esa-sceva/satcom-chunk-collection huggingface dataset:
## Results  
### Microbial Isolates, Hydrolytic Properties, and SEM Observation  
The SEM analysis displayed the presence of fungi in various samples (Figure 2), in fact it is possible to note conidia, spores, and hyphae. The occurrence of bacteria in several samples is confirmed, indirectly, by the observation of biogenic crystals (Figure 2C,D).  
Bacteria were isolated from the samples a (bodice, from the ribbon), b (bodice, from the body), d (silk bobbin lace cap), and e (linen head-band). The most isolated genera were _Moraxella_, _Kocuria_, and _Paracoccus_. The only bacterial isolates able to hydrolyze all the tested substrates was _Microoccus yunnanensis_ B2. Various bacteria degrade four substrates; three isolates (the two _Moraxella_ isolates and _Paracoccus chinensis_) were negative to all assays. A slight cellulolytic activity was shown only by the two _Kocuria_ isolates and _Microoccus yunnanensis_ B2. The most effective keratinocyte isolate was _Paenibacillus illinoisensis_ D1 which displayed also an extensive protease ability together with _Microoccus yunnanensis_ B2. _Paenibacillus illinoisensis_ D1 evidenced also the best fibroinase activity followed by _Splingomonas_ sp. D2. Usually the isolates positive to proteolytic assays can degrade also the silk fibroin (Table 1).  
From sample c (bodice, tissue covering the stiff reinforcement) only filamentous fungi were isolated belonging mainly to genera _Aspergillus_ and _Penicillium_. The yeast _Sporidibolus metaroseus_ B2-F and _Penicillium commune_ B1-F, recovered from sample b, were the only eukaryotic microorganisms able to produce positive results for all the hydrolytic assays. Three fungi over seven isolates displayed cellulolytic ability, on the contrary all the fungal members have significant fibroinase property.  
Figure 2: Scanning electron microscope (SEM) observation of nitrocellulose membranes. (**A**) Conidia, (**B**) fungi colonizing insect remains, (**C**,**D**) biogenic crystals and their morphological details, (**E**) ascospores, (**F**) presence of fungal conidia and spores.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample** & **Strain Code** & **Percent Identity** & \begin{tabular}{c} **Keratinase** \\ **Activity (mm)** \\ \end{tabular} & \begin{tabular}{c} **Cellase** \\ **Activity (mm)** \\ \end{tabular} & \begin{tabular}{c} **Proteinase** 1** \\ **Activity (mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Proteinase** 2** \\ **Activity (mm)** \\ \end{tabular} &
\begin{tabular}{c} **Fibronase** \\ **Activity (mm)** \\ \end{tabular} \\ \hline \hline \multicolumn{7}{c}{**Bacteria**} \\ \hline \multirow{4}{*}{a, bodice} & A1 & MT071578 _Moravelle_ sp. 100\% & 0 & 0 & 0 & 0 & 0 \\  & A2 & CT024180 _Moravelle colonensis_ 100\% & 0 & 0 & 0 & 0 & 0 \\  & A3 & KY43463 _Kocurle_ 99.83\% & \(1\pm 0.3\) & \(1\pm 0.2\) & \(4\pm 0.6\) & \(2\pm 0.4\) & 0 \\  & A4 & MT07158 _Bakoorces_ sp. 100\% & 0 & 0 & \(2\pm 0.3\) & 0 & \(4\pm 1\) \\ \hline \multirow{2}{*}{b, bodice} & B1 & FF072311 _[PERSON] sp. 922\% & \(7\pm 0.8\) & 0 & \(7\pm 1.2\) & \(4\pm 1.2\) & \(10\pm 1\) \\  & B2 & KY816357 _Micro
## 5 Results and Discussion
### Effect of Plate Dimensions  
One of the determining factors that allow modifying the structural behavior is the thickness of the double-bottom floor. In accordance with this approach, the study of the influence of the thickness is established for each of the six different models, by means of three modifications of the thickness for each cutout geometry established above. Figure 15 shows the particular case of models 1, 3, and 6, for different cutouts (1, 2, 3, and 4) and different thickness values (14.5, 18, and 22) in millimeters, under axial load as a loading scenario.  
