esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Seasonality Anomaly Detection Algorithm under Intricate KPI Profiles
## 3 Materials and Methods
### Scaling Auto-Period Seasonal Length Detection Algorithm
#### 3.1.3 Restore the Seasonal Length  
Our \(ASAD\) algorithm would restore and regularize the Auto-Period method's calculation findings after Auto-Period algorithm completing the fitting period of scaled seasonal components. The scaled seasonal component is the input of Auto-Period algorithm, and Auto-Period's computation result is the period or the seasonal length of the scaled seasonal component. As a result, the original seasonal length requires to be restored by using the scaled factor. In the restoration process, the main unit of real seasonal length is usually days, quarters and so on. To accurately recover to the original seasonal length, seasonal length identification in \(ASAD\) method uses the day-based rounding approach. The original seasonal length must be regularized in days as the basic unit when the restoration operation is done. The procedure for restoring and regularizing method of Auto-Period algorithm is as follows.  
We design \(map\) function as the regularization method, and the restoration process is to multiply the scaled seasonal component \(S^{\prime}\) by the number of sample points for down-sampling. Where the period of scaled seasonal component \(S^{\prime}\) is denoted as \(\mathit{period}_{S^{\prime}}\), and  
Figure 4: Period fitting of seasonal component data.  
the seasonal length of the seasonal component \(S\) is indicated as \(seasonality\). Finally, \(map\) function rounds the duration of time using days as the base unit.  
\[seasonality=map(m\times period_{S^{\prime}}) \tag{7}\]  
where \(map\) is a function that uses day as the basic unit to round the length of time, \(m\) is the number of sample points for down-sampling.
## 5 Implementation Details  
The current study proposes a new method for identifying L-type pseudoknots, a rare type of pseudoknot. The proposed method involves creating a set of all potential pseudoknots according to a proposed grammar, and selecting the best option based on the principles of maximum base pairs around the pseudoknot and minimum free energy. The prediction of pseudoknots for an arbitrary RNA sequence is an NP-complete problem, and current algorithms, such as free energy minimization algorithms, become less precise as the sequence length increases. In addition, heuristic approaches lack generalization capabilities when tested under different datasets. To address these challenges, we propose a hybrid strategy that selects the RNA sub-sequence with the most probable pseudoknot expression. This involves creating a set of all L-type pseudoknot structures and then solving an optimization problem to select the pseudoknot expression with the maximum number of base pairs around the pseudoknot and the minimum free energy. The code routines were implemented in C, in Python, for further execution time optimization during the parsing task. The input sequence is sliced into multiple sub-sequences to parallelize the workload, and a parallel CFG parser is employed to evaluate all sub-sequences in parallel by spawning a pool of tasks. Each parser instance produces a pseudoknot structure that describes potential pseudoknots within the CFG domain. All pseudoknot structures are stored in a data structure, and the most likely solution is selected based on the least free energy. To overcome the computational and memory-intensive task of computing the free energy for each potential RNA folding, a maximum stem count lookup is performed, with a time and space complexity of O(n) proportional to the input sequence's length. The source code for the implementation is publicly available under the _L-type-knotiy_ GitHub repo [51].
# Comparative Study of Energy Savings for Various Control Strategies in the Tunnel Lighting System
## 2 Tunnel Lighting Controls: Strategies and Technologies  
The tunnel lighting control strategy is a conceptual description of how to modify the amount and quality of illumination provided by a lighting system to suit the luminance and human vision comfort requirements. The strategy provides specific technology methods to control the lighting system while representing a significant contribution to tunnel energy consumption.  
Tunnel lighting control strategies have gone through two successive stages: manual control and automatic control strategies [41]. The automatic control strategy implements energy-saving by decreasing lamps' operating time based on various factors such as time of day, ambient luminance, and the presence or absence of vehicles. Thus, this strategy consists of time-scheduling control, daylight-adaptation control, and intelligent control. In this section, the performance, hardware devices, and energy consumption of several control strategies are investigated.
