esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyzing TCP Performance in High Bit Error Rate Using Simulation and Modeling
## 6 Conclusions and Recommendations  
In this paper, we analysed TCP performance in a high bit error rate (BER) using simulation and modelling. Results obtained have shown that TCP throughput collapses for \(p_{drop}=10\%\). The problem is that TCP was not designed to handle such high BER wireless environments. The assumption of the TCP algorithm (fast recovery) is that packet loss caused by damage is very small (much less than 1%) [47]. Fast recovery algorithm is not good in recovering multiple dropped packets from the same packet streams [6]. This is because the receiver must wait for the TCP timeout of the sender. The waiting for the timeout brings much higher delays than the fast retransmission algorithm. Therefore, it is no surprise that both the simulation and the emulation show a much longer download time at \(p_{drop}=10\%\) than any other scenarios. For instance, FTP download response time is about 32 times longer than that of a perfect channel (no packet dropping). The findings reported in this paper provide some insights into TCP performance in high BER wireless and mobile network environments, which can help network researchers and engineers contribute further towards the development of smart transport layer protocols for the next-generation Internet. However, analyzing TCP performance over the Internet of Things for life-saving emergency applications is suggested as future work.  
In this paper, we provide three specific recommendations. Specifically, network researchers and engineers should: (1) avoid the combination of a very high noisy channel (BER \(>10^{-6}\)) and TCP; (2) employ either an optimized TCP or other transport layer protocols for wireless connections; and (3) design a new transport protocol, e.g., Datagram Congestion Control Protocol (DCCP, RFC 4340), or leave the task of a reliable end-to-end connection to the application layer by choosing the User Datagram Protocol. Adopting these recommendations will help network researchers and engineers to produce better TCP performance in practice, especially in wireless environments.  
Conceptualization, R.A. and N.I.S.; methodology, R.A. and N.I.S.; software, N.I.S.; validation, R.A. and N.I.S.; formal analysis, R.A. and N.I.S.; investigation, R.A. and N.I.S.; resources, N.I.S.; data curation, N.I.S. and R.A.; writing--original draft preparation, R.A.; writing--review and editing, S.M.S.Z.; visualization, R.A.; supervision, N.I.S.; project administration, N.I.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest. We also confirm that our paper has not been previously published and is not currently under consideration by any other journals. All authors involved have approved the contents of this paper and have agreed to the Journal of MDPI Electronics submission policies.
# Performance Assessment and Comparison of Deployment Options for 5G Millimeter Wave Systems
## 4 Numerical Results  
In this section, we will elaborate our numerical results by assessing the performance of different 5G mmWave deployment options: (i) conventional BS-only based, (ii) terrestrial IAB deployment, and (iii) airborne IAB deployment utilizing UAV as IAB nodes. As metrics of interest, we utilize the coverage of a single BS, spectral efficiency, and the required BS deployment density.  
The default system parameters are provided in Table 2. We consider links from the aerial IAB to the UE to use the UMa scenario, while other links use the UMi scenario. This consideration is due to the fact that the aerial IAB is located at a high altitude and, thus, is located above rooftop level, while the BS, terrestrial IAB, and UEs are all located close to ground level in urban areas. The scenario parameters are given in Table 3.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline \(\alpha_{0}\) & 3.19 & NLoS building blockage coefficient \\ \(\alpha_{1}\) & 2.1 & LoS building blockage coefficient \\ \(\beta_{0}\) & 5.24 & NLoS human blockage coefficient \\ \(\beta_{1}\) & 3.24 & LoS human blockage coefficient \\ \(f\) & 28 Ghz & Carrier frequency \\ \(P\) & 2 Wt & Transmitted signal power \\ \(SNR_{marg}\) & \(-9.478\) dB & Signal-to-noise ratio margin \\ \(SCM\) & 3 dB & Shannon capacity margin \\ \(P_{0}\) & 0.1, 0.01 & Outage probability \\ \(loss_{C}\) & 2 dB & Cable loss \\ \(NF_{UE}\) & 7 dB & Noise figure due to UE configuration \\ \(r_{0}\) & 1 km & Base radius \\ \(h_{BS}\) & 10 m & eNB height \\ \(h_{Terr}\) & 10 m & Terrastial re-transmitter height \\ \(h_{UE}\) & 1.3 m & User equipment height \\ \(h_{UAV}\) & 100 m & UAV re-transmitter height \\ \hline \hline \end{tabular}
\end{table}
Table 2: System parameters.  
