esa-sceva/satcom-chunk-collection huggingface dataset:
# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
## 5 Experiments and Results
### Attack Simulation
#### 5.2.5 Malicious Client Attack  
In this attack, a malicious client targets the blockchain network by flooding the ordering service with a high volume of unconfirmed transactions [73]. This overwhelms the network's processing capacity, delaying consensus formation as nodes prioritize handling the spam requests over legitimate transactions. This attack scenario is shown in Figure 9. To demonstrate this attack, we deployed three rogue client applications using the HLF SDK for Java, two clients authenticated with peers from Organization 1 and one client authenticated with a peer from Organization 2. Each client was assigned a unique Fabric user certificate for secure channel interaction. We created a function to generate high-volume transaction proposals that were sent concurrently to ensure a sustained pressure on network resources, which mimicked a DoS scenario. Transactions were generated at a rate that exceeded the network's default throughput limits, which saturated the communication channels. Next, we observed the impact of this attack on a normal node. Figure 9(a) shows a 183% increase in the delay for transaction logging and retrieval on a legitimate node during the attack (going up from 2.5 s to 7.2 s). As shown in Figure 9(b), the transaction throughput was decreased by 65% as the ordering service struggled to manage the malicious load (it went down from 24 TPM to 7.1 TPM). The attack forced the nodes to allocate resources to redundant request handling, which stalled the block finalization and propagating delays across the network.  
Figure 8: Comparison of transaction throughput and successfully recorded transaction rate in collusion attack.
## 1 Introduction  
There is a clear trend in consumer preference towards natural food ingredients and additives that are perceived to be healthy [1]. It is well known that diet supplementation with plant-based products rich in phytochemicals, like phenolic compounds [2; 3], can provide a positive influence on human health--reducing the risk of cancer, diabetes and inflammatory diseases, and preventing neurodegenerative and cardiovascular system disorders, among others [4; 5; 6]. In this context, knowledge of the bioactive potential of unexplored plant secondary metabolites may grant the development of new plant-based functional foods, nutraceuticalals and drugs [6; 7].  
_Salvia_ is the largest genus in the Lamiaceae family of plants, with over 900 species [8]. The name _Salvia_ has its origin in the Latin word _salware_, that means \"healer\", in reference to the curative properties of these plants, which have been used since ancient times as a medicinal herb [9; 10]. The plants of this genus are distributed worldwide, although they are particularly prevalent in tropical and temperate regions of Europe around the Mediterranean area, in South-East Asia and in Central and South America [8; 11]. They are mostly aromatic and perennial shrubs, with flowers of distinct colors (e.g., violet, blue, lilac or pale-blue), size and structure--such as their staminal lever, the length and form of the corolla tube and their floral nectery morphology. In general, stems are long, reaching between 50 to 100 cm, with 3 to 5 branches, containing opposite, simple, ovate and petiolate leaves [12; 13; 14].  
Sage plants have been used for centuries in culinary, cosmetic and fragrance industries, and in traditional medicine [9; 10; 15]. The latter application includes their use in a wide range of ailments, including digestive, respiratory, renal, hepatic, neurological, cardiac, blood circulation and metabolic, among others [16; 17; 18; 19]. In fact, many authors confirmed the potential bioactive properties of _Salvia_ constituents, such as those in essential oils [20; 21; 22; 23]. More recently, polar extracts rich in phenolic compounds have also emerged as promising bioactive agents, enabling them to be applied in different areas [15; 24; 25; 26; 27].  
This review highlights the health benefits of several _Salvia_ species as potential sources of phenolic compounds that can combat oxidative stress and related diseases, namely diabetes and obesity. The summary of relevant studies focusing on these issues is shown in the four tables of this manuscript which also include the available information regarding the main phenolic constituents.
