esa-sceva/satcom-chunk-collection huggingface dataset:
# Coastal Sargassum Level Estimation from Smartphone Pictures
## 5 Conclusions  
A study for Sargassum level classification in public photographs has been presented. Several networks have been tested using different training strategies. The best results were obtained by the VGG16 model trained under fine-tuning. The best results show an accuracy of 64%. When analyzing the performance of the model with respect to each class, it was unexpected that the best behavior occurred in the excessive class, since it is one of the minority classes in the dataset, with 82% correctly classified images. This may be an indication that the model correctly extracts the features in this particular type of images. Class imbalance is another factor that may be affecting the performance of all models, so it is necessary to obtain more images of minority classes or apply class balancing methods to verify this assumption. We also hypothesize that the limited accuracy achieved by the models is bias because the levels of Sargassum in each image were assigned using human criteria. In consequence, a discrepancy between labels can be found. A stricter way for label assignment could increase the performance of the models. We hope that our system can offer a tool for Sargassum monitoring. So that, scientists, ecologists, and authorities can have a better understanding of the phenomenon. As a result, cleaning efforts and studies can be directed to critical areas. Our next research direction is to segment the Sargassum from the images and to include image geo-localization.  
Conceptualization and Investigation, J.I.V.; Investigation, J.I.V. and A.G.-F.; Data Curation and Software, E.V.-M.; Writing--original draft, A.V.U.-A. and H.T. All authors have read and agreed to the published version of the manuscript.  
Concary _Citedras_ Project: 1507.  
Not applicable.  
All gathered data are open access and available via Kaggle: [[https://www.kaggle.com/datasets/irvingvasquez/publicsargazods](https://www.kaggle.com/datasets/irvingvasquez/publicsargazods)]([https://www.kaggle.com/datasets/irvingvasquez/publicsargazods](https://www.kaggle.com/datasets/irvingvasquez/publicsargazods)), (accessed on 15 September 2022). The dataset will be continuously updated as new data are obtained.  
The authors thank to the following people who have recollected images from public forums: [PERSON], [PERSON], and [PERSON]. The authors would like to thank the Instituto Politecnico Nacional (Secretaria Academica, Comision de Operacion y Fomento de Actividades Academicas, Secretaria de Investigacion y Posgrado, Centro de Innovacion y Desarrollo Tecnologico en Computo), Consejo Nacional de Ciencia y Tecnologia, and Sistema Nacional de Investigadores for their support in developing this work.  
The authors declare no conflict of interest.  
Figure 4: Examples of Sargassum level classification using the selected convolutional neural network. Displayed images are already pre-processed: normalization and resize. The label for each example indicates: ‘predicted label/true label’.
## 3 Results
Figure 6 shows the percentage of deviations (%) and absolute changes (in \(\upmu\)m) in stone cast dimensions from those of the typodont master cast for the three tested groups. The ADA specification No. 19 for elastomeric type I impressions allows a maximum contraction of 0.5% after 24 h [20]. Thus, a clinically significant distortion could be considered as any dimensional change (contraction or expansion) greater than 0.5 percent. For stone casts made using the ALG impression material, Figure 6 shows that the HAPC and HCA measurements did not exceed 0.5 percent in dimensional change. For stone casts made using both AA and PVS-2 impression materials, the only measurement that exceeded 0.5 percent in dimensional change was the V measurement. However, the greatest number of distortions above 0.5 percent (at all dimensional locations) was produced by the digital models. