should be pointed out that future climate change could affect solar photovoltaic power by altering climate variables, such as global horizontal irradiance, surface wind speed, and surface air temperature [44]. The impact of future climate change is not considered in the investigations.