The rest of this section is organized as follows: We start with an assessment of the outage probability and spectral efficiency for urban deployment conditions. Then, we compare the coverage areas and BS placement density necessary to cover a given area for all options.
## 4 Experimental Results
### Data Imbalance
#### 4.6.2 SeqGAN: Malware Family Augmentation  
To achieve a balanced dataset, an \(n\)-amount of samples were added for each class, as shown in Table 2. The quality of the synthetic samples generated by SeqGAN was evaluated by the BLEU (Bilingual Evaluation Understudy) score metric. The BLEU metric assesses the similarity between the generated samples and real samples by comparing overlapping n-grams. The generated samples are not intended to perform actual malicious activities but rather serve as data to address class imbalance. Therefore, similarity was measured simply as the degree of word overlap.  
We compared 1 gram of the generated and real samples, motivated by the nature of malware scripts, where individual commands and API calls are the fundamental units of malicious functionality. The generated samples achieved higher BLEU-1 scores. A higher BLEU-1 score indicates a successful replication of malicious samples. This quantitative validation demonstrates that our synthetic samples effectively capture the essential characteristics of script samples. In Table 6, we report the BLEU-1 scores for the generated samples. A lower score on EPS can be attributed to the architectural complexity and payload flexibility inherent in EPS malware. The combination of a PostScript code with binary data introduces more variability in patterns compared with the simpler structure of iframe and VBS scripts. Additionally, the flexibility of EPS files, which can embed various types of malicious content, such as shellcode or executables, leads to greater variation between samples.  
Although these generated samples may not represent actual malware, they contribute to enhancing the model's robustness when combined with the original samples. After data augmentation, the F1 score increased by 35.58% for the VBA class, which was the most inadequate class, and improved for other classes, as shown in Table 7. Compared with CNN, the Transformer model performs better before and improves after data augmentation. This suggests the possibility of using a Transformer model to overcome the limitations of unbalanced data. We leave this for future analysis study.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Malware Family** & **BLEU-1** \\ \hline EPS & 79.12 \\ Iframe & 99.03 \\ VBS & 97.45 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of BLEU for the augmented malware family.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Malware Family** & **BLEU-1** \\ \hline EPS & 79.12 \\ Iframe & 99.03 \\ VBS & 97.45 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance results (using MalCode2 Vec embedding) without and with data augmentation. P, R, and F1 are precision, recall, and F1 score.
## 6 Model Validation and Performance Evaluation  
In this section, we use the theoretical models formulated in Section 5 to calculate the average delay and energy consumption for RFSA under delta traffic conditions. We first describe the considered scenario. Then, we analyze how the delay and energy consumption are influenced by the number of slotsper frame and the number of end-devices. We discuss the numerical results and determine the criteria to minimize the average delay and energy consumption. In addition, we compare the performance for RFSA and FSA.
## 2 Data and Methods
### Model Descriptions  
#### 2.3.1 The AVM  
The AVM model developed by [PERSON] _et al._[12] is based on the EVI and air temperature. An important finding is that daily variance of GPP in alpine meadows during the growing season can be expressed in terms of EVI and air temperature. AVM has only been tested in alpine grasslands and the model calibration in the other ecosystems is still unknown. The AVM is expressed as:  
\[\mathrm{GPP=m\times EVI_{scaled}\times T_{scaled}} \tag{4}\]  
where \(m\) is a conversion coefficient with the unit of gC/m\({}^{2}\)/eight-day, and EVI\({}_{scaled}\) is given by:  
\[\mathrm{EVI_{scaled}=EVI-EVI_{base}} \tag{5}\]  
where EVI\({}_{base}\) is set to be 0.15 [12]. When EVI is less than EVI\({}_{base}\), EVI\({}_{scaled}\) is set as 0. T\({}_{scaled}\) is the air temperature stress factor:  
\[\mathrm{T_{scaled}=(T_{a}-T_{min})/(T_{max}-T_{min})} \tag{6}\]  
where T\({}_{a}\) is the mean air temperature for each eight-day period; T\({}_{min}\) and T\({}_{max}\) are the biological minimum and maximum temperature during the growing season, respectively (Table 2). If T\({}_{a}\) is less than T\({}_{min}\), T\({}_{scaled}\) is set as 0.