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Pairwise Comparison** & **Mean (SD)** & **Mean Difference** & \(p\)**Value \({}^{*}\)** \\ \hline \multirow{6}{*}{HAPS} & ALG vs. AA & 14.91 (0.08) & \multirow{2}{*}{0.13} & \multirow{2}{*}{<0.001 \({}^{*}\)} \\  & 14.78 (0.04) & & \\ \cline{2-3}  & ALG vs. PVS-2 & 14.91 (0.08) & & \\ \cline{2-3}  & ALG vs. DIGITAL & 14.84 (0.05) & & \\ \cline{2-3}  & ALG vs. DIGITAL & 14.91 (0.08) & & \\ \cline{2-3}  & ALG vs. DIGITAL & 15.14 (0.05) & & \\ \cline{2-3}  & ALG vs. DIGITAL & 14.78 (0.04) & & \\ \cline{2-3}  & ALG vs. DIGITAL & 15.14 (0.05) & & \\ \cline{2-3}  & PVS-2 vs. DIGITAL & 14.84 (0.05) & & \\ \hline \multirow{6}{*}{HAPC} & ALG vs. AA & 40.67 (0.18) & \multirow{2}{*}{\(-0.08\)} & \multirow{2}{*}{0.359} \\  & 40.75 (0.08) & & \\ \cline{2-3}  & ALG vs. PVS-2 & 40.67 (0.18) & & \\ \cline{2-3}  & ALG vs. DIGITAL & 40.74 (0.09) & & \\ \cline{2-3}  & ALG vs. DIGITAL & 40.74 (0.09) & & \\ \cline{2-3}  & PVS-2 vs. DIGITAL & 41.11 (0.08) & & \\ \hline \multirow{6}{*}{HCA} & ALG vs. AA & 44.40 (0.11) & \multirow{2}{*}{\(-0.34\)} & \multirow{2}{*}{<0.001 \({}^{*}\)} \\  & ALG vs. PVS-2 & 44.64 (0.07) & & \\ \cline{2-3}  & ALG vs. DIGITAL & 44.40 (0.11) & & \\ \cline{2-3}  & ALG vs. DA & 44.90 (0.07) & & \\ \cline{2-3}  & ALG vs. DIGITAL & 44.40 (0.11) & & \\ \cline{2-3}  & ALG vs. DA & 44.90 (0.07) & & \\ \cline{2-3}  & ALG vs. DA & 44.74 (0.07) & & \\ \cline{2-3}  & ALG vs. DA & 44.74 (0.07) & & \\ \cline{2-3}  & ALG vs. DA & 44.90 (0.07) & & \\ \cline{2-3}  & ALG vs. DA & 44.74 (0.07) & & \\ \cline{2-3}  & ALG vs. DA & 44.74 (0.07) & & \\ \cline{2-3}  & ALG vs. DA & 44.90 (0.07) & & \\ \hline \end{tabular}
\end{table} 
Table 4: Multiple pairwise comparisons of the accuracy of the three impression materials and digital impression using Tukey’s HSD Test.
# Current Promising Strategies against Antibiotic-Resistant Bacterial Infections
## 4 Developing Alternatives to Antibiotics
### Anti-Biofilm Drugs  
Biofilms are a well-known problem in the treatment of bacterial infections. They profoundly hinder the penetration of antibiotics and favor the development of resistance. Thus, biofilm-related infections are impossible to treat with conventional antibiotics. The attachment of bacteria to a surface is the first step in the formation of biofilms. Targeting the initial attachment can be a reasonable strategy to prevent biofilm formation. Manososides are small molecules that target FimH, an adhesion protein of type 1 pili in _E. coli_[100]. The administration of monomeric biphenyl mannosides prevented the formation  
Figure 4: Alternatives to antibiotics.  
of uropathogenic _E. coli_ biofilm in vitro and blocked the adherence and invasion of this bacteria in animal models [101]. Antigen 43 and curli fibers are two additional types of adhesion molecules mediating the attachment of bacteria on biotic and abiotic surfaces [102]. Two curlicides (FN075 and BibC6) derived from ring-fused 2-pyridones have been developed to inhibit the biosynthesis of curli fibers needed for biofilm formation [103]. Peptides with anti-biofilm activity (anti-biofilm peptides) are new solutions for biofilm inhibition. Anti-biofilm peptides IDR-1018, DJK-5, and DJK-6 inhibited biofilm formation in multiple GNBPs and GPBPs by binding and degrading guanosine tetrapphosphate (ppGpp), which is one of the second messenger nucleotides that sense nutrient starvations and contribute to the antibiotic resistance and biofilm formation [104; 105]. In addition, the phage-encoded depolymerases have anti-biofilm activity, as demonstrated by their ability to degrade extracellular polymers and related biofilm components [106]. A depolymerase from \(\varphi\)AB6 phage digested capsular polysaccharide, inhibited the colonization of _A. baumannii_ on the surface of medical devices, and suppressed _A. baumannii_ infection in a zebrafish model [107]. Given the specific recognition of bacterial surface components by aptamers, utilizing aptamers as anti-biofilm agents is possible through the specific binding to certain constituents of the bacterial surface. For instance, three DNA aptamers were identified to bind with the flagellum, a crucial motile component that mediates tropism and initial attachment in the process of biofilm formation, with aptamer 3, having the best anti-biofilm activity [108]. Moreover, using aptamers that bind to the common constituents of both bacterial surface and biofilm is another anti-biofilm strategy. The six DNA aptamers that interacted with _S. aureus_ biofilm were demonstrated to assist in the eradication of _S. aureus_ biofilm when combined with liposomal delivery of vancomycin and rifampicin [109].