# Light-Activating PROTACs in Cancer: Chemical Design, Challenges, and Applications
## 4 Photoswitches in PROTACs
### Photoswitchable PROTACs Targeting Kinase Protein
**Azo-PROTAC-4C** (-) showed no BCR-ABL and ABL protein degradations, suggesting that BCR-ABL and ABL degradation of **Azo-PROTAC-4C** is CRBN-mediated ubiquitination. Further use of **dasatinib** (as a POI inhibitor), lenalidomide (as an E3 ligase ligand), and an inhibitor NEDD8-activating enzyme (**MLN4924**) in competitive assays, and observing no degradation of BCR-ABL, suggests the **Azo-PROTAC-4C** degradation activity (a) utilizes the active site of BCR-ABL (in case of dasatinib), (b) is cereblon-E3 ligase mediated (in the case of lenalidomide), and (c) depends upon NEDD8-activating enzyme (in case of **MNL4924**). All these outcomes show confirmation that **Azo-PROTAC-4C** is a CRBN-dependent ABL degrader. Interestingly, a substantial difference in ABL and BCR-ABL degradation were recorded for _cis-_ and _trans_-configurations of **Azo-PROTAC-4C**. In a dose-effect evaluation, a slight degradation at 25 nM but a significant BCR-ABL and ABL protein degradation at 100 nM was observed for _trans_-configuration of **Azo-PROTAC-4C**. However, no noticeable BCR-ABL degradation was observed, even at 500 nM for cis-configuration of **Azo-PROTAC-4C**, which could have been due to the \"_hook effect_\" (a typical limitation with PROTAC approaches) or based on the presence of photodationary state of _cis_-configuration of **Azo-PROTAC 4C** in the solution [46]. The spatiotemporal degradation of ABL and BCR-ABL proteins by **Azo-PROTAC-4C** was studied by using time-course analysis. In this study, _trans_-configuration of **Azo-PROTAC-4C** showed ABL protein degradation started after 4 h treatment, significant degradation of BCR-ABL and ABL proteins after 10 h, and > 90% of BCR-ABL degradation after 32 h treatment. Interestingly, no noticeable BCR-ABL reduction for _cis_-configuration of **Azo-PROTAC-4C** was recorded, even after 32 h of treatment, which was somewhat surprising because of the reported half-life (T\({}_{1/2}\) = 620 min at 25 \({}^{\circ}\)C) and, therefore, a large proportion of _trans_-configuration of **Azo-PROTAC-4C** could be present at earlier time points [46]. To evaluate the reversible photoswitchablity of **Azo-PROTAC-4C** against its protein degradation activity, K562 cells were treated with _trans_-configuration of **Azo-PROTAC-4C** for 24 h and then irradiated with UV-C light every 4 h. ABL and BCR-ABL levels were increased over time in the UV-irradiated group, while low cellular levels of these proteins were maintained in the visible light group. Later, after preincubation of K562 cells with _cis_-configuration of **Azo-PROTAC-4C** shielded from light, K562 cells were transferred to a fresh medium and irradiated with visible light, where BCR-ABL degradation was observed with time. However, evaluation of the cell viability of _cis_-configuration of **Azo-PROTAC-4C** would be quite interesting in order to engage researchers, although the authors did not perform this [46].
## 3 Experimental Demonstration
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline  & & \(\lambda_{1}\) & \(\lambda_{2}\) & \(\lambda_{3}\) & \(\lambda_{4}\) & \(\lambda_{5}\) & \(\lambda_{6}\) & \(\lambda_{7}\) & \(\lambda_{8}\) & \(\lambda_{9}\) & \(\lambda_{10}\) & \(\lambda_{11}\) & \(\lambda_{12}\) & \(\lambda_{13}\) & \(\lambda_{14}\) & \(\lambda_{15}\) & \(\lambda_{16}\) \\ \hline \multirow{2}{*}{\(\lambda_{1}\)} & OOK & & 4.4 & 3.3 & 3.1 & 3.5 & 3.8 & 4.2 & 3.8 & 3.5 & 3.3 & 3.6 & 3.2 & 4.0 & 3.4 & 3.1 & 4.0 \\ \cline{2-19}  & DPSK & & 4.9 & 5.3 & 5.8 & 5.8 & 6.0 & 5.5 & 5.1 & 5.9 & 6.1 & 5.2 & 5.0 & 5.1 & 5.0 & 5.8 & 5.8 \\ \cline{2-19}  & OOK & 5.0 & 4.5 & 4.3 & 4.0 & 3.5 & 3.2 & 2.8 & & 3.3 & 3.7 & 3.4 & 3.6 & 3.7 & 3.5 & 3.4 & 3.4 \\ \cline{2-19}  & DPSK & 5.3 & 5.2 & 5.4 & 6.0 & 5.4 & 6.1 & 5.9 & & 6.0 & 6.0 & 5.8 & 5.8 & 5.2 & 5.1 & 5.1 & 5.5 \\ \cline{2-19}  & OOK & 5.3 & 5.1 & 4.7 & 4.2 & 4.0 & 4.1 & 3.8 & 4.0 & 3.5 & 3.6 & 3.7 & 3.5 & 3.1 & 2.9 & 2.6 \\ \cline{2-19}  & DPSK & 6.7 & 6.6 & 6.4 & 6.5 & 6.2 & 6.0 & 5.9 & 5.5 & 5.7 & 5.4 & 5.2 & 5.3 & 5.1 & 5.0 & 5.0 \\ \hline \end{tabular}
\end{table} 
Table 1: Absolute value of conversion efficiency. Figure 5: Bit error rate _vs._ receiver input power for 16 wavelength conversions: (**a**) OOK for conversion range from 0.8 nm to 12 nm, (**b**) DPSK for conversion range from 1.6 nm to 12.8 nm. Finally, we examine OSNR for conversion to \(\lambda_{9}\) (\(\lambda_{WC}\) = 1555.4 nm for OOK, \(\lambda_{WC}\) = 1555.7 nm for DPSK). We report measured output OSNR _versus_ the input OSNR in Figure 6a, observing a linear relationship between the input and output OSNR as expected. We also study the impact of the input OSNR degradation on the conversion efficiency. We observe a conversion efficiency degradation with decreasing input OSNR that is less than 2 dB for OOK and less than 1 dB for DPSK, as shown in Figure 6b.
