esa-sceva/satcom-chunk-collection huggingface dataset:
# Bacteriotherapy in Breast Cancer
## 3 Anticancer Substances Released by Bacteria
### Bacterial Peptides in Cancer Therapy
#### 3.2.1 Ohmyungsamycins A and B  
New cyclic peptides, called Ohmyungsamycins A and B, are produced by Streptomyces sp., isolated from a volcanic island in the Republic of Korea. These cyclic peptides contain unusual amino acids in their structure, including N-methyl-4-methoxytytophan, hydroxyphenylalanine, and N, N-dimethylvaline. Both peptides show the anticancer activity, which could inhibit the growth  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Protein/Petide** & **Source** & **Breast Cancer Cell Line** & \begin{tabular}{c} **Other Human Cancer Cells/Cell** \\ **Lines** \\ \end{tabular} & **Ref** \\ \hline Ohmyungsamycins & & & & \begin{tabular}{c} diverse cancer cells, colon cancer \\ (HCT116), lung cancer cells \\ (A549), stomach cancer (SNU638), \\ liver cancer (SKHEPI) \\ \end{tabular} & \
\begin{tabular}{c} [50] \\ (51) \\ \end{tabular} \\ Azurin & _Pseudomonas aeruginosa_ & &
\begin{tabular}{c} MCF7, ZR-75-1, 147D, \\ MRA-MB-157, MDD2, \\ MDA-MB-231 \\ \end{tabular} & \  
of diverse cancerous cell lines and they also have antibacterial effects. Furthermore, these cyclic peptides show selective anti-proliferative activity against tumor cells as compared with normal cells. Ohmyungsamycins show the anticancer activity against human breast cancer cell line MDAMB231 [50].
# FTUC: A Flooding Tree Uneven Clustering Protocol for a Wireless Sensor Network
## 6 Simulation and Analysis  
In this section, we provide several simulation experiments to compare FTUC with the classical WSN LEACH [14] and UCR [12] routing algorithms. We first start by evaluating the parameters of our algorithm to find their optimal values, and we then make several comparisons with state-of-art algorithms.  
In order to achieve a higher level of confidence, we conducted 20 experiments in each scenario. The network life times (LT-1 and LT-2) and percentage of alive sensor nodes were used as evaluation standards for WSN longevity.  
We used the Cupcarbon tool [22] for our simulations. CupCarbon is an open-source simulator specifically designed for Smart City and Internet of Things Wireless Sensor Network (SCI-WSN) evaluations. It enables a visual analysis of WSN and permits a fast evaluation of new approaches. For the experiments, we assume a WSN with N = 100 sensor nodes, distributed uniformly in an M = 200 m, and the base station is the center of the sensing field (coordinate (0,0)). Each sensor monitors its environment once, sends 1-bit data every 10 s, and has \(\mathrm{E_{init}}\) = 100 Joules of initial energy. The transmission range of each sensor is set to be \(\mathrm{R_{c}}=\mathrm{M}=200\) m. This value has been chosen to make fair comparisons with the LEACH and the UCR algorithms that require direct communications between each node and the BS.  
The technological parameters used for all the WSN are resumed in Table 1; these values have been chosen as they are used in the literature [14].  
The Cupcarbon simulation result of FTUC is shown in Figure 4. As explained above, a tree network is first built, and CHs are elected among the nodes. As can be seen, the fCHs (in red on the figure) are uniformly distributed near the reference position circles. The remaining nodes then choose a cluster (represented by a same color in the figure). Smaller clusters are created near the BS and bigger ones are built further away from the BS.
