esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis
### Experimental Configuration  
In this work, we employed the Adam optimizer with weight decay as the primary optimization method. The Adam optimizer was chosen for its adaptive moment estimation properties, which help accelerate convergence by adjusting the learning rate for each parameter based on the first and second moments of the gradients. The learning rate was initialized at 3 \(\times\) 10\({}^{-3}\), and weight decay was set to 1 \(\times\) 10\({}^{-4}\) to prevent overfitting by applying L2 regularization to the model parameters.  
To improve convergence and avoid the risk of becoming stuck in local minima, we adopted the Cosine Annealing Warm Restarts strategy for the learning rate. This scheduler reduces the learning rate in a cosine fashion during each cycle, with periodic restarts to explore a wider range of solutions. We set the initial period, period multiplier, and minimum learning rate to 30, 3, and 1 \(\times\) 10\({}^{-5}\). This learning rate strategy is designed to enable fast exploration during early epochs, while facilitating more fine-grained convergence in later stages of training through periodic restarts and learning rate fluctuations.  
To evaluate the classification performance of the 3D object recognition model, two standard metrics were adopted [20]: the average class accuracy, defined as the mean classification accuracy over all categories, and the average instance accuracy, defined as the overall accuracy across all test samples.
# Phytomicrobiones: A Potential Approach for Sustainable Pesticide Biodegradation
## 5 Future Research Scope  
The advent of synthetic microbial communities (SynCom) stands as a beacon of promise in the realm of microbiome research, opening new avenues for exploration. This innovative technology entails the co-cultivation of diverse microbial taxa under controlled conditions, effectively mimicking the functional and structural attributes of a natural microbiome. The intrinsic complexity of pesticide compositions often limits the efficiency of natural pesticide degradation. In contrast, synthetic communities offer the potential for enhanced biodegradation outcomes [167]. A case in point involves _Variovorax_ spp. WDL1, which efficiently degrades liunuron by utilizing it as a source of carbon and nitrogen to support its growth. Conversely, _Pseudomonas_ spp. WDL5 and _Delfatia acidovorans_ WDL34 produce intermediate metabolites during liunuron degradation but cannot fully degrade the compound. Remarkably, when these three strains are combined to create a synthetic community, it exhibits the highest degradation efficiency due to the synergistic interactions between the strains [167]. Another instance highlights the degradation of 4-chlorosalicylate, which occurs only when _Achromobacter spanius_, _Pseudomonas reinekei_, _Wautersiella falsenii_, and _Pseudomonas veronii_ strains are co-applied [168]. Given the propensity of plants to harbor a multitude of degradable microbes in pesticide-exposed environments, SynCom can be crafted by isolating endophytic pesticide-degrading microbes, thereby promoting bioremediation in pesticide-contaminated soil.  
Undoubtedly, SynCom presents a promising tool for future sustainability; however, a deeper understanding of microbiome interactions among communities is essential to unlock its full potential. The dynamics of synthetic communities in association with plants remain an enigma that requires a comprehensive exploration of their role in pesticide biodegradation and bioremediation. The deployment of CRISPR-Cas9 technology, a revolutionary genomic tool, allows for targeted genetic screening, thereby elucidating the intricacies of the genotype-phenotype relationship. Through the systematic perturbation of genes on a broad scale, this technology holds promise in investigating plant-microbe interactions post-pesticide application. Such insights are invaluable for building a comprehensive database elucidating the mechanisms through which the phytomicrobiome fosters the growth of pesticide-degrading microorganisms [169]. While techniques like the super delta method can aid in data normalization during gene expression analysis, a more profound genetic-level comprehension of phytomicrobiome interactions in pesticide-treated soils is imperative to pave the way for future breakthroughs.
## 5 Key Technologies Supporting Deliberate Attack Defense for IECPS
### Cyber Security of IECPS
#### 5.4.1 Data Encryption and Processing  
By leveraging wide-area measurement technology and agile control strategies, IECPS achieves safe, stable, and efficient operation. During this process, vast amounts of operational data are collected, transmitted, stored, and processed across various hierarchical levels of the system. To prevent operational data from being eavesdropped or tampered with, data encryption serves as a crucial defensive measure against potential attacks.  
Data encryption refers to the process of converting plaintext data into ciphertext, which cannot be directly recognized or read, using specific encryption algorithms. During transmission or storage, unauthorized personnel cannot directly access, steal, or tamper with the encrypted data; only the recipient holding a valid key can decrypt the ciphertext back to plaintext. However, performing encryption and decryption on massive amounts of data can affect the speed of information computation and processing, greatly impacting the agility of system control, which in turn compromises the system's security and stability. To address this issue, less sensitive data can be selectively deleted, or only sensitive data can be encrypted. Additionally, encryption methods that match hardware accelerators are used to speed up the encryption process of sensitive data, thereby balancing real-time requirements and security control.  