A common behavior is observed, whatever the model tested; the trend of the maximum von Mises stress decreases linearly with sequential increases in the thickness of the double-bottom floor, a behavior that can be extrapolated to determine the necessary increase in thickness to decrease the desired amount of von Mises stress. Regardless of the model, a clear improvement, translated into a lower stress level, is observed for cutouts no. 1 and no. 2 with respect to cutouts no. 3 and 4. The geometries relative to the double-bottom floor with the cutouts no. 1 and no. 2 present the biggest difference for the smallest thickness (14.5 mm) and of value 3.86, 3.76, and 4.16%, for the case of model no. 1, model no. 3, and model no. 6, respectively. Figure 16 shows the stress distribution as a function of the models at equal thickness (14.5 mm) and an axial loading scenario, for the case of cutout no. 1 and cutout no. 2.  
Figure 14: Distribution of Von Mises stress (MPa). (a) Cutout no. 1, (b) cutout no. 2.  
Figure 15: Von Mises maximum stress at modification of thickness. (**a**) Model no. 1, (**b**) model no. 3, (**c**) model no. 6.
## 4 Applications in Optimization
### Self-Organizing Migrating Algorithm (SOMA)
#### 4.4.4 Hybrid Chaos Immune Algorithm  
Liu et al. [94], seeking to improve the performance of the immune algorithm, integrated chaos optimization into the immune clone selection algorithm. After decoding, a portion of the antibodies are mapped onto the Lozi system, then every optical value is collected at each iteration. Those values are added into the new antibody group to improve the antibody-antigen fitness value. Comparative results indicate that the optimization method of the hybrid chaos immune algorithm with self-adaptive parameter adjustment has many advantages, such as better self-adaptive capacity, higher computation efficiency, and better design accuracy.
# Feasibility Study on Grouting Compactness Detection in Sleeves Using Piezoelectric Transducers
## 1 Introduction
The compactness detection results showed that the degree of grouting significantly affected the propagation of the stress wave; thus, the relationship between the two was established. And the effectiveness of the proposed PZT-based method was demonstrated.
## 3 Materials and Methods
### Fine-Tuning  
To exhibit the method's better performance and overcome its challenges, an advanced fine tuning technique is used, and this process is exhibited after the process of transfer learning where a fraction of CNN layers are unfrozen and the weights are made accessible to train along with features from the fully connected layers.  
These weights are trained subject to the required field of interest to make the pre-trained model more relevant to the real-time dataset collected. In [24], the authors unfroze all the required CNN layers of the pre-trained architecture and trained every weight using their specified dataset to distinguish interstitial lung diseases. This understanding shows the importance of the training and fine-tuning of a particular dataset. Experimental results from [25; 26] illustrate that the consistent usage of fine tuning has empowered the performance of pre-trained CNN models and produces better results with the same datasets. The successful usage of fine-tuning can be observed in [27; 28]. The details of the number of layers unfrozen and trained can be observed in Table 1. Unfreezing the complete neural network and training it on small-scale dataset will lead to overfitting the model. Considering the depth and complexity of the state-of-the-art models, we chose to unfreeze \(1/4\)th of the neural network and to train these layers on the collected dataset, whereas the frozen layers have the weights set on the Imagenet dataset.  
Total No. of layers--Total number of layers consisting of input, zero padding, convolutional, and fully connected layers. Unfrozen Layers--Each model is trained from this nth layer to the fully connected layer to predict the output.  
The proposed methodology begins with data collection and data augmentation of images related to goods carriers followed by data splitting into train, test, and validation sets. In the subsequent stage, transfer learning was implemented on the state-of-the-art DCNNs where the convolutional layers were frozen with ImageNet weights and only the classifier layers (i.e., fully connected layers) were trained on the collected dataset. Furthermore, fine-tuning was carried out by unfreezing the one-fourth portion of the neural network for the model training. Later, the best performing model was chosen based on different performance metrics. Figure 1 provides a visual overview of the proposed system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S. No.** & **Model** & **Tot. No. of Layers** & **Unfrozen Layers** \\ \hline
1 & MobileNetV2 & \(158\) & \(120\) \\ \hline
2 & ResNet50 & \(178\) & \(134\) \\ \hline
3 & VGG19 & \(25\) & \(18\) \\ \hline
4 & EfficientNetB0 & \(240\) & \(180\) \\ \hline
5 & EfficientNetB3 & \(387\) & \(290\) \\ \hline
6 & EfficientnetB4 & \(477\) & \(360\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fine-tuning of deep CNN models.