# A More Efficient Conditional Private Preservation Scheme in Vehicular Ad Hoc Networks
## 2 Signature Security  
The security of OBU's signature \(Sig_{M}\) is based on the discrete logarithm assumption. it is not feasible to output a forgery in polynomial time, which makes the scheme resistive to the impersonation attack and the bogus message spoofing attack.  
Lemma 2: _If the discrete logarithm assumption holds, then the signature is secure against existential forgery under an adaptively chosen message attack._  
Proof of Lemma 2: We suppose that \(A\) which is an adversary taking message \(M\) and public key \(Y\) as input has a non-negligible probability to output an existential forgery in polynomial time. Then, \(A\) can get two forgeries for the same message according to the forking lemma [28]. Let \(Sig_{M}=(R,s_{1})\) and \(Sig_{M}^{\prime}=(R,s_{2})\) are the two signature forgeries, respectively, where \(R=g_{1}^{r},s_{1}=r+x\cdot h(M,R)\) mod \(p\) and \(s_{2}=r+x\cdot h^{\prime}(M,R)\) mod \(p\). Then, we have the following equation.  
\[s_{1}-s_{2}=x(h(M,R)-h^{\prime}(M,R))\mod p \tag{10}\]  
Hence  
\[x=(s_{1}-s_{2})(h(M,R)-h^{\prime}(M,R))^{-1}\mod p \tag{11}\]  
As can be seen from the above, \(x\) can be computed successfully, but it contradicts the discrete logarithm assumption. Therefore, \(Sig_{M}\) is difficult to be forged.
# A Graph Neural Network Node Classification Application Model with Enhanced Node Association
## 4 Data Preparation  
### Dataset Selection  
This experiment selected the publicly available Cora [36] citation network dataset and the stock dataset constructed by crawling the stock prospectus of China Eastern Wealth Network's STB by itself for the experimental dataset selection.  
Cora dataset: At this time, the Cora dataset is one of the most popular and convincing datasets for graph neural networks in node classification problems. The Cora dataset was selected as the dataset to be compared with other models for the analysis experiments in order to incorporate the robust application capability and high classification accuracy of the proposed model. The Cora dataset is a citation network made up of papers on machine learning. Each paper was initially categorized into one of seven classes: case-based, genetic algorithms, neural networks, probabilistic methods, reinforcement learning, rule-based learning, and theory. Table 1 displays the categorization and associated explanatory remarks for the dataset.  
Stock dataset: The reason for selecting another dataset in this paper is to crawl the stock data from Oriental Wealth to create the stock dataset by itself, in order to demonstrate the broad application scope, strong applicability, and good future application value of the model proposed in this paper. This example also demonstrates that the same model-based strategy can be applied to comparable classification tasks. Document classification, government tendering document classification, news classification, government announcement classification at each prefecture level, etc., are all examples. The stock dataset used in this study comprises several Eastern Wealth Network Kotex stock prospectuses. For the category division, a total of eight categories were designed, including pharmaceutical manufacturing, computer technology, equipment manufacturing, material manufacturing, metal manufacturing, electrical machinery, environmental protection, and rubber-plastic manufacturing.
## 4 Testbed Development
### Testbed Architecture
#### 4.2.1 Concealment of the Starlink Operation Details  
The integration of Starlink introduces a third-party commercial solution that acts as a black box, so the internal processes or behaviors of the system are completely hidden from end users. This inevitably abstracts us from operational details (e.g., service perturbations caused by satellite overloads), and therefore, we have to understand service performance as something stable and constant over time.
