esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Method for Packet Loss Types in IoT: An Naive Bayes Distinguisher
## 3 System Model and Feature Extraction  
Figure 1 shows a hybrid network system model that includes wired network and wireless network connected by a gateway. Servers, routers, and a gateway formed a wired LAN. Packets are sent by servers, passed through the router, and finally forwarded by the gateway to the wireless sensors. Assume that there is a bottleneck link between the router and the gateway. When the data stream increases, it will cause congestion and packet loss. The wireless link between the gateway and the wireless sensors has a high wireless error rate. That is, when packet loss occurs during transmission, it may be due to network congestion or wireless error.  
Packet loss authentication is a classification technique based on the characteristics of packet loss behavior. Choosing appropriate feature attributes is the key to successful classification. Since the number of selected feature attributes in the mathematical representation is the dimension, it determines the difficulty of the algorithm implementation. In theory, the more feature attributes are selected, the classification accuracy of the algorithm is higher. However, in practice, there are different degrees of dependence among the feature parameters, and their accuracy in classification is different. Therefore, selecting appropriate feature parameters will greatly improve the accuracy of the algorithm. We propose two feature parameters, the priority feature value and the RPTI feature value, which take into account both accuracy and efficiency of the algorithm.  
Figure 1: System Model.
# Optimizing Retransmission Threshold in Wireless Sensor Networks
## 3 Problem Description
### Problem Definition
Thus, \(k^{*}_{1},...,k^{*}_{n}\) are the feasible solutions of Equation (2), and hence:  
\[\min\sum_{i=1}^{n}\sum_{j=1}^{u_{i}-l_{i}}C_{i,j}S_{i,j}+p_{i}^{l _{i}} =\sum_{i=1}^{n}\sum_{j=1}^{u_{i}-l_{i}}C^{*}_{i,j}S_{i,j}+p_{i}^{l _{i}}=\sum_{i=1}^{n}p_{i}^{k^{*}_{i}}\] \[\geq\min\sum_{i=1}^{n}p_{i}^{k_{i}} \tag{7}\]  
Thus, the objective function value of Equation (2) is a lower bound of that of Equation (3). Based on the analysis above, we can conclude that \(k^{*}_{1},...,k^{*}_{n}\) are the optimal solutions of Equation (2); the values of the two objective functions are identical. **Corollary 4**: _The hardness of integer optimization Equation (2) is NP-hard. That is, finding optimal retransmission thresholds for each node in a delivery path is NP-hard._  
**Proof. ** Based on Theorem 3, the hardness of Equation (2) is identical to 0-1 programming Equation (3), which is equivalent to the _Knapsack problem_. Since the _Knapsack problem_ is _NP-hard_[29], the hardness of integer optimization Equation (2) is _NP-hard_.
# Curcumin Innovative Delivery Forms: Paving the Yellow Brick Road' of Antitumoral Phytotherapy
## 2 Biological Actions of Curcumin
### Molecular Targets of Curcumin
#### 2.2.1 Curcumin Modulates the Activity of Transcription Factors  
Three main families of transcription factors are involved in cell proliferation, cell invasion, metastasis, angiogenesis, and resistance to chemotherapy and radiotherapy. They are:  
1. the families of the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) and of the activated protein-1 (AP-1),
2. signal transducers and activators of transcription (STAT), and
3. steroid receptors [77]. NF-kB is involved in cell response to several external agents, including physical strain, oxidative stress (free radicals), and cytokines. NF-kB is usually inactive in the cytoplasm, but once activated by the adequate stimuli it can translocate to the nucleus, inducing expression of apoptosis-suppressing genes to promote cell proliferation and even metastasis. Curcumin was shown to inhibit the activation of the NF-kB pathway by studies in vitro [78] and in vivo [79] and in a phase II clinical trial [80]. AP-1 is involved in the differentiation, proliferation, apoptosis, and oncogenic transformations of the cells [81]. AP-1 can be activated by stimuli of growth factors, cytokines, or bacterial/viral infections. Activated