esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Security Rules and Mechanisms to Protect Data from Assaults
## 6 Comparative Analysis
#### Novelty of Proposed Work  
In this paper, different techniques are used for data protection in different phases, and a set of rules are defined in each phase. When an attacker tries to access the data or apply cryptanalysis on the ciphertext, the attacker cannot access the original data due to the lack of a rule-based snatching algorithm. Bit-reversing is first implemented to protect plain text data so that the original data can be hidden. A salting mechanism is then implemented on the bit routing so that each value in the eight-bit paring can be divided into different pieces. Then, several time shiftings are performed using the Caesar cipher algorithm on the obtained values. By shifting the number of times, the previous value is replaced with the latest value, and the original data link is lost due to the latest value. Then, the cipher matrix algorithm is implemented on the Caesar cipher algorithm. A set of rules is defined in the cipher matrix algorithm. When the attacker tries to decipher the data, it will be difficult to understand and exploit each rule, which can increase the security of the data. A key is implemented on the cipher matrix text obtained from the cipher matrix algorithm, and a secure ciphertext is obtained with the help of the XORation technique. When the data are protected with the help of such algorithms, no matter how many efficient algorithms are developed to decrypt the data, the data cannot be decrypted, which is the novel contribution of this paper. A complete comparative analysis is shown in Table 6.
## 2 Methods
### Network Architecture  
Figure 2 shows the proposed network architecture and its dataflow. The proposed network adopts a FCOS-based encoder-decoder architecture. Given an input image, we first feed it into the feature extractor and obtain four feature maps {C2, C3, C4, C5}. ResNet-101 is taken as basic feature extractor first. Details of the output of backbone are shown in Table 1.  
In the sequence, two flows happen in parallel. In one flow, the output of conv5 is fed into the enhanced feature module (EFM) for improving the feature representation. The proposed EFM contains two key components, the multi-scale local attention (MSLA) module and the channel attention-guided unit (CAU), to promote discrimination between objects and background.  
In the other flow, the feature pyramid layers {P3, P4, P5, P6, P7} with 256 channels are generated by the original FPN. The layers P3 and P4 are generated by a lateral connection and a top-down pathway. The layers P6 and P7 are generated by down-sampling operation with a convolution layer, respectively.  
After that, the two flows converge: the enhanced feature map X output from EFM joins all feature pyramid layers followed by 1 \(\times\) 1 convolution to generate the final enhanced feature layers {E1, E2, E3, E4, E5}. That is important to improve feature reconstruction. Additionally, a context-aware strategy is used for better suppressing negative samples and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Stage** & **Backbone** & **Output** \\ \hline C2 & \(\left[\begin{array}{c}1\times 1\ conv,64\\ 3\times 3\ conv,64\\ 1\times 1\ conv,256\end{array}\right]\times 3\) & \(128\times 128\), 256 \\ C3 & \(\left[\begin{array}{c}1\times 1\ conv,128\\ 3\times 3\ conv,128\\ 1\times 1\ conv,512\end{array}\right]\times 4\) & \(64\times 64\), 512 \\ C4 & \(\left[\begin{array}{c}1\times 1\ conv,256\\ 3\times 3\ conv,256\\ 1\times 1\ conv,\ 1024\end{array}\right]\times 23\) & \(32\times 32\), 1024 \\ C5 & \(\left[\begin{array}{c}1\times 1\ conv,512\\ 3\times 3\ conv,512\\ 1\times 1\ conv,2048\end{array}\right]\times 3\) & \(16\times 16\), 2048 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The architecture of the backbone.  
Figure 2: The architecture of the proposed method, which is based on FCOS. The multi-scale enhanced layers are generated by the enhanced feature module and multi-scale feature fusion. For bounding box regression, a context-aware strategy and CIoU loss are introduced to achieve better performance.  
low-quality predicted bounding boxes. Finally, focal loss is used for classification branch and CIoU loss and centerness loss is used for bounding box regression.