## 3 Laser Tracker as a Measurement System  
An LT is a portable measurement system that provides the position of a point in spherical coordinates. This position is determined by comparing a measurement beam and a reference beam from the combination of a laser interferometer and the readings of the azimuth and polar coordinates of the angular encoders. Due to its versatility, LTs are used in many industrial applications such as in metrology and quality departments. Due to its long measurement range, accuracy, and versatility, the LT was selected as an adequate measurement system for volumetric verification, allowing the purchase of a dedicated system to be avoided and reducing costs.  
The three biggest LTs manufacturers are Leica Geosystems, Faro Inc. (formerly SMX, Lake Mary, FL, USA), and API (Automated Precision Inc., Rockville, MD, USA). All of them have their own design and system characteristics. Also, the origins of their own coordinate systems are in different places (Figure 3).  
The main sources of errors in LT accuracy that affect verification are divided into errors due to environmental influences, assembly, and calibration errors, errors of LT components such as encoders or sensors, and errors due to the retroreflector and measurement technologies [24; 25; 26; 27; 28].  
Figure 2: Working principle of post-processor software.
## 3 Materials and Methods
### Attention Module  
#### 3.2.1 Backbone: Feature Extraction Network (CSPDarknet53)  
Regardless of the subdataset in Figure 5, the YOLOv4 model's backbone module is still used to extract image features. CSPDarknet53 can extract important semantic features from the YOLOv4 model. As a result, it is employed as a backbone network. As shown in Figure 5, CSPDarknet53 is a CNN that acts as the foundation for object detection by employing DarkNet-53. The feature map on the base layer of a CSPNet network is divided into two parts that are linked together using a CSP-stage hierarchy. The complexity of network optimization can be considerably decreased while keeping accuracy with this network by identifying problems with redundant gradient information. It is compatible with a variety of networks, such as DenseNet, ResNeXt, and ResNet.  
In CSPDarknet53, there are five residual blocks that are labeled CSP 1-5. Each residual block (Resunits) has several residual units. Resunit employs the 3 \(\times\) 3 convolution operation. This convolution operation includes two steps. The second convolution operation (3 \(\times\) 3 conv and 1 \(\times\) 1 conv) is then conducted on each residual block. The overall output is the outcome of these two methods. Figure 6 depicts the particular operational technique. As a result, each residual block can resolve the vanishing gradient problem.  
Figure 6: Structure of CSPDarknet53.  
Therefore, the backbone network could extract image features. In our model, the two subdatasets automatically classified after the preattention stage still use CSPDarknet53 to extract target features. CSPDarknet53 enters the feature fusion stage after performing feature extraction since it has a large receptive field and a minimal number of parameters.  
Figure 6 contains 29 convolutional layers of 3 \(\times\) 3 and 5 residual blocks. * represents the multiplication sign. The residual units in each block are 1, 2, 8, 8, and 4. The resolution of the input image of 618 \(\times\) 618 is set; thus, the output resolution of the third residual block is 76 \(\times\) 76. The fourth output resolution is 38 \(\times\) 38, and the fifth output resolution is 19 \(\times\) 19.
## 5 Experiments
### Second Experiment: Evaluate the Model for Transfer Learning Tasks  
We compare the SelfCCL model to the previous models on the seven SentEval transfer learning tasks as follows:  
MR [66]: Binary sentiment prediction on movie reviews.  
CR [67]: Binary sentiment prediction on customer product reviews.  
SUB [68]: Binary subjectivity prediction on movie reviews and plot summaries.  
MPQA [69]: Phrase-level opinion polarity classification.  
SST-2 [70]: Stanford Sentiment Treebank with binary labels.  
TREC [71]: Question type classification with six classes.  
MRPC [72]: Microsoft Research Paraphrase Corpus for paraphrase prediction.  
More details about these seven tasks can be found in Ref. [65]. In this experiment, the default configuration of the SentEval toolkit [65] was used, in which sentence embeddings serve as features for a logistic regression classifier. Then, the logistic regression classifier is trained with different tasks in a 10-fold cross-validation and the prediction accuracy is calculated for the test-folds. The results are given in Table 5.  