## 1 Introduction
### 3.2. 3b-Photopolymerization of Renewable Monomers
\begin{table}
\begin{tabular}{|c|c|c|} \hline
**Monomers** & **Rp/[M\({}_{\rm{g}}\)]\({}^{\rm{*}}\)(s\({}^{\rm{-}}\))** & **Conversion** \\ \hline LDO \({}^{\rm{a}}\) & 0.016 & 71.4\% \\ ESO \({}^{\rm{a}}\) & 0.002 & 21.5\% \\ ELO \({}^{\rm{a}}\) & 0.0002 & 7.0\% \\ \hline EFA \({}^{\rm{a}}\) & 0.001 & 9.0\% \\ LDO \({}^{\rm{b}}\) & 0.05 & 81.4\% \\ ESO \({}^{\rm{b}}\) & 0.006 & 43.1\% \\ \hline ELO \({}^{\rm{b}}\) & 0.0006 & 29.7\% \\ EFA \({}^{\rm{b}}\) & 0.001 & 29.0\% \\ \hline \end{tabular} \({}^{\rm{a}}\) in absence of silane; \({}^{\rm{b}}\) in presence of silane; \({}^{\rm{c}}\) [M\({}_{\rm{g}}\)] is the initial monomer concentration.  
\end{table} 
Table 1: Polymerization rates and final conversions of different renewable epoxides using BAPO/Ph\({}_{2}\)I\({}^{\rm{+}}\) (1%/2% _w/w_) as the photoinitiating system in the absence (**a**) or in the presence (**b**) of _tris_(trimethylsilyl)silane (TTMSS) (3% _w/w_); under air; Xenon lamp irradiation (\(\lambda>400\) nm). Figure 12: IR spectra recorded in the course of a photopolymerization of ELO; initiating system: BPSK/Ph\({}_{2}\)I\({}^{\rm{+}}\)/TTMSS (1%/2%/3% _w/w_): (**A**) fluorescent bulb irradiation (from \(t=0\) to 30 min) and (**B**) sunlight exposure (from \(t=0\) to 30 min); under air. \begin{table}
\begin{tabular}{|c|c|} \hline
**Monomer** & **Tackfree** \\ \hline LDO \({}^{\rm{a}}\) & 48 min \\ \hline ESO \({}^{\rm{a}}\) & \(*\) \\ \hline ELO \({}^{\rm{a}}\) & 50 min \\ \hline EFA \({}^{\rm{a}}\) & \(*\) \\ \hline LDO \({}^{\rm{b}}\) & 12 min \\ \hline ESO \({}^{\rm{b}}\) & 45 min \\ \hline ELO \({}^{\rm{b}}\) & 9 min \\ \hline EFA \({}^{\rm{b}}\) & \(*\) \\ \hline \end{tabular} \({}^{\rm{a}}\) in absence of silane; \({}^{\rm{b}}\) in presence of silane; \({}^{\rm{*}}\): no tack-free coating after 1 h.  
\end{table} 
Table 2: Irradiation times to get tack-free coatings using BAPO/Ph\({}_{2}\)I\({}^{\rm{+}}\) (1%/2% _w/w_) in the absence (**a**) or in the presence (**b**) of _tris_(trimethylsilyl)silane (TTMSS) (3% _w/w_) upon sunlight exposure; under air.
# Improving the Robustness of AI-Based Malware Detection Using Adversarial Machine Learning
## 2 Theoretical Background
### Need for Artificial Intelligence  
Malware detection systems based on signatures work flawlessly when the malware is previously known and has been detected by some other antivirus vendors. However, such a system is practically useless in polymorphic malwares, as they would go undetected because such malware can change their signatures. So generally, malware detection depends on the heuristic-based approach for such kinds of mal  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Static Analysis** & **Dynamic Analysis** \\ \hline Signature-based malware detection & Behavior-based malware detection \\ Simple and Straightforward analysis & Thorough analysis \\ Best suited to detect common malware & Can detect advanced malware \\ It is fast and safe & It is time-consuming and vulnerable \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between static analysis and dynamic analysis.  
ware. However, it is not very efficient in adequately detecting malware, as it gives many false positives and false negatives.  
Now, the spread of such polymorphic malware has overwhelmed the traditional heuristic-based approach. One solution to this problem is to amalgamate the heuristic-based approach and the powers of machine learning methods [29] to yield higher accuracy during the detection.
# Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities
## 2 Recommendation System Categories
### Collaborative-Filtering Recommendation Systems  
Collaborative Filtering evaluates products using users' ratings (explicit or implicit) from historical data [16]. It works by developing a database of the user's preferences for items. Active users will be mapped against this database to reveal the active user's neighbors with similar purchase preferences. Collaborative filtering techniques are classified into item-based filtering and user-based filtering [17]. User-based techniques go through two main stages to forecast items' ratings for a specific user. The first stage locates similar users to the target user. The second stage obtains rates from similar users to the active user, then using them to produce recommendations. There have been many collaborative filtering algorithm measures that calculate the similarities among users. The commonly used similarity measures in the literature include mean-squared difference, Pearson correlation, cosine similarity, Spearman correlation, and adjusted cosine similarity [18; 19]. Collaborative filtering is the commonly used choice for RSs, and it does not require domain knowledge because the embeddings are automatically learned. Embedding of items in a recommender system refers to mapping items to a sequence of numbers. This way of representing items with learned vectors, is used to train algorithms to find the relationship between items and extract their features. Next, an advantage of collaborative filtering is that it generates models that help users discover new interests. Finally, collaborative filtering is a great starting point for other RSs, as the RS only requires the rating matrix R to develop a factorization model. The rating matrix R is a two-dimensional matrix of n users and m items; each entry in this matrix, rij represents the rating provided of user i to item j. Although being favorable in many aspects, collaborative filtering has several disadvantages, such as the cold-start problem, which we will elaborate on in Section 3.1 of this paper.
# Experimental Study of Wave-Induced Response of Piles in Seabed with Various Permeability
## 4 Discussion of Wave-Induced Pile-Seabed Response
### Pile-Cross-Section Effect  
This paper also studied the effectiveness of fins attached to piles in different seabed types. The results of pore pressure in Sample 1 seabed with a finned pile can be seen in Figure 10. Compared with the results from T2(f-\(H8\)-\(D3\)p) with a normal pile, the application of fins in T4(f-\(H8\)-\(D3\)p-fin) led to a decline in \(p_{\mathrm{m}}\) by 6.4 %, 10.4 % and 4.1 % at \(z=5\) cm, 15 cm and 25 cm, respectively, which was especially significant at locations adjoining fin locations (see \(z=15\) cm in T4(f-\(H8\)-\(D3\)p-fin)). Fins contributed more lateral resistance by mobilizing more surrounding soil, along with small pile displacement and pore pressure.  
The effect of fins on the responses of pore pressure in Sample 2 seabed can be seen in Figure 11. As the depth increased from 5 cm to 25 cm, the residual pore pressure (\(p_{\mathrm{r}}\)) of the soil around the finned pile in T10(s-\(H8\)-\(D3\)p-fin) rose similarly to that around the normal pile in T8(s-\(H8\)-\(D3\)p), but its peak value was smaller, e.g., 45.6% lower at \(z=25\) cm in T10(s-\(H8\)-\(D3\)p-fin). Sample 2 seabed with higher relative density, which might be critical for fin application, allowed the fins to acquire more support from the subgrade and to stop surrounding soil from compacting. Figure 15 presents the oscillating pore pressure in T10(s-\(H8\)-\(D3\)p-fin) (\(p_{\mathrm{o}}\)) decreasing with depth, especially in the stable response period, e.g., the mean values of amplitude at \(z=5\) cm were about four times those at \(z=15\) cm in the first 400 s and tended to rapidly develop with loading time when Sample 2 soil was close to liquefaction. Note that the amplitude of \(p_{\mathrm{r}}\) at \(z=15\) cm was even greater than the wave pressure at the seabed surface (see Figure 11b); this is because the constitution and structure of local soil had rebuilt and its particles had moved in response to periodic seepage. This alternation affected the permeability of the local seabed and resulted in a sharp secondary increase in \(p_{\mathrm{r}}\).  