## 2 Motivation and Background
### Privacy Preserving Data Publishing vs. Privacy Preserving Data Mining  
Many studies about data privacy has been done in the database field under the name of \"privacy preserving data publishing\" [4]. The main aim is to provide privacy of data tables which are exchanged by other parties. At first glance, it may be assumed that privacy problem can be easily solved by stripping off the attributes which identifies individuals like name, social security number, _etc._ However, it is shown that it is possible to identify the owner of a record by using the residual data and other public information sources. This attack is called \"re-identification attack\" [5]. Re-identification attack is based on the assertion that some attributes, called quasi-identifiers, can easily help to identify the individuals although they do not uniquely identify them. Anonymity, which is defined as being not identifiable of an individual within set of individuals [6], is used as a privacy criterion in order to make data resistant to \"re-identification attacks\". \"\(k\)-anonymity\" brings a specific restriction to anonymity so that an individual is hidden among at least \(k\) other individuals [5]. Quasi-identifier attributes are generalized or suppressed in order to meet the requirements of anonymity. In a generalization operation, attribute is replaced by a more general one like replacement of birth data \"04.05.1977\" by 1977. One attribute or all attributes of a record are deleted in a suppression operation. These operations cause information loss so anonymity solutions intrinsically try to solve a trade-off between information loss and privacy. They try to cause minimum information loss while achieving the required level of privacy.  
Privacy preserving data mining is another major field in privacy studies [7]. Privacy preserving data mining techniques perturb the original data so that application of data mining techniques to perturbed data will give the same accurate mining results. However, the party that obtains perturbed data has no possibility to obtain the original data that may violate the privacy of data owners. Some attributes of data records may be modified with false data, they may be swapped among different records or totally artificially created new records can be inserted to original data sets at the data owner side.  
Privacy preserving data publishing techniques do not change the truthfulness of data at record level. They try to collect information as much as possible. They aim to provide data for widespread categories of data analysis tasks. In situations where the details of data analysis that would be performed by data collector party is not known in advance or there is a huge need for performing various types of analysis tasks at data collector side, data publishing techniques are preferred. The proposed framework in this paper is based on privacy preserving data publishing.
# Prediction of Cell-Penetrating Peptides Using a Novel HSIC-Based Multiview TSK Fuzzy System
## 2 Materials and Methods
### TSK Fuzzy System  
The TSK fuzzy system is a classic fuzzy model. Its input and output are nonfuzzy values, and it is highly flexible and interpretable. Therefore, we chose the TSK fuzzy system as the basic model. A fuzzy rule in TSK can be defined as follows:  
\[\begin{array}{c}\text{R}^{k}:\text{IF }x_{1}\text{ is }A_{1}^{k}\text{ }\wedge x_{2}\text{is }A_{2}^{k}\text{ }\wedge\ldots\wedge x_{\text{I}}\text{is }A_{D}^{k},\\ \text{THEN }y^{k}(\mathbf{x})=p_{0}^{k}+p_{1}^{k}x_{1}+p_{2}^{k}x_{2}\ldots+p_{D}^{k }x_{D},\text{ }k=1,2,\ldots,\text{ }K\end{array} \tag{1}\]  
The above TSK fuzzy system consists of K rules, and the input vector \(\mathbf{x}=[x_{1},x_{2},\ldots,x_{D}]^{\text{T}}\). \(A_{d}^{k}\) is a fuzzy set corresponding to the dth feature of the kth rule, \(y^{k}\) is the output of the kth rule, and \(p_{d}^{k}\) is the parameter. The membership function of the fuzzy set \(A_{d}^{k}\) is commonly represented by a Gaussian function:  
\[\mu_{A_{d}^{k}}(x_{d})=\exp\left(-\frac{\left(x_{d}-c_{d}^{k}\right)^{2}}{2 \sigma_{d}^{k}}\right) \tag{2}\]  
where \(c_{d}^{k}\) denotes the center and \(\sigma_{d}^{k}\) denotes the variance. In this paper, the fuzzy c-means (FCM) algorithm is employed to calculate \(c_{d}^{k}\) and \(\sigma_{d}^{k}\).  