## 2 Materials and Methods
### Overview of the Study Area
\begin{table}
\begin{tabular}{c c} \hline
**Geographical Features and Meteorological Factors** & **Details** \\ \hline Latitude and Longitude Range & 42\({}^{\circ}\)45\({}^{\prime}\)–45\({}^{\circ}\)20\({}^{\prime}\) N, 86\({}^{\circ}\)37\({}^{\prime}\)–88\({}^{\circ}\)58\({}^{\prime}\) E \\ \hline Regional Span & Approximately 180 km from east to west and 153 km from north to south \\ \hline Slope & The terrain is complex. The mountainous areas have relatively steep slopes, with slopes in the southern mountains reaching 30–60\({}^{\circ}\) or even greater. The northern plain areas have relatively gentle slopes, generally below 5\({}^{\circ}\). \\ \hline Aspect & Influenced by the mountain range orientation, the slopes around Urumqi are diverse. For example, in the southern mountain ranges, there are sunny and shady slopes. The southern slopes are usually sunny, with better lighting conditions, while the northern slopes are shady and relatively cold \\ \hline Atmospheric Pressure & Urumqi is under the control of a cold high-pressure system, resulting in relatively high atmospheric pressure. In winter, the Mongolian–Siberian cold high-pressure system is extremely strong. As Urumqi lies within its sphere of influence, its atmospheric pressure is higher than that of some surrounding areas. \\ \hline Temperature & Winters are cold and long. The average temperature ranges from \(-\)10 \({}^{\circ}\)C to \(-\)20 \({}^{\circ}\)C, and the extreme low temperature can drop below \(-\)30 \({}^{\circ}\)C. There is a large diurnal temperature variation, with the daily temperature difference generally between 10 \({}^{\circ}\)C and 15 \({}^{\circ}\)C. \\ \hline \end{tabular}
\end{table} 
Table 1: Summary table of key geographical information and meteorological factors. Figure 3: Schematic diagram of the altitude in the study area.
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 4 Proposed Method for Intrusion Detection and Prevention
### Detection Stage 1: OCSVM  
One-class support vector machine (OCSVM) is a distinctive method in one-class classification, focusing solely on a singular data class, unlike binary or multiclass methods [12]. After training, the model assesses whether a new sample belongs to the designated target class. OCSVM operates in a high-dimensional kernel space F by transforming samples from \"zDATA\" using a mapping function \(\phi\). The inner product within F is computed using a kernel function k aiming to find a hyperplane that maximizes the separation between data and the origin. OCSVM addresses a quadratic problem, formulating a hyperplane with a weight vector w and margin \(\rho\), allowing for soft margins via non-negative slack variables \(\epsilon\). The hyperparameter \(\
u\) controls the trade-off, acting as an upper limit on the proportion of anomalies. In the OCSVM framework, w signifies the weight vector of the hyperplane, and \(\rho\) represents the margin. The inclusion of non-negative slack variables \(\epsilon\) introduces flexibility by permitting some samples to extend beyond the hyperplane, softening the margin. The hyperparameter \(\
u\), constrained within (0, 1), plays a crucial role in  
Figure 5: The architecture of the proposed autoencoder.  
balancing the trade-off and acts as an upper limit on the proportion of anomalies, affecting the model's sensitivity to outliers. The Gaussian kernel function, as employed in Equation (4), effectively represents data in the kernel space, with the hyperparameter \(\gamma\) controlling the shape of the decision boundary in the feature space. Following problem resolution, the decision function f(x) is calculated for a given testing sample as in Equation (5):  
\[k(x,y)=exp\Big{(}-\gamma\parallel x-y\parallel^{2}\Big{)} \tag{4}\]  
\[f(x)=Sgn(<w,\theta(x)>f-\rho) \tag{5}\]  
Anomalies are identified, as illustrated in Figure 6, when new samples fall on the incorrect side of the hyperplane, resulting in negative values in the decision function for anomalous points, denoted as \"zDATA\"[1]. Conversely, normal samples positioned correctly on the hyperplane yield positive values in the decision function, denoted as \"zDATA\".