To achieve wide-area measurement and agile control in IECPS, a significant number of internet of things (IoT) and embedded devices are deployed at the terminals of the physical system. These devices, with their low power consumption and weak computing capabilities, require higher adaptability for encryption and processing technologies. Lightweight encryption technology is a data encryption method designed specifically forresource-constrained devices, requiring very low computational power and energy consumption, and operates at high speed [211]. Common lightweight encryption algorithms include symmetric encryption, stream cipher algorithms, hash algorithms, public-key encryption algorithms, etc. By flexibly selecting the encryption algorithm based on the scenario limitations, the system's defense capabilities can be maximized.  
In recent years, quantum encryption technologies based on the \"uncertainty principle\" and the \"no-cloning theorem of single quantum\" have begun to be implemented in the business scenarios of energy systems. Among them, quantum encryption methods represented by the BB84 quantum key distribution protocol have been initially piloted in small-scale trials in scenarios such as distribution automation, distribution network protection, new energy monitoring, and transmission line monitoring [67].
## 5 Discussion and Future Opportunities
### Transferability  
In landslide susceptibility assessment, the portability of models refers to their applicability and effectiveness across different regions or geographical conditions. Investigating model portability ensures their reliability and utility in various environments. Factors influencing model portability include geographical variations, data quality, and features, as well as the complexity of the models. Future research can focus on the following areas to enhance model portability:  
1. Building Cross-Regional Datasets: Creating datasets that cover multiple regions can train more generalized models, thus improving their applicability across different areas.
2. Transfer Learning: Applying transfer learning techniques allows models to quickly adapt to new regions, thereby enhancing their stability and robustness.
3. Ensemble Learning: Combining multiple models through ensemble learning can leverage the strengths of each model, boosting overall performance.
4. Factor Importance Analysis: Analyzing the importance of various factors in the models can help identify and understand the key elements affecting landslides in different regions, thus improving model applicability.
5. Model Interpretability Research: Enhancing model interpretability helps in understanding the decision-making processes and making necessary adjustments when applying models to new regions.  
Despite these strategies, challenges such as data availability, regional variations, model complexity, and validation issues still persist. Future research needs to delve deeper into these areas to promote the widespread application and continuous improvement of landslide susceptibility assessment models, providing strong support for landslide prevention and disaster mitigation efforts.
# Optical OFDM Error Floor Estimation by Means of OTDR Enhanced by Front-End Optical Preamplifier
## 2 Analysis
### OFDM BER Floor Model  
As it is challenging to uniquely relate the OTDR trace events (primarily the reflective ones) to the parameter(s) determining the OFDM BER error floor under high-SNR conditions and therefore dominant time dispersion, which implies that we can apply the m-subcarrier OFDM BER floor model [10], specifically when sampling time is just upon the arrival of the first impulse of the channel impulse response [2].  
So, we consider the power-delay profile as the sum of \(N\) impulses with powers \(A_{i}^{2}\), and delays \(\tau_{i}\), \(i=1,2,\ldots N\)[10].  
Then the BER floor is:  
\[BER\approx\frac{k_{MOD}}{2\sqrt{\pi\cdot m}}\cdot\frac{\sqrt{E[\tau^{2}]}}{T_{ s}}=\frac{k_{MOD}}{2\sqrt{\pi\cdot m}}\cdot\frac{\sqrt{\sum\limits_{i=1}^{N}A_{i}^ {2}\tau_{i}^{2}}}{T_{s}} \tag{1}\]  
where \(\frac{\sqrt{E[\tau^{2}]}}{T_{s}}\) is the standard rms delay spread (normalized to the symbol time \(T_{\text{S}}\)) of the channel delay power profile (describing time dispersion), whereas the modulation  
Figure 4: Dominant reflective events identified by OTDR.  