###### Abstract  
Jasmonates have emerged as a prominent elicitor for enhancing trichome development and cannabinoid production in _Cannabis sativa_ L. (cannabis). These glandular trichomes synthesize and store important cannabinoids, including tetrahydroccannabinol (THC) and cannabidiol (CBD), which determine the yield, potency, and quality of cannabis flowers. Methyl jasmonate (MeJA) acts through the COII-JAZ-MYC signaling pathway to upregulate genes associated with trichome initiation and cannabinoid precursor formation. Evidence suggests that moderate MeJA concentrations (typically 50-100 m) can effectively boost trichome density, elevate hexanoyl-CoA availability, and modestly enhance key biosynthetic enzyme activities, ultimately increasing THC and CBD content. However, higher methyl jasmonate doses can amplify these benefits, yet pose a risk of excessive vegetative stunting, highlighting the crucial trade-off between enhancing cannabinoid potency and maintaining overall biomass yield. Interaction with hormones like gibberellins, salicylic acid, and ethylene further shapes the plant's stress responses and secondary metabolism. Application in controlled environments, such as greenhouses or vertical farms, shows promise for enhancing resin production while minimizing biomass loss. In outdoor conditions, the application may offer additional defense benefits against pests and pathogens. These responses can vary depending on the cultivar, underscoring the importance of cultivar-specific optimization. As demand for high-cannabinoid cannabis products continues to grow and agrochemical options remain limited, leveraging MeJA treatments offers a practical, non-genetically modified approach to optimize yield, quality, and resilience in cannabis cultivation.  
plant hormones; secondary metabolite; hemp; specialty crops; medicinal plant; methyl jasmonate; jasmonate acid +
Footnote †: journal: Journal of Plant Biology  
1
# High-Speed VCSEL-Based Transceiver for 200 GbE Short-Reach Intra-Datacenter Optical Interconnects
## 2 Materials and Methods
### Experimental Setups
#### 2.2.2 Experimental Setup for the Transceiver Link Experiment  
The experimental setup used for evaluating the performance of the full link is shown in Figure 5.  
Two single-ended outputs of an 8 bit, 65 GSa/s keysight AWG with a repeating pattern length of \(2^{9}-1\) symbols generated two identical NRZ electrical signals of 500 mV peak-to-peak to feed the respective MSB and LSB AC-coupled inputs of the VCSEL board. To decorrelate the NRZ tributaries used for generating PAM-4, a delay difference of 10 bits was introduced between the two AWG outputs. The baud rate was varied to investigate the link's performance under different data transmission scenarios. Pulse shaping to restrict the bandwidth of the baseband signal was performed  
Figure 4: The experimental setup used for the high-speed single-mode VCSEL experiment.  
Figure 5: The setup that was used for the full-link experiment.  
by implementing a raised cosine electrical filter with a roll-off factor of a = 1. A 40 GHz Anritsu signal generator phase locked to the AWG provided the full-rate clock reference for the transmitter board. The generated PAM-4 optical signal was transmitted through a 500 m and 2 km link of SSMF to the linear receiver board. To investigate the dependence of the BER to the incident optical power in the linear receiver, an erbium-doped fiber amplifier (EDFA) with a 28 dB gain, a 13 dBm saturated output power, and a noise figure of approximately 3.8 dB was employed along with a variable optical attenuator (VOA). The BER assessment was performed by exploiting the equipment Infiniium DSO-X 93304 real-time oscilloscope (Keysight Technologies, Santa Rosa, CA, USA) and the offline processing is described in Section 2.2.1.