## 4 Experiment Results and Discussion
### Experimental Parameters  
#### 4.2.1 Natural Model Training Parameters  
Natural model training involved training a model conventionally without utilizing FL. The objective of this training session was to assess the accuracy of the model, establishing it as a baseline for comparison with the global model trained using FL. In natural model training, a larger dataset was typically employed for training, validation, and testing. Conversely, FL operated in a distributed manner, with participating clients often having limited data and computing power in real-world scenarios.  
For training and testing the model, the parameter settings outlined in Table 1 were applied. Categorical crossentropy served as the loss function during both training and validation. The training configuration included a batch size of 32 and a maximum of 100 epochs. The training process was halted if the loss failed to decrease after 7 iterations. The model weight with the minimum loss value was retained at the conclusion of training. The optimizer chosen for this process was [PERSON], with a learning rate of 0.0001.
# Covert Channel Communication as an Emerging Security Threat in 2.5D/3D Integrated Systems
## 5 Covert Channel Attacks in 2.5D/3D ICs
#### 2.5.1 2.5D/3D Integration  
The number of commercial applications that utilize advanced packaging technologies has been increasing. These technologies include interposer, or interconnect, bridge-based 2.5D integration [78], high density organic substrates [79], TSV-based 3D integration with active interposer [80], fan-out wafer-level packaging [81], and hybrid bonding [82; 83]. Despite important differences in physical characteristics and cost, each of these emerging packaging technologies enables dense integration of chiplets within a single package [84]. Chiplet-based integration has the potential to provide heterogeneous systems, where chiplets, with diverse functions, can be fabricated with different technology nodes [85]. Having many, smaller chiplets (instead of a large monolithic die) increases yield and, potentially, decreases the overall cost [86; 87]. Furthermore, in 2.5D/3D integration, since chiplets are tightly coupled and interconnect density is high, conventional parallel interconnects can be used, which consume less power at lower interconnect latency as compared to serial interconnects [88]. These advantages are particularly important for emerging data-centric applications in domain specific computing, such as machine learning and Internet-of-things. Despite these promising advantages, dense 2.5D/3D integration of heterogeneous chiplets brings new and largely unexplored security challenges, such as physical covert channels.
# Application of OCT in the Gastrointestinal Tract
###### Abstract  
Optical coherence tomography (OCT) is uniquely poised for advanced imaging in the gastrointestinal (GI) tract as it allows real-time, subsurface and wide-field evaluation at near-microscopic resolution, which may improve the current limitations or even obviate the need of superficial random biopsies in the surveillance of early neoplasias in the near future. OCT's greatest impact so far in the GI tract has been in the study of the tubular esophagus owing to its accessibility, less bends and folds and allowance of balloon employment with optimal contact to aid circumferential imaging. Moreover, given the alarming rise in the incidence of Barrett's esophagus and its progression to adenocarcinoma in the U.S., OCT has helped identify pathological features that may guide future therapy and follow-up strategy. This review will explore the current uses of OCT in the gastrointestinal tract and future directions, particularly with non-endoscopic office-based capsule OCT and the use of artificial intelligence to aid in diagnoses.  
gastricintestinal imaging; optical coherence tomography; Barrett's esophagus; artificial intelligence +
Footnote †: journal:  
Of these technologies, OCT is a uniquely poised imaging modality, combining nearly microscopic resolution with volumetric and subsurface real-time imaging capabilities [12]. These characteristics position OCT to solve many of the weaknesses of the other GI tract imaging techniques. This review will explore some of the recent advances of OCT in GI applications and describe the future potential for OCT to incorporate purpose-build probe designs and computer-aided diagnosis techniques [12].
## 2 Materials and Methods
### Analysis of Gut Microbial Composition
#### 2.3.3 qPCR Analysis of Selected Targets  
qPCR was performed using Takyon(tm) No ROX SYBR MasterMix and the forward and reverse primers of each taxa-specific population (Eurogentec S.A., Seraing, Belgium). Reactions were performed in hard-shell 96 well plates (Bio-Rad Laboratories Inc., Tense, Belgium) covered with AMPLISeal(tm) plate sealers. The C1000(tm) Touch Thermal Cycler and CFX Maestro(tm) software v.4.1 from Bio-Rad were utilized as detection systems.  