AP-1 induces the expression of several genes that code proteins involved in the angiogenesis and growth of cancer cells, such as cyclin-D1, MMP, and VEGF [82]. Curcumin inhibits this pathway by direct interaction with the AP-1 DNA-binding site, namely in human leukemia cells, transformed keratinocytes and prostate cancer cells [83; 84; 85; 86; 87]. The effect of curcumin on the expression of NF-kB and AP-1 members was evaluated in an oral cancer cell line [88]. Nuclear extracts obtained from curcumin-treated cancer cell were evaluated regarding binding of the transcription factors AP-1 and NF-kB, to reveal that binding is reduced in a dose dependent manner and that in cells treated with 100 uM of curcumin, the DNA-binding activities of AP-1 and NF-kB were completely lost. These results confirmed the downregulation of several transcription factors and inhibition of NF-kB and AP-1. The Janus kinase (JAK) signal transducer of activators of transcription (STAT) pathway signalling pathway is a signaling pathway employed by diverse cytokines, interferons, growth factors, and related molecules, allowing these extracellular factors control gene expression and regulate cell growth and differentiation [89]. In cancer cells, this pathway is consistently active, being involved in metastasis. Inhibition of the JAK/STAT pathway by curcumin was observed in prostate, lung, and glioblastoma cancer cell lines [90; 91; 92]. Curcumin was shown to inhibit STAT3 phosphorylation and to lower levels of interleukin-6 (IL-6), a pro-inflammatory cytokine involved in cell proliferation and survival. Curcumin also exhibited antineoplastic effects in K652 chronic leukemia, ovarian, and endometrial cancer cells by suppression of JAK/STAT signalling [93; 94].
## 5 Conclusions  
The usefulness of Sentinel data in power system control is various. This paper presents the possibilities of using it in the protection against forest fires and aerosol contamination. Sentinel data are mainly favorable for power network planning and maintenance operations on short-term and long-term timescales. Satellite data used at the operational level on an hourly timescale should have a much-better time resolution. A better temporal and spatial resolution has been promised by the already-announced Meteosat Third Generation (MTG) satellites. Some of their products are already available at the non-operational level [58]. Alongside a better temporal resolution, it also promises a better spatial resolution, which could be crucial in power system planning and maintenance in the era of emphasized climate change and renewables inclusion. Local data are necessary for all levels, from electrical energy production through transmission and distribution to the energy market and consumption. Satellite data significantly contribute to data supply because it is unrealistic to expect that surface measurement stations will be installed with a resolution as good as that offered by satellite observations.  
Conceptualization, A.B.; methodology, A.B., B.F.-G. and B.F.; software, A.B. and B.F; validation, A.B. and F.T.; formal analysis, A.B.; investigation, A.B.; resources, A.B., B.F.-G., B.F., D.S. and F.T.; data curation, B.F., B.F.-G. and A.B.; writing--original draft preparation, A.B.; writing--review and editing, A.B., B.F.-G., B.F., D.S. and F.T.; visualization, A.B.; supervision, B.F.-G., B.F. and F.T.; project administration, D.S. and F.T.; funding acquisition, D.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Space Agency (ESA), grant number 4000137701/22/NL/SC, and by the Ministry of Science, Education and Youth Republic of Croatia, grant number NPOO.C3.2.R2-II.05.0009.  
Not applicable.  
Data Availability Statement:Data used in this research can be downloaded at [[https://dataspace.copernicus.eu/](https://dataspace.copernicus.eu/)]([https://dataspace.copernicus.eu/](https://dataspace.copernicus.eu/)) (accessed on 2 February 2025). The EFFIS data can be downloaded at [[https://forest-fire.emergency.copernicus.eu/](https://forest-fire.emergency.copernicus.eu/)]([https://forest-fire.emergency.copernicus.eu/](https://forest-fire.emergency.copernicus.eu/)) (accessed on 2 February 2025).  