The pile-head displacement of the finned pile in Sample 1 seabed (T4(f-\(H8\)-\(D3\)p-fin)) can be seen in Figure 12a. The permanent displacement of the finned pile developed much more slowly with a small peak value. Combined with the pore-pressure measurements, this result demonstrates that sufficient lateral resistance was mobilized by using fins and that the lateral stiffness of the pile in Sample 1 seabed was nearly unvaried.  
Figure 13a gives the permanent displacement of the finned piles embedded in Sample 2 seabed (see T10(s-\(H8\)-\(D3\)p-fin)). Related to pore pressure, the reduction in lateral stiffness leading to poor efficiency of pile-seabed interactions was limited in Sample 2 seabed. Compared with normal piles, fin application is a very effective method for controlling pile response and sufficient lateral support from the seabed.
# Light-YOLO: A Study of a Lightweight YOLOv8n-Based Method for Underwater Fishing Net Detection
## 4 Experimental  
### Fishing Net Vulnerability Dataset Collection and Construction  
#### 4.1.1 Expansion of the Fishing Net Vulnerability Dataset  
This study used a dataset obtained from underwater camera shots taken in a laboratory pool. The small number of samples in the annotated dataset will significantly limit the model's ability to achieve the desired detection performance if not properly processed. In addition, due to the scarcity of image resources available for learning, the model is prone to overfitting, leading to an increase in the gap in the generalization ability between the training loss and the validation loss, as well as limiting the effective scaling of the model and performance improvement.  
To solve this problem, the generative adversarial network (GAN [25]) is introduced into the model. The GAN network consists of two sub-modules: one is a generator, and the other is a discriminator, which improves their respective performances by confronting each other during the training process. The schematic diagram of the GAN network principle is shown in Figure 6 below.  
Generator G receives random noise vectors and outputs a realistic fishing net hole image after adversarial training; discriminator D inputs the original image and the generated fishing net hole image and recognizes their authenticity. In the training process, random noise is first fed into generator G to generate time-frequency map samples. These samples generated by G are then fed into the discriminator D along with the actual time-frequency maps for verification. The entire system operates in an adversarial training framework. As training progresses, the generator G is optimized and learns to generate samples closer to the real ones. At the same time, the discriminator D enhances its ability to distinguish between real and fake samples. The training goal of the GAN network is to complete the training by minimizing the sum of the loss functions of G and D and continuously updating the parameters of the two networks. The detailed parameters of our trained GAN network are shown in Table 1.  
Figure 6: Schematic diagram of the GAN network principle.
## 3 Results and Discussion
### Recognition Performance of Each SCR System
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Accuracy (\%)**} \\ \hline  & \multicolumn{2}{c}{**Convolution Neural**} & \multicolumn{2}{c}{**Convolution Neural**} & \\  & \multicolumn{2}{c}{**Network with**} & \multicolumn{2}{c}{**Network with a Phonetic**} & \multicolumn{2}{c}{**Automatic Speech**} \\  & \multicolumn{2}{c}{**Mel-frequency Cepstral**} & \multicolumn{2}{c}{**Posteriorogram**} & \multicolumn{2}{c}{**Recognition (ASR)**} \\  & \multicolumn{2}{c}{**Coefficient (CNN-MFCC)**} & \multicolumn{2}{c}{**(CNN-PPG)**} & \\ \hline \multirow{2}{*}{**Times**} & **Training** & **Application** & **Training** & **Application** & **Training** & **Application** \\  & **Phase** & **Phase** & **Phase** & **Phase** & **Phase** & **Phase** \\ \hline
1 & 97.9\% & 57.9\% & 95.4\% & 95.3\% & 100\% & 89.5\% \\ \hline
2 & 98.2\% & 67.3\% & 95.7\% & 94.2\% & 100\% & 94.2\% \\ \hline
3 & 98.2\% & 63.7\% & 93.2\% & 95.3\% & 100\% & 89.5\% \\ \hline
4 & 97.9\% & 67.8\% & 96.7\% & 96.5\% & 100\% & 74.9\% \\ \hline
5 & 96.7\% & 69.6\% & 95.7\% & 93.6\% & 100\% & 87.1\% \\ \hline