All qPCR reactions contained 10 uL of the MasterMix and the pair of primers (from 0.3-0.5 uM) diluted in molecular biology grade water to reach 17.5 uL. A volume of 2.5 uL of each DNA template (\(\sim\)4 ng/uL) was added to reach a final volume of 20 uL per well. A Non-Template Control (NTC) was included in all assays. Sealed plates were briefly centrifuged (3500 g, 1 min, at room temperature) before being loaded into the thermocycler.  
A single qPCR protocol was validated according to standard guidelines for fluorescence-based qPCR experiments for all taxon targets analyzed, variating only the optimal annealing temperature (Tann) (Supplementary File S1, Table S1). The cycling protocol included an initial denaturation step at 95 \({}^{\circ}\)C for 5 min, 35 cycles at 95 \({}^{\circ}\)C for 15 s, the corresponding Tann for 15 s, and 72 \({}^{\circ}\)C for 30 s, followed by a final elongation step at 72 \({}^{\circ}\)C for 5 min [40]. Fluorescent data were obtained during the extension phase.  
The analysis of the melt curves (65 \({}^{\circ}\)C-95 \({}^{\circ}\)C) allowed us to estimate the specificity of the amplified products. Primer's efficiency was inferred from the shift of the quantification cycle (Cq) of an eight-point calibration curve designed with mixed DNA (from 0.025 ng-40 ng) from all the fermentation experiments. Efficiency percentages were validated to be between 90-105%, and regression coefficients above 0.98, following the recommendations of MIQE guidelines [41].  
A comparative quantification via the 2\({}^{-\Delta\Delta\text{Cq}}\) method was used to report the expression levels of the target populations [42]. Thus, results are stated as a fold increase (or decrease) of the target in the samples relative to the calibration sample (Day 0--'inoculation'), and they were normalized to the expression of the reference sequence used as 'universal' (total) bacteria.
###### Abstract
This analysis holds immense potential for various applications, including market research, brand reputation management, political sentiment tracking, and customer feedback analysis. Among the various forms of social media content, tweets have emerged as a particularly valuable source for sentiment analysis. Characterized by their concise and informal nature, tweets provide a real-time snapshot of public sentiment on a wide range of topics [11]. However, analyzing tweets presents unique challenges due to their limited length, non-standard language usage, presence of emojis and hashtags, and the use of abbreviations and slang [12; 13]. The main contributions of this study include the following: firstly, we propose a novel approach that incorporates negation cues (ne, neces, neete, necu, nema, nemaju, nemam, nemas, nemate, nemoj, ni, nigdje, nijedan, nijedna, nijedno, nikad, nikada, niko, nisam, nisi, nismo, nista, niste, nisu, odbijen, porice, poriem, poricemo, porices, poricete), AnA-words, and sentiment-labeled words from a lexicon to enhance the accuracy of sentiment analysis in Bosnian language tweets. Secondly, we explore various machine-learning techniques, including SVM [14; 15; 16], Naive Bayes [17; 18; 19], RF [20; 21; 22], and CNN [23; 24; 25], optimizing their parameters to achieve optimal performance. Thirdly, we leverage a pre-trained model, BOSentiment, to provide an initial sentiment value for each tweet, which is then used as input for the CNN. For the creation and development of language tools, including tools for sentiment analysis, it is necessary to have quality language resources. Researchers who want to develop language tools for languages lacking such tools, unfortunately, encounter obstacles because the creation of the most advanced tools requires language resources that less-resourced languages are lacking. The manuscript is organized as follows: In the introduction section, we provide a comprehensive overview of the importance of sentiment analysis in the context of social media and the challenges specific to analyzing sentiment in tweets, and a review of language technologies research that has been done for the Bosnian language and related languages. Following the Introduction, the State of the Art section presents a thorough review of the existing literature on sentiment analysis, with a focus on studies related to the Bosnian language and other relevant languages. Section Three delves into the methodology and work conducted in this study. Next, we present the results of our research, including the performance evaluation of our sentiment analysis model, and the last section, entitled Discussion and Further Work, comprises the conclusion of our work.