Author [PERSON] was employed by the company Adnet Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 1 Introduction
### Contributions  
To speed up the architecture while utilizing fewer hardware resources, the following contributions are reported in this article:  
1. The fixed window algorithm is implemented to optimize point multiplication operations by leveraging the binary representation of the scalar multiplier and performing point doubling operations within a fixed window size, minimizing point additions and improving computational efficiency.
2. Two-stage pipelining is adopted to increase the throughput in resource-constrained devices. Dividing the cryptographic operations into two stages and optimizing each stage separately increases the overall throughput of the cryptographic system.
3. The Montgomery radix-4 multiplier (a hardware optimization technique) is proposed to increase the speed of cryptographic operations on resource-constrained devices. The proposed multiplier uses a radix-4 representation to perform multiplications more efficiently. It reduces the number of memory accesses and computation steps, making it a good choice for crypto accelerators in devices with limited resources.
4. An finite state machine (FSM) is designed that effectively controls the data path for the proposed low area point multiplication design.
## 4 Discussion
It has been shown in previous studies that the ratio of _Firmicutes_ to _Bacteroidota_ increases as the severity of diabetic conditions changes [65]. In the current research, the T2 DM group of mice had considerably higher Firmicutes abundance and significantly lower Bacteroidetes abundance, and this disruption of the intestinal-derived metabolic regulatory network accelerates the process of \(\beta\)-cell de-differentiation. Notably, the FMLSP treatment significantly ameliorated this intestinal flora disruption in T2 DM mice. This may be due to the fact that FMLSP is rich in phenolics, which were previously shown to alter the abundance of critical gut microbiota at the phylum level by [PERSON] et al. [66]. [PERSON] et al. showed that _Akkermansia_ in the gut has the potential to promote metabolic function in diabetic patients, and low concentrations of _Akkermansia_ indicate a weakened gut barrier function [67]. In the present study, FMLSP treatment significantly increased the abundance of _Akkermansia_. Studies have demonstrated that _Lachmospiraceae_NK4A136_group_ is associated with favorable metabolism and a healthy diet, which contributes to the degradation of dietary fiber for the prevention and treatment of diabetes [51]. FMLSP is rich in dietary fiber, which increases the abundance of _Lachmospiraceae_NK4A136_group_ and promotes intestinal metabolism. Meanwhile it has been shown that _Alloprevotella_ is associated with IR and is a key gut microbe involved in the hypoglycemic effect [68]. In addition, lactobacillus have been shown to repair the gut mucosal barrier to prevent the passage of harmful bacteria and regulate dyslipidemia due to long-term blood sugar metabolism disorders [69; 70]. This research demonstrated that the FMLSP intervention improved T2 DM by dramatically increasing the number of these two beneficial bacteria. The beneficial bacteria in the intestines of T2 DM mice are reduced, but the abundance of some bacteria increases, such as _Rikenelaceae_RC9_gut_group_, and _Enterorhabhus_. It is reported that the _Rikenelaceae_RC9_gut_group_, _Enterorhabhus_ as harmful bacteria are proven to cause colorectal cancer, gut inflammation, obesity, and diabetes [71]. In the research, FMLSP restored the equilibrium of the intestinal flora in T2 DM mice and dramatically decreased the number of Enterorhabolus and the dangerous bacteria Rikenellaceae_RC9_gut_group. In conclusion, by influencing the quantity of both good and bad bacteria in the gut flora, FMLSP may help people with type 2 diabetes.
# Secure and Lightweight Cloud-Assisted Video Reporting Protocol over 5G-Enabled Vehicular Networks
## 4 Overview of Proposed Protocol
### System Architecture  
As a major difference from the convectional cloud assisted vehicular network architecture, the 5G-Enabled vehicular network is deployed on the following communication mediums:  
* Heterogeneous Networks: This network is originated from the ultimate desire to achieve high data rate and network capacity for the 5G-enabled network. Thus, two solutions may help to attain the aforementioned capacities by making the size of cells smaller and embracing the mmWave spectrum. Making the size of the cell much smaller would increase the spectral efficiency [48]. On the other side, the mmWave communications will offer high data rates because it operates in the range of 30-300 GHz and 1-10 mm for the spectrum and wavelength respectively. As mentioned in [38], the mmWave technology still suffers from considerable propagation loss that generates tremendous line of sight (LOS) connections.