**Results:** As shown in Table 5, SelfCCL-BERT and SelfCCL-SBERT outperform SOTA SimCSE and all previous models except for the baseline SBERT. As explained in Section 2, the baseline SBERT performs better than all other models on these seven classification tasks because it was trained on the classification objective. For SelfCC-BERT, the corresponding improvement is 0.39 point compared to the SOTA SimCSE. For SelfCCL-SBERT, the improvement is 0.13 points compared to SBERT-base-nli-v2. SelfCCL-SBERT also achieved an approximately similar average accuracy as SOTA SupMPN in transfer learning tasks, while according to Table 3, it requires less computational power compared to SOTA SupMPN. To sum up, using both methods, contrastive learning and curriculum learning, also show an improvement in average accuracy compared to cases where only contrastive learning is used.
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
###### Abstract  
The development of the Internet of Things (IoT) predicts several new applications, some of which are designed to be incorporated into e-health systems, and some technologies, like cloud computing and device-to-device communication (D2D), are promising for use in the support of resource-constrained devices employed in Mobile-health (m-health) and Telecare Medicine Information Systems (TMIS). In a scenario with billions of devices predicted for the IoT, it is essential to avoid performance and security problems, among others. Security is fundamental for the achievement of optimal performance regarding the sensibility of e-health shared data and, especially, the anonymity of patients and other entities, while it is also essential to consider the scarcity of bandwidth in wireless networks. This paper proposes a new mutual authentication protocol for m-health systems, which supports D2D communication, ensuring security and surpassing the performance and security of other authentication procedures reported in the literature.  
authentication; device-to-device; m-health; security; IoT Article
# FPSO Hull Structures with Sandwich Plate System in Cargo Tanks
## 2 Literature Review
### Sandwich Plate System (SPS)  
The studied FPSO uses a sandwich plate developed by Intelligent Engineering Limited (IE), considering a composite laminate with two metal layers and an elastomer core instead of conventional steel structures of stiffened plates. It was reported to have an enhanced capability in terms of impact absorption in the structure with this sandwich plate system (SPS) when compared to the conventional structures made of steel [2,3]. This is due to the so-called \"sandwich effect\", when the second moment of area and bending stiffness are significantly increased in the cross-section of the structure [1].  
Regarding the marine and offshore structure environment, the SPS panels are used in several designs as an alternative to the traditional steel structure [10]. For example, an engineering study [11] confirmed the impact resistance of the SPS overlay in an impact scenario between an FPSO and a supply vessel. It was studied as a form to offer equivalent protection to an FPSO monohull as a double hull. The main results showed that the SPS attached to the side plates was able to provide increased local impact resistance, reduced critical fatigue stresses, reduced risks and costs during fabrication and inspections and less maintenance.  
Furthermore, [PERSON] et.al [3] studied an innovative method for the repair of in-service FPSOs using the SPS overlay as a way to reinstate the strength and corrosion allowance. In the SPS plate, the existing stiffened plating was considered one of the plates, with a second plate attached to it with an elastomer core. The study carried out several structural evaluations comparing both arrangements (AH/DH32 steel and SPS overlay) for the bottom structure of the FPSO using the finite element method (FEM). The main results showed that the sandwich plate configuration had a better performance, reducing the stresses at the static load case and also increasing the ultimate strength of the structure.  
The Transport Industries Under European Commission in 2013 [1], with the help of several classification societies and universities, proposed a coordination action on advanced sandwich structures in the transportation industry. The result was a guide for best practices for sandwich structures, including the SPS, in marine applications with the main objective to provide knowledge of diverse types of sandwich plates in order to help their integration in ship design and fabrication. It was focused mainly on the design of plates with the analytical/numerical/ experimental analysis of several limit states, such as ultimate strength, buckling and fatigue, and other types such as vibration and crash response.
# Supporting Privacy of Computations in Mobile Big Data Systems +
## 3 Background
### 1-Out-Of-4 Oblivious Transfer  
In the 1-out-of-4 oblivious transfer (OT) protocol, there are two parties: a sender and a chooser. There are four private messages at the sender: \(M_{00}\), \(M_{01}\), \(M_{10}\), \(M_{11}\), and the chooser holds two private choice bits, \(\sigma_{1},\ \sigma_{2}\). At the end of the 1-out-of-4 OT protocol, the sender learns nothing, and the chooser learns \(M_{\sigma_{1}\sigma_{2}}\) only.  