## 3 Results and Discussion
### New RFB Flow Frame Configuration  
In the case of the conventional flow frame, owing to the number of bifurcations, the symmetry coefficient (\(C_{sym}\)) reached a maximum of 3.8%, and the maximum flow rate deviation (\(D_{m}\)) of the solution entering the electrode reached 17.9%, as mentioned above. The flow path clogging problem was due to the small diameter and the oily bromide complex, and the chevrons at the inlet channel with the highest velocity increased the pressure drop of the entire flow frame. To address these problems with the conventional frame, the configuration of the flow channel was modified (Figure 15). The curved geometry of the third bifurcation (yellow bifurcation in Figure 10) was removed to solve the flow path clogging problem caused by the settlement of the oily bromide complex; this settlement was observed through previous research (refer to Section 3.2), and the fourth and fifth bifurcations, which exhibited that the highest symmetry coefficient (\(C_{sym}\)) were integrated. In addition, square vanes and rectangular outlets were constructed to reduce the nonuniformity of the flow into the electrode. After exiting the square vanes, the Zn/Br solutions formed mixing zones and uniformly entered the electrode.  
Figure 14: Pressure drops at several locations in the inlet channel along the flow direction.  
To solve one potential cause for the increased pressure drop in the conventional frame, the chevrons of the first section (Figure 16a) were moved to the end of the channel (third section; Figure 16b). The number of chevrons remained unchanged (72), so as to minimize the reduction in the mixing effect.
# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 3 Results
### DL-Based Applications for Optical Imagery
These simulations were generated based on physical models of wake formation and imaging mechanisms, focusing on accurately reproducing the specular reflections of sunlight on the wake regions, which are crucial for Kelvin wake identification in optical imagery. Their approach involved dividing large-scale GF-1 optical images into overlapping sub-images of 224 by 224 pixels. They employed a deep neural network, specifically GoogLeNet with inception modules [89], to classify each sub-image as containing a Kelvin wake or a natural sea surface. By merging the classified sub-images, they effectively detected and delineated wake regions. This framework transformed the detection task into a simpler classification problem, similar to classic two-stage detectors using region proposals. [PERSON] and [PERSON]'s method achieved a high recall rate of 94.0% and demonstrated that high-resolution spatial details are critical for detecting Kelvin wakes. Interestingly, they found that incorporating multispectral data through pan-sharpening did not improve the detection performance, indicating that the original high-resolution panchromatic images were most effective. They also experimented with removing wake regions distant from ship hulls--manually logged to simulate known ship locations--which increased precision and slightly decreased recall. This suggested that many false positives occurred in areas far from any identified ships.
## 4 Experimental Results and Analysis
### Experimental Setup  
To implement and evaluate our proposed methodology on the IoT-Botnet 2020 dataset, we performed the experiments on an HP laptop installed with a Windows 10 operating system, having 8 GB of RAM with Intel Core I7-8550U processor NVIDIA GeForce MX150. Python (version 3.6.9) is used as the primary implementation tool for implementation and evaluation in a _Google Colab_ environment by selecting the GPU as a hardware accelerator [77; 78; 79].