###### Abstract
In recent years, researchers have used new methods for analyzing hydraulic performance. These methods include artificial neural networks (ANNs), gene expression programming (GEP), the genetic algorithm (GA), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) methods. So far, relatively extensive research has been conducted using the above methods, including the following:  
[PERSON] et al. [13] used the ANN model and linear regression to predict the amount of sediment in water flows. They were able to establish a relationship between four parameters that affect the amount of sediment and sediment density using the two methods. [PERSON] and [PERSON] [14] used two types of artificial neural networks Feed Forward Back-Propagation (FFBP) and Radial Basis Functions (RBF) and compared the results with a multiple linear regression. They concluded that the neural network provided more accurate results compared to linear regression. [PERSON] and [PERSON] [15] used field and laboratory data to investigate the potential of SVM for predicting scour depth and showed that changes in flow conditions, geometry and substrate materials affect scour depth. [PERSON] et al. [16] evaluated the efficiency of SVM for predicting hydraulic jump parameters in a sudden divergence. The results showed that the relative energy dissipation and the ratio of conjugate depths with the Froude number had the best performance predictive performance. [PERSON] et al. [17] studied the scouring of supercritical current jets upstream of lattice plates in experiments using artificial intelligence methods. In that research, Sugeno fuzzy logic (SFL), neuro-fuzzy (NF) and support vector machine (SVM) methods were used. [PERSON] et al. [18] investigated a vertical drop equipped with double screens using SVM. The results showed that the parameters of the vertical drop correspond very well with the output results of the support vector machine. [PERSON] et al. [19] investigated the discharge coefficient of trapezoidal labyrinth weirs using ANN and SVM. The results showed that both methods had good accuracy for estimating discharge coefficients. [PERSON] et al. [20] predicted longitudinal dispersion coefficients in natural rivers usinga cluster-based Bayesian network. Their results showed that a dimensionless bayesian network (BN) model resulted in a 30% reduction of the root mean square error. The accuracy criterion increased from 70 to 83% by performing clustering analysis based on the BN model. One of the parameters that can be considered in the design and operation of systems with horizontal screens is the aperture diameter of these screens. The effect of an aperture diameter with fixed porosity has not been investigated using artificial intelligence methods such as the support vector machine method. Considering the effect of diameter size of screens on flow hydraulics has not been investigated so far; thus, we consider three different diameters of a constant porosity screen and the effect of this parameter on vertical hydraulic parameters is investigated using SVM.
## 4 Experimental Evaluation
### Dataset  
We used BigCloneBench for training and testing our model. BigCloneBench [15] is a real-world benchmark that contains over 6,000,000 tagged clone pairs across 43 functionalities, as well as 260,000 false clone pairs. BigCloneBench specifies a clone pair by triple, including a pair of similar code fragments and their clone-type. It contains both intra-project and inter-project clones of all four clone-types. Depending on the similarity ratio, these clones and clone-types have all been manually evaluated. BigCloneBench divides T3 and T4 clones into four categories: Very-Strongly Type 3 (VST3), Strongly Type 3 (ST3), Moderately Type 3 (MT3), and Weakly Type 3+4 (WT3/4).  
Figure 3: Example code fragments of a T4 type and non-clone type.  
BigCloneBench's T1 clone refers to syntactically identical code fragments, excluding differences in white space, layout, and comments identified after applying T1 normalization. T2 refers to syntactically identical code fragments, excluding differences in identifier names and literal values, in addition to Type-1 clone differences identified after applying T2 normalization. T1 normalization refers to annotation removal and pretty-printing, while T2 normalization includes the addition or modification of identifier names and literal values, in addition to T1 normalization. BigCloneBench's ST3, MT3, and WT3/4 clones were classified by measuring similarity as the minimum ratio of lines or token code fragments shared after the T1 and T2 normalizations were applied. As there is no consensus on classification criteria of a T3 clone or T4 clone, BigCloneBench defines the range of similarity of each clone-type to separate clones because of these types. ST3 clones are those clones with a similarity in the range of 70% (inclusive) to 90% (exclusive), and most syntactic clone detection tools use this criterion. MT3 clones refer to clones with a syntactic similarity of between 50 and 70%. Conventional syntactic clone detection tools classify MT3 clones as either a T3 clone or T4 clone. The final WT3/4 are those clones with a syntactic similarity of less than 50%, and most clone detection tools do not detect this type.  