## 4 Fuzzy-Logic-Based Multi-Criteria Evaluation Metric for Spam Detection Approaches
### Mathematical Representation of Decision Rules  
In order to evaluate the impact of a fuzzy decision rule, transformation of the rule into a mathematical form is essential for a tangible outcome. This is achieved through a fuzzy aggregation function. Over the years, a number of fuzzy aggregation functions have been proposed in the literature. Some well-known functions are [PERSON]'s function, Dubois and [PERSON] functions, [PERSON]'s ordered weighted average (OWA) functions, [PERSON]'s function, [PERSON]'s function, and the Unified AND-OR (UAO) function, among many others [50]. Among these operators, the UAO function [49] has some unique features (see [PERSON] and [PERSON] [49] for details and mathematical properties of the function). Due to the unique characteristics of the UAO function, the function has been employed effectively in many diverse studies [51; 52; 53; 54; 55; 56; 57], Therefore, the UAO function is also used in the underlying study. The prime uniqueness of the UAO function is that it can behave as the \"AND\" function or the \"OR\" function. This \"ANDing\" or \"ORing\" is controlled by a variable \(\
u\geq 0\), whose value decides the behavior of the function. For the spam detection problem, the aforementioned three membership functions, accuracy, precision, and recall, are embedded in the UAO function as follows:  
\[\mu_{O}=\frac{\mu_{A}\mu_{P}\mu_{R}+\
u\max\{\mu_{A},\mu_{P},\mu_{R}\}}{\
u+\max \{\mu_{A},\mu_{P},\mu_{R}\}}=\begin{cases}I_{*}=\mu_{A\cup\
u\cup R}(x)&\text{if }\
u>1\\ I^{*}=\mu_{A\cap\
u\cup R}(x)&\text{if }\
u<1\end{cases} \tag{7}\]  
where \(\mu_{O}\) represents the overall membership value of the decision. \(I^{*}\) signifies the AND function while \(I_{*}\) represents the OR function. With \(0<\
u<1\), UAO behaves as the AND function, while \(\
u>0\) gives the OR behavior. Using Equation (7), the mathematical structures corresponding to the fuzzy decision rules given in Section 4.3 are represented as follows:  
* Rule 1: \(\mu_{O}=I^{*}(\mu_{A},\mu_{P},\mu_{R})\)
* Rule 2a: \(\mu_{O}=I^{*}(\mu_{A},I_{*}(\mu_{P},\mu_{R}))\)
* Rule 2b: \(\mu_{O}=I^{*}(I_{*}(\mu_{A},\mu_{P}),\mu_{R})\)
* Rule 2c: \(\mu_{O}=I^{*}(I_{*}(\mu_{A},\mu_{R}),\mu_{P})\)
* Rule 3: \(\mu_{O}=I_{*}(\mu_{A},\mu_{P},\mu_{R})\)  
In the above mathematical representations, the value of \(\mu_{O}\) is proportional to the individual membership values (i.e., \(\mu_{A}\), \(\mu_{P}\), and \(\mu_{R}\)). Therefore, high values of input membership values result in a high value of \(\mu_{O}\) and vice versa. Consequently, a high value of \(\mu_{O}\) indicates that the performance of the algorithm is on the high side, while a low \(\mu_{O}\) suggests a degraded performance. For example, a \(\mu_{O}\) = 0.95 indicates that the performance of a given algorithm is near the ideal performance (since \(\mu_{O}\) = 1 defines the ideal condition as per the fundamentals of fuzzy logic). Likewise, \(\mu_{O}\) = 0.85 indicates that the performance is good enough, but there is room for improvement.
# Real-Time 3D Reconstruction Pipeline for Room-Scale, Immersive, Medical Teleconsultation
## 3 Pipeline Overview  
Our processing pipeline for mesh reconstruction consists of five steps (Figure 3). We only accept a synchronized set of depth images as input, which either requires all images to match in a certain time window or an exact match if synchronization is handled beforehand using hardware synchronization. Initially, we use an image-based temporal filter as found in Realsense SDK [63] to reduce noise on the static parts of the depth images.
# Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor
## 4 Experiments
### Compression Speed  
In this sub-section, we compare the compression speed of MCDRC to CMIX. In our experiments, CMIX takes 13.6 min/MB for compression on average, even with its highly optimized implementation. As for MCDRC, the compression speed is related to the batch size. The compression performance of MCDRC on the MPACT signals with different batch sizes (64, 128, 256, 512, and 1024) is reported in Figure 3. For each batch-size configuration, the experiment is repeated 10 times, and the median results are reported. Other hyper-parameters are the same as in Table 4.  
As we can see from Figure 3, when the batch size increases, the compression ratio gradually decreases, but less time is needed for compression. Therefore, there is a trade-off between the compression ratio and the compression speed. When we use a batch size of  
Figure 3: The relationship between the batch size and (**a**) the compression ratio; (**b**) the average compression speed.  
256, the proposed method consistently outperforms CMIX regarding the compression ratio and is five times faster. Note that this result also holds for decompression, since these two processes are symmetrical.