###### Abstract
The authors in [25] considered a reconfigurable intelligent surface (RIS)-aided cognitive NOMA model, where RIS (instead of the secondary relays) was deployed to serve two secondary destinations. However, the published works [4; 5; 19; 20; 21; 22; 23; 24; 25] were only concerned with one-way relaying underlay CRNs. Two-way relaying (TWR) [26] is an efficient method that provides higher throughput for bidirectional communication networks. In TWR, two sources attempt to exchange their data via the assistance of one or many common relays. In conventional dual-hop TWR networks [27], the data exchange is performed via 04 orthogonal time slots, and hence the obtained data rate is 02/04 (02 data/04 time slots). In digital network coding (DNC) TWR or three-phase TWR [27; 28; 29; 30; 31], the sources use the first two time slots to send their data to the common relay. Then, this relay attempts to decode the received data, performs the XOR operation, and finally transmits the XOR-ed data to the sources at the third time slot. The DNC TWR approach can reduce one transmission time slot, and hence the obtained data rate is 02/03 (02 data/03 time slots). The authors in [27; 28] proposed max-min relay selection approaches to reduce the bit error rate (BER) for the DNC TWR networks. In [29], the three-phase TWR model using FRS, with the presence of untrusted relays, was reported. The published works [30; 31] studied the OP performance of the DNC TWR networks operating in the radio-frequency energy harvesting (RF-EH) environment. Moreover, both PRS and FRS were considered and evaluated in [31]. Analog network coding (ANC) TWR or two-phase TWR was introduced in [32], where two source nodes used the first time slot to transmit their data to the common relay. At the second time slot, the relay amplified the received signals, and then sent the amplified signal to both sources. Therefore, the ANC TWR scheme obtained the data rate of 02/02 (02 data/02 time slots). However, in the two-phase TWR, when amplifying the received signals, noises are also amplified and accumulated at the sources. Unlike ANC TWR, the relay in DNC TWR can perfectly remove noises due to the decoding operation. However, the disadvantage of DNC TWR is that this technique must use three time slots for each data exchange. To enhance the data rate for DNC TWR, the published works [33; 34; 35] applied the interference cancellation technique (ICT) at the common relay, and hence the schemes proposed in [33; 34; 35] also obtained the data rate of 02/02. This paper proposes the DNC TWR scheme operating in the underlay spectrum sharing mode. In the primary network, the transmit antenna selection (TAS) and selection combining (SC) techniques are used at the transmitter and receiver nodes, respectively. In the secondary network, the DNC TWR approach with PRS and ICT is used to enhance the OP performance.
## 3 Results
### Physical Properties of Matrices
#### 3.1.3 Mechanical Testing  
The choice of carrier appropriate for use in local periodontal treatment is crucial, as it must display sufficient mechanical strength and flexibility, be devoid of sharp edges, and be not too sticky [41; 42]. The mechanical properties of rupture force and elongation at break of various CMC/GE and HEC/GE matrices were, therefore, investigated in dry states (as shown in Table 4) to determine the most appropriate matrix for clinical trials. The differences in the pore structure of matrices confer the higher flexibility of CMC scaffolds compared to HEC materials. The differences in the texture profile between CMC and HEC materials can be explained by the differences in the structure of these two polymers [43]. The matrices with a higher content of cellulose derivative elongated more, while the increase of gelatin in matrix composition decreased elongation. Increased elongation at break of matrices with a higher CMC and HEC concentration highlights the plasticizing effect of both polymers within the gelatin network [40]. Statistically significant differences were observed between all matrices with CMC sodium salt (parametric ANOVA test, \(p<0.05\)) with regard to mechanical properties. It is well known that flexibility depends on the type and strength of bonds that occur between the chains building the porous matrix. Covalent bonds in a gelatin molecule are of stiff nature, so a higher content of gelatin and a higher content of amino acids affects the strength of interactions between the chains, which is reflected in a lower ratio of elongation and higher force required to tear the material apart [17]. Moreover, a homogenous, dense pore network results in strong interactions between the filler and, thus, restricts the matrix motion [40].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Batch Code** & **Weight Loss of Matrices \(\pm\) SE (\%) n = 6** \\ \hline CMC1 & \(11.58\pm 1.83\) \\ CMC2 & \(20.89\pm 2.55\) \\ CMC3 & \(27.83\pm 2.76\) \\ HEC1 & \(36.62\pm 1.12\) \\ HEC2 & \(45.12\pm 1.40\) \\ HEC3 & \(77.00\pm 2.80\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Weight loss analysis of CMC and HEC porous matrices: Data are presented as the mean \(\pm\) SE, n = 6.