The sender randomly generates two pairs of keys, \((L_{0},L_{1}),\ (R_{0},R_{1})\), and computes the encryptions of \(M_{00}\), \(M_{01}\), \(M_{10}\), \(M_{11}\) as follows. Let \(F_{k}(x)\) denote the output of a pseudorandom function such as AES-128, that is keyed using \(k\) on the input \(x\). Let \(E_{ij}\) denote the encryption of \(M_{ij}\), \(\forall i,j\in\{0,1\}\). Then, \(E_{ij}=M_{ij}\oplus F_{i_{L}}(2i+j+1)\oplus F_{R_{j}}(2i+j+1)\).  
The sender and the chooser engage in 1-out-of-2 OT twice. In the first 1-out-of-2 OT, the sender holds two messages, \(L_{0},\ L_{1}\), and the chooser holds the choice bit, \(\sigma_{1}\); at the end of this OT, the chooser obtains \(L_{\sigma_{1}}\). In the second 1-out-of-2 OT, the sender holds two messages, \(R_{0},\ R_{1}\), and the chooser holds the choice bit, \(\sigma_{2}\); at the end of this OT, the chooser obtains \(R_{\sigma_{2}}\).  
Now, the sender sends all the four encryptions, \(E_{00}\), \(E_{01}\), \(E_{10}\), \(E_{11}\), to the chooser. Using \(L_{\sigma_{1}}\), \(R_{\sigma_{2}}\), the chooser decrypts \(E_{\sigma_{1}\sigma_{2}}\) to obtain \(M_{\sigma_{1}\sigma_{2}}\), as \(M_{\sigma_{1}\sigma_{2}}=E_{\sigma_{1}\sigma_{2}}\oplus F_{L_{\sigma_{1}}}(2 \sigma_{1}+\sigma_{2}+1)\oplus F_{R_{\sigma_{2}}}(2\sigma_{1}+\sigma_{2}+1)\).
## 4 Results
### Biofilm Assay
#### 4.3.2 Biofilm Formation Assay  
Figure 6a,b shows the effectiveness of free NAC and NACESOL(tm) in the reduction of bacterial colonies growth, respectively. Results are expressed as percentage of bacterial cells reduction using a non-treated metal disk as a control.  
Figure 5: Biofilm appearance after 48 h of incubation.  
In both cases, the active concentrations chosen for the test were 0.5 mg\(\cdot\)mL\({}^{-1}\); 2 mg\(\cdot\)mL\({}^{-1}\); 4 mg\(\cdot\)mL\({}^{-1}\). These concentrations were chosen based on the literature [17]. [PERSON] et al. evaluated NAC activity against a wide range of strains and discovered that NAC at the concentration of 0.5 mg\(\cdot\)mL\({}^{-1}\) was able to reduce the adhesion of bacteria to steel surfaces.  
The number of CFU on controls was always homogeneous for each experiment: \(2.4\times 10^{6}\pm 3.6\)\(\times\)\(10^{5}\) (Figure 5(a)) and \(2.6\times 10^{6}\pm 2.5\times 10^{5}\) (Figure 5(b)). The control bacterial cells reduction in the NAC experiments was \(1\%\pm 14.7\%\), and in the NACESOL\({}^{\mathrm{TM}}\) experiments \(1\%\pm 9.6\%\). Thus, it can be concluded that experiments are well carried out, and active results can be compared among them.  
NAC is able to determine a statistically significant reduction of the number of colonies only at concentration of 4 mg\(\cdot\)mL\({}^{-1}\) (\(p=0.0001\)). The results highlighted in Figure 5(a) show that at 0.5 mg\(\cdot\)mL\({}^{-1}\) the free NAC reduces the number of colonies by \(19.42\%\pm 4.28\%\), at 2 mg\(\cdot\)mL\({}^{-1}\) the reduction is \(20.87\%\pm 4.34\%\) while the concentration of 4 mg\(\cdot\)mL\({}^{-1}\) produces a reduction of the biofilm equal to \(89.52\%\pm 5.83\%\).  