\[c_{d}^{k}=\frac{\sum_{i=1}^{N}u_{ik}x_{id}}{\sum_{i=1}^{N}u_{ik}} \tag{3}\]  
\[\sigma_{d}^{k}=\frac{h\sum_{i=1}^{N}u_{ik}\Big{(}x_{id}-c_{d}^{k}\Big{)}^{2}}{ \sum_{i=1}^{N}u_{ik}} \tag{4}\]  
The output of the TSK fuzzy system is the combination of the results of each rule, which can be expressed as:  
\[\mathbf{y}(\mathbf{x})=\frac{\sum_{k=1}^{K}\mu^{k}(\mathbf{x})y^{k}(\mathbf{x })}{\sum_{k=1}^{K}\mu^{k}(\mathbf{x})}=\sum_{k=1}^{K}\widetilde{\mu}^{k}( \mathbf{x})y^{k}(\mathbf{x}) \tag{5}\]  
where  
\[\mu  
\[\mathbf{x}_{g}=\left(\mathbf{(\widetilde{x}^{-1})}^{T},\mathbf{(\widetilde{x}^{-k}) }^{T},\ldots,\mathbf{(\widetilde{x}^{-k})}^{T}\right)^{T} \tag{10}\]  
then  
\[\mathbf{p}^{k}=\left(p_{0}^{k},p_{1}^{k},\ldots,p_{D}^{k}\right)^{T} \tag{11}\]  
\[\mathbf{p}_{g}=\left(\left(\mathbf{p}^{1}\right)^{T},\mathbf{(p}^{2})^{T}, \ldots,\mathbf{(p}^{K})^{T}\right)^{T} \tag{12}\]  
\[\mathbf{y(x)=p}_{g}^{T}\mathbf{x}_{g} \tag{13}\]  
According to the above transformation, the TSK fuzzy system is transformed into a linear model. We employed the method of [PERSON] et al. [23] to solve model coefficients. The objective function is as follows:  
\[\underset{\mathbf{p}_{g}}{\min}I_{\text{TSK}}\mathbf{(p}_{g,c})=\frac{1}{2} \sum_{c=1}^{C}\mathbf{p}_{g,c}^{T}\mathbf{p}_{g,c}+\frac{\lambda_{\mathbf{p}_{ g}}}{2}\sum_{c=1}^{C}\sum_{i=1}^{N}\parallel y_{ic}-\mathbf{p}_{g,c}^{T} \mathbf{x}_{gi}\parallel^{2} \tag{14}\]  
Taking the derivative of the objective function with respect to \(\mathbf{p}_{g,c}\), the optimal solution of \(\mathbf{p}_{g,c}\) can be obtained:  
\[\mathbf{p}_{g,c}=\left(\mathbf{I}_{D\times D}+\sum_{i=1}^{N}\mathbf{x}_{gi} \mathbf{x}_{gi}^{T}\right)^{-1}\cdot\left(\lambda_{\mathbf{p}_{g}}\sum_{i=1}^ {N}\mathbf{x}_{gi}y_{ic}\right) \tag{15}\]
## 7 Validation and Results
### Named Entity Recognition Results  
For each split in the corpus (training, development, test) we carry out the following experiments:  
1. The BiLSTM-CRF base model proposed by [32] is used.
2. General domain embeddings training by Fast text ([[https://fasttext.cc/docs/en/pretrained-vectors.html](https://fasttext.cc/docs/en/pretrained-vectors.html)]([https://fasttext.cc/docs/en/pretrained-vectors.html](https://fasttext.cc/docs/en/pretrained-vectors.html))) on Wikipedia are added to the BiLSTM-CRF model.
3. Spanish medical embeddings proposed by [41] have been added to the BiLSTM-CRF model.
4. Spanish medical embeddings and char embeddings are added to the model.
5. A combination of medical embeddings, char embeddings, lemmas, and Part of Speech (POS) tagging features.  
Table 3 shows obtained results for every experiment previously described, and the partition (training, development, test) that obtained the best performance was chosen. According to this table, one can see that the best results are obtained in the fifth experiment. This fact indicates that the combination of medical embeddings, char embeddings, lemmas,  
and POS tagging features considerably improve cancer entity extraction. Moreover, the use of medical embeddings has the most significant impact on improving the model. This fact suggests that medical domain embeddings helps the neural network to learn to extract named entities. In addition, the use of lemmas and POS tagging features also have a considerable impact on the model, while adding char embeddings seems to have less impact on the final rate.  
On the other hand, (see Table 3), the use of Wikipedia general domain embeddings does not obtain such effective results. This fact suggests that using general domain embeddings to extract entities in specific and specialized domains such as the medical domain does not produce the best results.  
The proposed model's performance is similar to other proposals dealing with extracting cancer entities from clinical notes written in Spanish [45; 46; 47]. However, the main limitation with those proposals is that they are only able to extract cancer entities and do not consider other entities required to correctly extract the cancer diagnosis, such as dates, events, family members, or tumor stage.