* D2D Communications: D2D communication enables devices to communicate with each other within the licensed cellular bandwidth without involving the BS. In the 5G-Enabled vehicular networks, the vehicles can communicate through D2D communication or by direct link under the mmWare technology.  
We describe the communication entities in the proposed protocol: TA, DV, DMV, RSC and vehicles that communicate through the on board unit (OBU) as shown in Figure 1.  
* Trusted Authority (TA): It is in charge of the registration of all entities (DMV, DV, RSC and vehicles) inside our system and issues cryptographic materials during the system initialization.
* Department of Motor Vehicles (DMV): All the vehicles are assumed to register with the DMV periodically. Beside the conventional techniques for vehicle's identification including the Electronic License Plate (ELP) or the Electronic Chassis Number (ECN), each vehicle is registered with a 5G identifier (5 GID) with the same functionalities as the subscriber identification module (SIM) chip.
* Road Side cloud (RSC): RSCs are servers located along the roads and accessible by the vehicles. RSCs stores the videos files (VFs) sent by the vehicles. In that case, the designated vehicles (DV) such as police or ambulance can download the files through the RSCs using mmWare communications. Due to the advancements of technology, we assume that RSCs are connected to an electricity power generator with enough computational capability.
* Designated Vehicles: The designated vehicles can be public or private vehicles registered by the government through the DMV that offers public services.
* Vehicles: Vehicles are equipped with OBUs which allow them to communicate with RSCs in order to send the recorded video files.
## 4 Survey Technique and Data Preprocessing
At the same time, the western part of the area was mapped by the predecessors quite accurately--in the western part of survey line 0 (points 7,8), a sedimentary cover began to appear with reduced values of resistivity, and then on survey lines 4,5,6 (points from zero to 10-15) its thickness increased synchronously with an increase in the elevation of the relief. The maximum depth of the structural unconformity boundary within the study area reached approximately 60 m and was located on the northwestern edge (profile 6 and 7 points 0,1). As a result, clarifying changes were made to the a priori geological map (Figure 12). Figure 11: 1D-inversion results of the UAV-TEM data on the survey lines No. 0 and \(-1\) (south of the transmitter line): (**a**) geophysical crossections (electrical resistance); (**b**) convergence of ordinary and model sounding curves. Thus, despite the shortcomings of the used prototype, the main one of which was the long time for stabilization of the measuring platform after UAV maneuvers, leading to the loss of some data, and the low frequency of pulses, it should be recognized that this technology, even in its current form, allowed obtaining data suitable for solving real geological problems. Even taking into account the discarded data (which could have been avoided by taking out the sections of turns, acceleration and deceleration of the UAV beyond the studied area), several hundred sounding points were obtained in 4 h. For comparison, in the case of terrestrial survey using the TEM method in conditions of dense forest, it is usually impossible to complete more than a few points, the maximum is the first tens of points per day. Based on this, we believe that the cost-effective price of UAV-TEM surveying should be half the price of terrestrial TEM surveying.
## 4 Experiments and Results
### Statistical Results  
The proposed Wet-ConViT model's per-class accuracy and overall accuracy metrics are thoroughly examined and compared to those of other current state-of-the-art models, as shown in Table 2. The results of this comparative analysis indicate the remarkable performance of the Wet-ConViT model, outperforming other models in terms of overall accuracy metrics and the majority of the land cover categories. However, the hybrid CoAtNet model demonstrated higher accuracies for some non-wetland classes, such as shrubland and barren land covers. The results revealed that the combination of convolutional and transformer components within the hybrid models yielded the highest accuracy compared to the single network architecture (either convolutional or transformer). However, convolutional layers were found to be more successful compared to transformers for classifying land covers, achieving an approximate overall accuracy of 90% with both ResNet50 and EfficientNet. This could be attributed to the better capability of CNNs in extracting local features, a key factor in land cover classification tasks that may be overlooked by transformers. This improvement was noticeable across all overall and most per-class accuracy metrics.  