NACESOL\({}^{\mathrm{TM}}\) shows an increase effect to compare with free NAC. In fact, the multi-composite at 0.5 mg\(\cdot\)mL\({}^{-1}\) (active concentration) is able to reduce the number of colonies by \(51.41\%\pm 22.10\%\), at 2 mg\(\cdot\)mL\({}^{-1}\) the reduction is equal to \(68.98\%\pm 24.30\%\); both reductions are statistically significant  
Figure 6: Percentage of biofilm reduction, expressed as a means \(\pm\) SDs, of (**a**) free N-acetylcysteine, at 0.5, 2, 4 mg\(\cdot\)mL\({}^{-1}\); control; (**b**) NACESOL\({}^{\mathrm{TM}}\), at 0.5, 2, and 4 mg\(\cdot\)mL\({}^{-1}\) of active; control.  
(\(p=0.0168\)). The concentration of 4 mg\(\cdot\)mL\({}^{-1}\) is not able to determine a significantly reduction (\(p=0.587\)) of colonies, also if results seem very good with a percentage of reduction of 48.00% \(\pm\) 31.14%. Tests made with resveratrol, \(\alpha\)-cyclodextrinrins, and the resveratrol-cyclodextrinrin compound demonstrate that neither one nor others have any effect on the biofilm (data not reported).
## 3 Results and Discussion  
### Material Characterization  
The FESEM and EDX data indicate stark differences between the synthesized GO and the graphite flakes that were used as a starting material. As can be seen from the panel of images in Figure 1, clear morphological differences are evident between the graphite flakes and the GO structures. The sharp edges that are seen in the graphite (Figure 1a) are replaced by more rounded edges as seen in Figure 1b, which is possibly due to the concentrated acid treatment. The EDX data shows the increase of oxygen in the GO material (approximately by 31%) where graphite was oxidized in the synthesis process (see Figure 1c,d).  
The characteristic GO flakes can easily be seen in the TEM images (Figure 2), which is further indication of the successful synthesis of high-grade GO. Verifying the presence of such GO flakes was important since it has been suggested that the GO sheets play a central role in its efficacy as an antimicrobial agent [40]. It has been hypothesized that larger GO flakes or sheets exhibit more superior antimicrobial activity compared to smaller flakes due to the increased surface area which can be used to cause entire warping of bacterial cells, thereby blocking their active sites and ultimately hindering their proliferation [41].  
Figure 1: SEM (scanning electron microscopy) images for graphite flakes (**a**), GO flakes (**b**), EDX (energy-dispersive X-ray spectroscopy) mapping for graphite and GO (**c**,**d**), respectively.  
Figure 2: TEM (transmission electron microscopy) images for synthesized GO flakes. The grid matrix seen is part of the support structure holding the GO sample.  
The TGA (and DTGA) thermograms for GO samples are shown in Figure 3. As can be seen by the DTGA for GO, the adsorbed water content is close to 10% of the total weight of the sample (area 1). The oxygen containing functional groups account for an impressive 38% of the total weight (area 2), indicating an oxygen-rich GO sample. The remaining weight of the sample nearing 40% accounts for the non-combusted material or ash (area 3). The TGA thermograms overlay for both graphite and GO seen in (b) clearly demonstrates the oxidation of graphite, with the introduction of adsorbed water molecules and oxygen containing groups indirectly inferred through the less stable curve for GO compared to the stable graphite TGA readings. A staggered decomposition of the GO can be observed where an initial weight loss is observed just below 100 \({}^{\circ}\)C which accounts for the loss of water molecules adsorbed by the GO sheets. A further graduated weight loss continues until 400 \({}^{\circ}\)C which is accounted for by the formation and subsequent loss of carbon dioxide. [PERSON] et al. [42] found that at 130 \({}^{\circ}\)C, the CO\({}_{2}\) is released as it is freed from the GO flakes. Importantly, the TGA curve for GO did not change drastically below 100 \({}^{\circ}\)C, which highlights the stability of GO at a vast range of membrane application processes.