# Causal Inference-Based Self-Supervised Cross-Domain Fundus Image Segmentation
## 4 Experiments
### Discussion on Domain Transformation Methods  
In our causal inference-based pseudo-label generation module, we employ the Fourier transform as the tool for domain style transformation, generating images with varying domain styles to simulate the stratification operation in backdoor adjustment. The primary rationale for choosing the Fourier transform is that, due to the large size of the original images and memory limitations, CycleGAN can only process compressed images, leading to a degradation in image quality; in contrast, the Fourier transform can directly handle the original images, thereby achieving a more effective style conversion. Moreover, as a deep learning model, CycleGAN's performance is constrained by the scale of the training dataset, and given the limited number of fundus images available, the Fourier transform can generate the required transferred images without additional training.  
To validate these points, we conducted comparative experiments using CycleGAN [38] as the domain transformation tool, comparing the results of the CycleGAN-based method with those of the Fourier transform-based method. As shown in Figure 9, although CycleGAN produces transferred images that are satisfactory in terms of domain style, noticeable differences in detail and contour exist compared to the original images. When converting target domain images to the source domain, the size of the OD is significantly enlarged. Further quantitative analysis supports this conclusion; as presented in Table 5, the overall model's segmentation performance on three datasets indicates that the Fourier transform achieves superior domain style transformation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Method** & \(\mathbf{DI_{cup}}\) & \(\mathbf{DI_{disc}}\) & \(\mathbf{\delta}\) \\ \hline \multirow{2}{*}{Drishti-GS} & Avg-pooling & 0.871 & 0.966 & 0.087 \\  & Ours & **0.876** & **0.971** & **0.081** \\ \multirow{2}{*}{RIM-ONE-r3} & Avg-pooling & 0.816 & 0.918 & 0.087 \\  & Ours & **0.818** & **0.922** & **0.083** \\ \multirow{2}{*}{REFUGE Val} & Avg-pooling & 0.861 & 0.947 & 0.053 \\  & Ours & **0.885** & **0.958** & **0.049** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of experimental results using different pseudo-label fusion methods. The best results are highlighted in bold.  