To compare the performance of our method with existing tools, we used the BigCloneBench's dataset (Table 1). The selected dataset consists of 59,618 individual code fragments, paired in two to form 97,535 code pairs, including 20,000 false clone pairs. Clone pairs consist of 15,555 T1 clones, 3663 T2 clones, 18,317 ST3 clones, 20,000 MT3 clones, and 20,000 WT3/4 clones. However, the number of clone pairs of each clone-type is not balanced. Specifically, the number of T2 clone pairs is significantly less than other types. Because imbalanced datasets produce biased training results in classes with multiple instances, we conducted sampling from the BigCloneBench dataset to obtain an appropriate dataset for training and testing our technique.
## 5 Perspectives on Studying Pathological and Treated Bone at the Nanoscale
Yet, considering how much bone bioimaging has advanced in the past few decades with the emergence of novel imaging techniques (such as correlative light electron microscopy, CLEM) as well as new knowledge about bone and bone cells, fresh insights and more systematic investigations are needed. One question might be: how is the process of mineralization mediated by intracellular phosphate-containing vesicles altered in diseased bone (such as in OC-OPN double-deficient mice), and how would this be implicated in the mineralization pattern and its extent in bone [15]? Finally, there appears to be incomplete quantitative analysis of the SEM or S/TEM images in the literature, even in recent reports. For example, whilst many have mapped and quantified the misorientation angle of mineral nanocrystallites in the bone matrix, few have reported the same for disorganized and misaligned collagen fibrils, common in many bone pathologies. Possible metrics include distribution of the fibril length; quantitative changes to the D banding, gap zone, and overlap zone of collagen; and mapping of the misorientation angle of the fibril assemblies. Quantification of these altered ultrastructural details can be helpful for comparison between healthy and diseased bone, between different pathological states, or even for correlation with mechanical properties (e.g., bone toughness as a function of the degree of fibril misalignment).
# Biofilm Lifestyle in Recurrent Urinary Tract Infections
## 6 Strategies for Eradication of Biofilms
### Drug Repurposing  
Drug repurposing is a new strategy that has attracted the interest of researchers. This approach involves the screening of already-used and -approved medications for therapeutic applications other than their original indications. This approach offers numerous advantages, including: (i) discarding the need for clinical trials and authorization from health organizations since these agents are already approved and tested for their safety; (ii) decreasing the cost of treatment because the same medication can be used for multiple therapeutic indications; and (iii) identification of active chemical groups which allows for synthesis of chemical derivatives with reduced side effects [195]. Drug repurposing has attained a lot of attention in our research group over the years, where we were able to publish numerous promising results on the subject. Some of the screened agents showed noticeable antimicrobial activities which, when used at sub-inhibitory concentrations, revealed significant reduction of QS and biofilm formation. Some of the first medicinal drugs screened in our team were the group of \(\alpha\)- and \(\beta\)-adrenergic blockers that were originally used for treatment of cardiovascular diseases [131; 132]. However, after screening for their antimicrobial effects, the results revealed potent anti-QS and anti-biofilm activities. Drugs such as terazosin, doxazosin, prazosin, atenol, and timolol showed significant reduction of the biofilms formed by _E. coli, P. mirabilis, P. aureginosa, Salmonella enterica_, and _S. marcescens_[131; 132; 136; 135]. Gliptins were another group of drugs originally used as antidiabetic agents--they act as dipeptidase inhibitor (DPI-4). Stagliptin is a member of this group that showed significant inhibition of _S. aureus_, _P. aureginosa_ and _S. marcescens_ biofilms [144; 145; 146; 62; 196]. Notably, both adrenergic blockers and gliptins inhibited biofilm formation by targeting the QS system and downregulation of its encoding genes. Furthering this avenue of inquiry, different chemical moieties of drugs were screened for their anti-biofilm activities [129; 198; 199]. Other drug groups were also screened, such as anti-inflammatory agents, mucolvtics and diuretics, with many members showing interesting results. Some of these results are presented in Table 1.