Figure 7: Two-dimensional visualization of extracted features from (**a**) ResNet50, (**b**) EfficientNet, (**c**) ViT, (**d**) Swin, (**e**) CvT, (**f**) CoAtNet, (**g**) WMF, and (**h**) the proposed Wet-ConViT model using UMAP.
# Predicting Choices Driven by Emotional Stimuli Using EEG-Based Analysis and Deep Learning
## 2 Background
### Signal Processing  
Signal processing is an essential step for choice classification, which includes two steps: signal preprocessing and feature extraction.  
First, EEG signals are preprocessed to remove artifacts from the recorded signals and enhance the ratio of the signal power to the noise power. EEG artifacts result from either physiological or technical factors. Physiological artifacts are the signals generated by movements of the physical parts of the body, such as the head, face, neck, and eyes. The technical artifacts are related to powerline noises and electrode resistance variations. Since the EEG signals are noninvasive and the power lines cannot meet with the signals being collected, this generates a low signal-to-noise ratio [10].  
Next, feature extraction is applied after the preprocessing to reduce the high dimensionality of the EEG data [10]. EEG signals can be analyzed in terms of two domains: frequency and time [11]. To extract relevant features for choice prediction, frequency domain analysis was employed in this study since it is the most commonly used approach in neuromarketing studies [12].
## 7 Conclusions  
In conclusion, this survey article provided a comprehensive overview of the current state-of-the-art in the field of SVAD and the various AI techniques that have been applied to this problem. The review highlighted the methods, datasets, challenges, and future directions explored in previous studies. The need for automated systems for detecting abnormal events in real-time has been driven by the increasing use of CCTV and other video recording systems, leading to an overwhelming amount of video data being produced. The ability to learn from new observations and continuously improve anomaly detection capabilities is also paramount in video surveillance.  
The field of SVAD is expected to see significant advancements in the coming years, thanks to the rapid progress in AI techniques and the availability of reasonably priced hardware. The survey has shown that prediction-based and reconstruction-based techniques are at the forefront of AI-based SVAD and are expected to provide improved anomaly detection capabilities and enable real-time monitoring of large-scale video surveillance systems.  
It is also worth noting that the field of SVAD is still an active area of research, and there is still much room for improvement. Future research should focus on developing more robust and efficient algorithms and addressing existing methods' limitations. Additionally, more comprehensive and diverse datasets should be used to evaluate the performance of the proposed methods. Using large-scale datasets with various types of anomalies and scenarios will help improve the generalization capabilities of the proposed methods.  
In summary, this survey article provided an up-to-date overview of the current state-of-the-art in the field of SVAD and highlighted the most-significant developments, techniques, and datasets used in this area. Soon, prediction-based and reconstruction-based techniques will be the trend in AI-based SVAD, providing improved anomaly detection capabilities and enabling real-time monitoring of large-scale video surveillance systems.  
Conceptualization, E.S.; Methodology, E.S. and R.S.; Software, E.S. and B.A.; Formal Analysis, E.S. and R.S.; Investigation, E.S. and Q.A.A.-H.; Data curation, R.S.; Visualization, Q.A.A.-H.; Resources, Q.A.A.-H.;Validation, B.A., A.A. and A.A.A.; Funding acquisition, B.A., A.A. and A.A.A.; Writing--original draft, E.S., R.S., Q.A.A.-H., A.A. and A.A.A.; Writing--review & editing, E.S., R.S., Q.A.A.-H., B.A., A.A. and A.A.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of education and the Deanship of Scientific Research at Najran University under the Research Groups Funding program grant (NU/RG/SERC/12/9).  
Not applicable.  
The authors declare no conflict of interest.