## 2 Materials and Methods
### Method
#### 2.4.3 Auxiliary Edge Neck  
In mangrove remote sensing image semantic segmentation tasks, precise edge region identification is particularly crucial. This is because pixels within objects typically have consistent semantic features, while semantic discontinuities mainly occur at boundaries between adjacent objects. Particularly in this paper's mangrove dataset, boundary regions between mangroves and backgrounds often exhibit ambiguity and uncertainty. This phenomenon primarily stems from the special mechanism of wetland vegetation in remote sensing imaging: different types of vegetation may have similar backscattering character  
Figure 6: Overall Structure Diagram of ConvNeXt Feature Encoder.  
istics [38], making it difficult to distinguish mangroves from the surrounding vegetation at boundaries.  
To address this issue, this study proposes the Auxiliary Edge Neck (AEN). The AEN module directly uses the third layer features (\(\beta\)2) from the feature extractor as input, as this layer retains sufficient spatial details while containing certain semantic information. In terms of network structure, it adopts a lightweight fully convolutional network (FCN) design, processing features through two 3 \(\times\) 3 convolutional layers. Meanwhile, an auxiliary hybrid loss function is designed (see Section 2.5). The AEN computation process is shown in Formula (1):  
\[F=conv_{2}(conv_{1}(\beta_{2})) \tag{1}\]  
where \(conv_{1}\) uses a 3 \(\times\) 3 convolution operation with stride 1, outputting 256 channels, followed by the BatchNorm2d and ReLU activation function; \(conv_{2}\) is a 3 \(\times\) 3 convolution that maps feature map channels to the number of target classes (2 in this task, corresponding to edge and non-edge). The AEN structure is lightweight and efficient. During training, AEN loss is weighted and combined with the semantic decoder's loss, with a weight coefficient of 0.4, providing additional gradient information through this auxiliary supervision mechanism to enhance the feature extractor's ability to learn edge features.
## 3 RRP Description
### Virtual Topology Construction  
Virtual topology construction takes place right after the deployment of all sensor nodes. It is completed by flooding a circuit discovery packet (CDP) initiated from the BS. The process is similar to BDA in [9], but we use CDP in our protocol. A CDP contains a hops-traversed (HT) field and a BS-information (BSI) field in its header. The HT counts the number of hops it has traversed and the BSI records the BS location information. When a CH receives a CDP, it has to verify this CDP by checking the HT. If HT = 1, then the CDP does not travel through the GSs and contains no useful information. Such CDP will be discarded by the CH. Otherwise, the CH increases the HT by one, and appends its own information including the ID and location to CDP. Then, it forwards the CDP to its successors in the downlink case (HT = 1), or to the BS in the uplink case (HT > 1). Note that in the case of a downlink, the CDP arriving at CHs has not yet traveled the GSs layer, so the HT filed equals to 1 when the CDP is forwarded; but the situation is the opposite in an uplink case. When a GS receives a CDP, it records the BSI contained in the packet, increases the HT by one and then forwards the CDP to its successors. The CDP is considered to be expired if the HT is larger than a given constant \(\delta\). When a CDP returns to BS, the BS gets the nodes' location and extracts the path that is formed by the ID sequence in the CDP to create entries into a GlobalGraph (\(n\times n\)). Figure 5 shows the pseudo-code and the GlobalGraph of the FSOSN in Figure 2 after this phase.
# A Deep Learning Approach for Maximum Activity Links in D2D Communications
###### Abstract  
Mobile cellular communications are experiencing an exponential growth in traffic load on Long Term Evolution (LTE) eNode B (eNB) components. Such load can be significantly contained by directly sharing content among nearby users through device-to-device (D2D) communications, so that repeated downloads of the same data can be avoided as much as possible. Accordingly, for the purpose of improving the efficiency of content sharing and decreasing the load on the eNB, it is important to maximize the number of simultaneous D2D transmissions. Specially, maximizing the number of D2D links can not only improve spectrum and energy efficiency but can also reduce transmission delay. However, enabling maximum D2D links in a cellular network poses two major challenges. First, the interference between the D2D and cellular communications could critically affect their performance. Second, the minimum quality of service (QoS) requirement of cellular and D2D communication must be guaranteed. Therefore, a selection of active links is critical to gain the maximum number of D2D links. This can be formulated as a classical integer linear programming problem (link scheduling) that is known to be NP-hard. This paper proposes to obtain a set of network features via deep learning for solving this challenging problem. The idea is to optimize the D2D link schedule problem with a deep neural network (DNN). This makes a significant time reduction for delay-sensitive operations, since the computational overhead is mainly spent in the training process of the model. The simulation performed on a randomly generated link schedule problem showed that our algorithm is capable of finding satisfactory D2D link scheduling solutions by reducing computation time up to 90% without significantly affecting their accuracy.  
D2D communications; deep learning; link activation; wireless networks; integer programming +
Footnote †: journal: Sensors