6 & 92.2\% & 71.9\% & 96.9\% & 92.9\% & 100\% & 94.2\% \\ \hline
7 & 95.9\% & 64.9\% & 95.2\% & 90.0\% & 99.7\% & 94.2\% \\ \hline
8 & 99.2\% & 64.9\% & 98.9\% & 90.0\% & 100\% & 88.9\% \\ \hline
9 & 97.9\% & 62.0\% & 97.2\% & 91.2\% & 100\% & 95.2\% \\ \hline
10 & 96.7\% & 66.7\% & 96.2\% & 95.9\% & 100\% & 88.9\% \\ \hline
**Average** & **97.1\%** & **65.7\%** & **96.1\%** & **93.4\%** & **99.9\%** & **89.6\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the 10 repeated experiments for each of the three systems. Figure 4: The average speech recognition rate (%) of CNN–MFCC, CNN–PPG, and ASR-based SCR systems in a test repeated 10 times. The number of parameters of the system used is related to the implementation costs, such as computation consumption and memory size; meanwhile, a large model size (i.e., higher parameter numbers) causes longer response time and has battery lifetime issues [69, 70]. Therefore, under a similar recognition performance condition, a smaller model size could provide more benefits for users. The experimental results show that the CNN-PPG can provide a higher recognition rate than the other two SCR systems with a lower model size than the ASR-based system. Hence, these results suggest that the CNN-PPG system can provide practical feasibility in the future.
# Local Data Debiasing for Fairness Based on Generative Adversarial Training
## 1 Introduction
In this sense, our objective is more generic than simply building a non-discriminating classifier, in the sense that we aim at debiasing the data with respect to the sensitive attribute. * Another strength of our approach is that once the sanitizer has been learned, it can be used locally by an individual (e.g., on a device under their control) to generate a modified version of their profile that still lives in the same representation space, but from which it is very difficult to infer the sensitive attribute. In this sense, our method can be considered to fall under the category of _randomized response techniques_[6] as it can be distributed before being used locally by a user to sanitize their data. Thus, it does not require their true profile to be sent to a trusted third party. Of all of the approaches that currently exist in the literature to reach algorithmic fairness [7], we are not aware of any other work that has considered the local sanitization with the exception of [8], which focuses on the protection of privacy but could also be applied to enhance fairness. * To demonstrate its usefulness, we have proposed and discussed four different evaluation scenarios and assessed our approach on real datasets for these four different scenarios. In particular, we carried out an empirical analysis of our approach to explain the behaviour of the sanitization process. In particular, we have analyzed the achievable trade-off between fairness and utility measured both in terms of the perturbations introduced by the sanitization framework but also with respect to the accuracy of a classifier learned on the sanitized data. However, we want to emphasize that in contrast to most of the previous works, once the dataset is sanitized it could be used for any other analysis tasks. The outline of the paper is as follows. First, in Section 2, we introduce the system model before reviewing the background notions on fairness metrics.Afterward, in Section 3, we review the related work on methods for enhancing fairness belonging to the preprocessing approach like ours before describing GANSan in Section 4. Finally, we evaluate experimentally our approach in Section 5 before concluding in Section 6.
# Study on Underwater Target Tracking Technology Based on an LSTM-Kalman Filtering Method
## 3 Simulation
### Model Training  
#### 3.2.1 Target Detection Model  
The purpose of the target detection model was to confirm the tracking value of the target to be detected and judge whether to start the target tracking model according to the detection results of the CNN model. We could obtain the type and confidence score of the detected target after the model training. When the confidence score was greater than or equal to 0.85, it was considered that the target had tracking value and the Kalman filter started to work. Figure 6 presents the flowchart of the target detection model.  