Figure 9: Visualization results of domain transformation using CycleGAN and Fourier transform
## 3 Results
### Prediction  
[PERSON] et al. [41] aimed to establish prediction models for evaluating the amputation and survival probabilities in patients with diabetic foot (DF). The dataset comprised 200 inpatients. Researchers included patients who exhibited clinical symptoms of DF, such as impaired circulation and peripheral neuropathy, and excluded those without complete clinical data. They compared three prediction models: one based on proportional hazard regression analysis (COX), one using a back-propagation neural network (BPNN), and another employing a BPNN optimized with a genetic algorithm. The findings reveal that BPNN-based models significantly outperformed the COX model and are more effective for clinical risk assessments. However, further research is needed to refine sample size requirements and address limitations in genetic algorithm complexity. [PERSON] et al. [42] analyzed the risk factors for DFU and amputations using statistical models and ML techniques on a dataset of 246,705 diabetic patients from Danish national registries [43]. They employed COX proportional hazards (PHs) and Aalen Johansen models to estimate hazard rates and account for competing risks like mortality, identifying key risk factors. Researchers used ML models, including logistic regression (LR) and random forest (RF), to predict DFU and amputation occurrences at different timeframes. The RF model outperformed LR, demonstrating higher classification accuracy in risk prediction. [PERSON] et al. [44] proposed the Extreme Learning Machine (ELM) and compared it with other machine learning models, including KNN, SVM with Gaussian kernel, and ANN, to predict foot ulcers. They used a dataset of 133 instances and 22 attributes sourced from the Figshare repository, focusing on the binary classification of DFU presence (positive or negative). After preprocessing the data for consistency, the ELM model, with a single
# Location Analysis for Arabic COVID-19 Twitter Data Using Enhanced Dialect Identification Models
###### Abstract  
The recent surge of social media networks has provided a channel to gather and publish vital medical and health information. The focal role of these networks has become more prominent in periods of crisis, such as the recent pandemic of COVID-19. These social networks have been the leading platform for broadcasting health news updates, precaution instructions, and governmental procedures. They also provide an effective means for gathering public opinion and tracking breaking events and stories. To achieve location-based analysis for social media input, the location information of the users must be captured. Most of the time, this information is either missing or hidden. For some languages, such as Arabic, the users' location can be predicted from their dialects. The Arabic language has many local dialects for most Arab countries. Natural Language Processing (NLP) techniques have provided several approaches for dialect identification. The recent advanced language models using contextual-based word representations in the continuous domain, such as BERT models, have provided significant improvement for many NLP applications. In this work, we present our efforts to use BERT-based models to improve the dialect identification of Arabic text. We show the results of the developed models to recognize the source of the Arabic country, or the Arabic region, from Twitter data. Our results show 3.4% absolute enhancement in dialect identification accuracy on the regional level over the state-of-the-art result. When we excluded the Modern Standard Arabic (MSA) set, which is formal Arabic language, we achieved 3% absolute gain in accuracy between the three major Arabic dialects over the state-of-the-art level. Finally, we applied the developed models on a recently collected resource for COVID-19 Arabic tweets to recognize the source country from the users' tweets. We achieved a weighted average accuracy of 97.36%, which proposes a tool to be used by policymakers to support country-level disaster-related activities.  
BERT models; dialect identification; location analysis; language identification; social networks +
Footnote †: journal: Medical and health information  
0000-0001-999-2270-2820][PERSON]  
1 The Engineering Company for the Development of Digital Systems, Giza 12311, Egypt; [EMAIL_ADDRESS]  
2 Faculty of Computers and Artificial Intelligence, Cairo University, Giza 12613, Egypt; [EMAIL_ADDRESS] (K.M.E.); [EMAIL_ADDRESS] (S.A.)  
3 Faculty of Engineering, Cairo University, Giza 12613, Egypt; [EMAIL_ADDRESS]  
4 College of Computer Sciences and Information Technology, King Faisal University, AlAlAsa 31982, Saudi Arabia; [EMAIL_ADDRESS] (S.K.); [EMAIL_ADDRESS] (M.M.H.); [EMAIL_ADDRESS] (A.A.); [EMAIL_ADDRESS] (M.A.A.).
## 3 Experiments and Analysis  
### Experimental System  
Using the experimental platform shown in Figure 3, we measured the 3D shape of many measured objects with the proposed method to verify its feasibility and validity in the actual measurement process. In this experimental platform, the digital light projector is PLED-W20 produced by ViewSonic, and the color camera is DFM 72 AUCO2 with the imaging resolution of 1024 \(\times\) 768 pixels.
## 3 Analysis and Validation of the Physical Model
### Evaluation of the Physical Model in the Flow Rate Calculation Algorithm  
For both PO001 and PO002, the flow rate was calculated by using the physical model in accordance with Algorithm 2 (outlined in Section 2.3). The flow calculation algorithm was applied to the time signals of the measurements. The average values of the time signal results are shown in the following figures. Figure 6a depicts the error \(|Q-Q_{calc}|\) for PO001, while Figure 6b represents the same for PO002. In the spectrogram on the left-hand side, the flow rate was calculated by using the physical model, whereas the spectrogram on the right-hand side was constructed based on using the polynomial approximation and the affinity laws. Within the range \(Q=100-120\) m\({}^{3}\)/h at \(n\approx 1200\) min\({}^{-1}\), PO001 exhibits a maximum error of \(|Q-Q_{calc}|=5\) m\({}^{3}\)/h. In the calculation, which was developed using the affinity laws, the maximum deviation also appears at this speed but in the range of the maximum flow rate. In general, the relative error for PO001 using the physical model and the affinity laws amounts to approximately 3%. For PO002, the calculation of the flow rate over the whole range shows a small error of \(|Q-Q_{calc}|<2\) m\({}^{3}\)/h. The average relative error from the calculation with the physical model and the affinity laws is about 1.5% for PO002.  
Figure 6: (**a**) Spectrogram of the error \(|Q-Q_{calc}|\) versus flow rate and speed for PO001; (**b**) Spectrogram of the error \(|Q-Q_{calc}|\) versus flow rate and speed for PO002.  
Figure 7 shows the errors \(|Q-Q_{calc}|\) for calculating the flow using the physical model for PO001 (Figure 7a) and PO002 (Figure 7b). The flow rate (\(Q\)) was plotted on the y-axis, and the prewhirl angle (\(\Delta\alpha_{1}\)) was plotted on the x-axis--both were varied. For PO001, the average relative error in calculating the flow rate is 1.6%. The average relative error for PO002 is 3.9%. In contrast to PO001, PO002 exhibits increased errors in flow rates at \(Q<50\) m\({}^{3}\)/h for prewhirl angles \(\Delta\alpha_{1}\
eq 0^{\circ}\). As mentioned in Figure 5c, the recirculation power is influenced by prewhirl bodies, which is not represented in the physical model. For PO001, an unstable characteristic curve occurs from a prewhirl angle of \(\Delta\alpha_{1}>15^{\circ}\), and for PO002, the same occurs from a prewhirl angle of \(\Delta\alpha_{1}>20^{\circ}\). The calculation of the flow rate becomes ambiguous in this case, as mentioned in [2; 5].
## 2 Materials and Methods
### The SCADA Data and the Loss of Information Due to Averaging  
Replacing the instantaneous values of a variable by a value averaged over a period introduces an error. Therefore, in general, the error due to averaged values is expected to increase as the period of averaging of the data increases. This error is also likely to be highly dependent on the variability of the data. The main factor in this uncertainty is the variability of wind speed and direction, and the frequencies contained in the signals. Therefore the averaging can be crucial if the signal has frequencies higher than the sampling/averaging, or of little relevance if it hardly varies at all in an averaging period.  
The autocorrelation of the signals involved will bring meaningful information. For example, if a signal has very low autocorrelation for certain time lags, this implies a very little statistical similarity between samples separated by these time lags. Thus, averaging over a similar period of time results in the signal significantly losing information. On the other hand, a signal with a high autocorrelation for a given time lag will suffer a minimal loss of information when averaged over periods close to such time lag. This dependence of the quality of SCADA data on its autocorrelation and the averaging period has already been studied previously in [11] and specially in [8], where a study with high-frequency SCADA data of a wind farm is presented, reporting that the autocorrelation of the variables shows a clear pattern consisting of a drop that fits an exponential that depends on the time of averaging. The exponential slope differs for each variable; for instance, the most pronounced is observed in the generated active power. In general, significant information losses due to 5-min aggregations, more commonly presented in SCADA systems, are reported especially in the following four variables: The wind speed, the orientation, the main rotor speed, and, as previously mentioned, the generated active power. In these cases, the normalized autocorrelation is practically zero in time lags of five minutes onwards.
# Efficient Next-Hop Selection in Multi-Hop Routing for IoT Enabled Wireless Sensor Networks
## 2 Related Work
In LEACH Vice Cluster Head (L-VH) [32], an additional sub \(CH\) with second highest energy is identified to manage the workload. The VH does not work in parallel with \(CH\), but it sleeps unless a sufficient threshold amount of energy is consumed by \(CH\). After that, VH wakes up to take over the tasks in progress at \(CH\) and then continues to work as \(CH\). It reduces the time for the next head selection, and the number of messages are processed by VH to manage the communication. In [33], Traffic and Energy Aware Routing (TEAR) is presented for selecting those nodes that have a high traffic rate with less energy dissipation. In this scenario, TEAR does not provide redundant data filtration. To attain both benefits of redundant data filtration and heterogeneity-based data trafficking with efficient data consumption in the WSN scenario, a hybrid method, ETASA, provides enhanced load balancing with efficient energy utilization of sensing devices, and these devices wake and sleep based on energy and data rates. SEED also allots a slot to each member of the pair group, and the presented scheme amends the conventional scheduling of SEED by allotting one slot for each group pair within a cluster. In this way, the idle listening problem is addressed to reduce energy dissipation. ETASA improves the balanced energy dissipation and \(CH\) selection technique.