The time-frequency map samples that were obtained in Section 3.1.1 and the corresponding target type were used as of the inputs and output, respectively. The dataset was input into the convolutional neural network (CNN) model for training. By properly adjusting the model parameters, the target recognition accuracy was considerable. When training the model, 70% of the data were used as training samples and 30% were test samples. Figure 7 shows the final model parameters.  
Figure 5: Partial training set of samples for the target detection model.  
The CNN was a five-layer network structure. The activation function used the \"ReLU\" function, the training optimizer was Adam, the loss function was the multi-classification loss function category \"cross-entropy\", and the training batch size was 64.  
The accuracy and loss trends of the CNN target detection model after 100 epochs of training are shown in Figure 8.  
The training results showed that after 100 epochs, the accuracy rose to 0.998 and the loss approached 0. Therefore, the CNN model could effectively learn the features of the target echo signals using many tests. The accuracy of target detection was sufficient for use in subsequent active underwater acoustic target tracking.  
Figure 6: The flowchart of the target detection model.  
Figure 7: Network structure of the target detection model.
# Boundary-Aware Hashing for Hamming Space Retrieval
## 3 Boundary-Aware Hashing
### Objective Function  
The boundary-aware penalization of the proposed BAH model in Equation (1) is a standard NP-hard problem [35]. It can be solved by employing the approximate relationship between effective continuous codes metric and discrete Hamming distance, i.e., continuous relaxation. Since the cosine distance measures each pair of continuous codes on a unit sphere and the discrete Hamming distance \(d\left(b_{i},b_{j}\right)\) measures each pair of continuous codes on a unit hypercube, the cosine distance on continuous codes is always the upper bound of the discrete Hamming distance on hash codes [7; 36]. Ideally, the cosine distance is equivalent to the Hamming distance. This paper adopts the cosine distance to approximate this relationship:  
\[\begin{array}{c}d\left(b_{i},b_{j}\right)=\frac{1}{2}\left(K-<b_{i},b_{j}> \right)=\frac{K}{2}\left(1-\frac{<b_{i},b_{j}>}{K}\right)\\ =\frac{K}{2}\left(1-\frac{<b_{i},b_{j}>}{\|b_{i}\|\|b_{j}\|}\right)\approx\frac {K}{2}\left(1-\cos\left(u_{i},u_{j}\right)\right)\end{array}, \tag{2}\]  
where \(u_{i}\) is continuous codes for each image \(x_{i}\), \(b_{i}\) is the hash codes corresponding to the continuous codes \(u_{i}\), \(K\) is the code length, \(||b_{i}||=||b_{j}||=\sqrt{K}\), and \(||\cdot||\) is \(l_{2}\) norm of a vector. To control the quantization error of the continuous relaxation and bridges the gap between cosine distance and Hamming distance, BAH adds a regularizer to minimize the difference between \(u_{i}\) and \(b_{i}\) with the \(l_{2}\) loss:  
\[Q=\sum_{i=1}^{n}\|u_{i}-b_{i}\|_{2}^{2}. \tag{3}\]  
We jointly optimize boundary-aware penalization \(L\) in Equation (1) and the \(l_{2}\)-norm regularizer \(Q\) in Equation (3), forming the objective function of the proposed BAH as  
\[\min_{\Theta}L+\alpha Q, \tag{4}\]  
where \(\Theta\) represents the network parameter sets to be backpropagated, and \(\alpha\) is a hyper-parameter to trade off between \(L\) and \(Q\).  
During the testing process, given a query \(x_{i}\), the BAH model can achieve its continuous values \(u_{i}\) directly through forwarding propagation, and then it obtains the hash codes \(b_{i}\) through a sign function, as follows:  
\[b_{i}=\text{sgn}(u_{i}), \tag{5}\]  
where \(\text{sgn}(\cdot)\) is the element-wise sign function, \(\text{sgn}(u_{i})=1\) if \(u_{i}>0\), otherwise \(\text{sgn}(u_{i})=-1\)