coefficient \(k_{\text{MOD}}\), is given by the ratio of the symbol error rate (SER) for the M-QAM modulation to BER:  
\[k_{\text{MOD}}=\frac{\frac{SER_{\text{MBQM}}}{SER_{\text{BPSK}}}}{ldM}=\left\{ \begin{array}{c}2/2=1;\ \text{4 QAM}\\ 3/4=0.75;\ 16\text{QAM}\\ 3.5/6=0.583;\ 64\text{QAM}\end{array}\right. \tag{2}\]  
However, in this paper, with no loss in (OFDM) generality, we consider simple binary transmission, and therefore adopted that \(k_{\text{MOD}}=1\), so that (1) simplifies to:  
\[BER\ \approx\ 0.28\ \ \frac{\sqrt{E[\tau^{2}]}}{T_{s}} \tag{3}\]  
Furthermore, as for the contemporary transmission networks including not only fiber links but the microwave ones, too, the bit-oriented transmission performance standards have long ago been replaced with the data block-oriented ones [10], let us now derive the Block Error Ratio (BLER) from BER estimated by (3) [10,11]:  
We can justifiably assume that, although the common binomial distribution statistically well describes mutually independent bit error occurrences within an \(L\)-bits-long data block, in this case, we consider that the appropriate error generating model should still preserve (moderate) mutual dependability among the individual bit error occurrences. This conforms to the statistical model of sampling without replacement, well described by the hypergeometric distribution of errors within an erroneous data block (containing one or more erroneous bits), which provides the following _BLER_ vs. _BER_ relationship [10,11]:  
\[BLER(BER)\approx 1-(1-BER)^{L} \tag{4}\]
## 5 Conclusions  
The data obtained confirmed the antifungal activity of _B. velezensis_ CT32 towards eight plant pathogens and indicated the pivotal role of VOC production in antagonist-pathogen interactions. GC-MS analysis of the bacterial volatile profile suggested the contribution of ketones, alcohols, aldehydes, phenols, nitrogen compound and thiazole to the antifungal property of strain CT32 emissions, which was verified by in vitro assays where individual chemical compounds were tested against the mycelial growth of _V. dahliae_ and FOF. Among these compounds, benzothiazole and 2,4-dimethyl-6-tert-butylphenol showed the strongest antifungal activity. These findings will hopefully provide lead compounds for the development of new antifungal agents. Although further studies are required to evaluate the efficacy of the volatile-producing strain CT32 for the protection of strawberry plants, it shows potential to be used as a biofumigant for the control of vascular wilt diseases.  
**Author Contributions:** Conceptualization, X.L. and S.Z.; methodology, X.W.; validation, B.W., Q.W. and M.L.; formal analysis, X.L.; investigation, M.L.; resources, S.Z.; data curation, Q.W.; writing--original draft preparation, X.L.; writing--review and editing, S.Z.; supervision, X.S.; project administration, B.W.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Key Research and Development Program of Shanxi Province, grant number 201703D211022; the Postdoctoral Foundation of Shanxi Academy of Agricultural Sciences, grant number YCX2018D2 BH1 and the Agricultural Science and Technology Innovation Programs of Shanxi Academy of Agricultural Sciences, grant number YGC2019 TD07, YCX2020 YQ24.  
**Acknowledgments:** The authors would like to acknowledge everyone who has provided helpful guidance.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Experiments
### The Result of 3D Pose Refinement  
We evaluated the applicability of our proposed model in 3D coordinates using images from the 3 DPW dataset where only a single person appears. Table 2 presents our experimental results. The data used for evaluation include \"courtyard_bodyScannerMotions_00\", \"courtyard_jumpBench_01\", \"courtyard_relaxOnBench_00\", and \"outdoors_freestyle_01\", represented as Pose 3, 6, 9, and 12, respectively. Due to the length constraints of the trained model, each dataset was truncated to 180 frames for evaluation. In the 3D coordinate improvement evaluation, we did not apply the rule-based outlier removal technique that is only applicable to golf motions. In the MPJPE scores, we observed performance improvements in most evaluation results when using our post-process coordinate refinement module. Notably, for Pose 9, we observed a roughly 9% reduction in the MPJPE score. Previous studies faced limitations in the refinement of 3D coordinates because of using an image process approach. On the other hand, our proposed method demonstrates that improvements in three-dimensional coordinates are achievable by leveraging coordinate sequence information.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Swing 2** & **Swing 4** & **Swing 5** & **Swing 6** & **Swing 7** & **Mean** \\ \hline BlazePose & 67.11 & 59.49 & 57.24 & 66.66 & 40.09 & 58.12 \\ BlazePose + Our & 67.11 & 66.66 & 66.30 & 66.66 & 41.54 & 61.66 \\ BlazePose + Our & 67.11 & 58.64 & 66.66 & 66.66 & 41.54 & 60.12 \\ w/o (a), (c), (d) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental result of 2D golf pose refinement module. This experiment compares the results of applying our pose refinement method to the baseline model, BlazePose. The evaluation score is based on mAP(%), which represents the average precision across different recall levels. (a), (b), and (c) are the algorithms in Figure 4. The “w/o” represents “without”.
# Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services
## 5 Performance Evaluation and Results
### Baseline Strategy  
In the current literature, we observe that most performance analysis of cloud service admission control schemes was carried out under the assumption that the queue holding times for new services and waiting services are identically distributed (some with exponential distribution), i.e., all cloud services calls were assumed to be identically distributed with the same parameter. This mechanism presents several characteristics inherited from different well-known approaches, such as the Uni-path reservation [42] and a unique reservation threshold [43]. In admission control schemes, baseline strategies are considered as a benchmark for the performance of a particular task. However, keeping in view the cloud services provisioning systems, we compare Bouncer with three schemes which are similar to the service-level agreement (SLA) based approaches in conventional cloud systems. In this paper, we consider the task execution of a unit cloud service as the minimal baseline parameter. We compare Bouncer against the following three strategies.  
* _Maximal Admittance Policy (MAP):_ The strategy allows all incoming requests without filtering them. Its objective is to improve and maximize the resource admittance rate in a system. The acceptance queue for this strategy in our system is theoretically set to maximum (infinite) but we limited our experimentation value up to 2500 requesting jobs in the job queue. This policy is similar to High Availability Admission Control Setting and Policy [44], often used in conventional SLAs.
* _Smart Job Admission Policy (SJAP):_ In SJAP, we consider a scenario where the request time \(\mathrm{R_{t}}\) for a job \(\mathrm{Q_{i}}\) measured on the basis of the waiting processes \(\mathrm{Q_{i}}_{\mathrm{mprocess}}\). Therefore, in order to get selected for admission in SJAP queue, a request must satisfy the condition of \(\mathrm{R_{t}}\geq\mathrm{Q_{i}}_{\mathrm{mprocess}}\) and is similar to [45] except that it does not perform the slicing for function isolation.
* _Load Balancing and Control Policy (LBCP):_ LBCP identify the incoming requests based on their impact on system resources by performing isolation on individual requests. The policy is a limited version of the load balancing policy presented in [46].
# Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings
## 3 Uncertainty Estimation in Neural Networks -- Mathematics and Algorithm
### Uncertainty Estimation for Embedding Vectors
#### 3.3.6 Obtaining a Scalar Uncertainty Quantifier  
In certain situations, it is very helpful to have a detailed uncertainty estimate which matches some other component's shape whose uncertainty it describes. We have already seen this in Equation (7) where we use the variance feature tensor to modify the mean feature tensor and will explore a similar strategy in Section 4.3. However, in other situations, it would be more helpful to have a simple and straightforward scalar uncertainty quantifier. This can be the case if the task is to define a cutoff for unacceptably high uncertainty or if the uncertainty values should be provided for human interpretation. Thus we need to choose a function to convert our uncertainty estimates \(\Sigma^{(D)},\Sigma^{(M)},\Sigma^{(V)}\in\mathbb{R}^{d}\) to a scalar uncertainty score \(s\in\mathbb{R}\).  
Given that the data uncertainty estimate is employed as the diagonal of a covariance matrix in our approach and that the other two uncertainty vectors are also calculated as variances, it appears reasonable to consider the distributions to which these variances pertain and to utilize tools of probability theory in order to quantify the uncertainty inherent in these distributions. In particular, entropy is commonly used for this purpose. This is because the entropy \(H\) of some random variable \(A\) is defined as the expected surprisal. Naturally, we consider the entropy of the probabilistic embedding \(\mathcal{N}(\mu,\text{diag}(\Sigma^{(D)}))\) for data uncertainty. Analogously, we consider the entropy of \(\mathcal{N}(\tilde{\mu},\text{diag}(\Sigma^{(M)}))\) and \(\mathcal{N}(\Sigma^{(D)},\text{diag}(\Sigma^{(V)}))\) for model and distributional uncertainty. The entropy of a multivariate normal distribution is given in Equation (16).  
\[H(A)=\frac{d}{2}\ln\left(2\pi\text{e}\right)+\frac{1}{2}\ln\det\Sigma, A\sim\mathcal{N}(\mu,\Sigma) \tag{16}\]  
Because this formula only depends on \(\Sigma\), and because we are working with diagonal covariance matrices whose diagonals are the uncertainty vectors, this simplifies to Equation (17) which describes the function \(\mathcal{H}:\mathbb{R}^{d}\to\mathbb{R}\) that maps the uncertainty vector to the entropy of the respective distribution.  
\[\mathcal{H}\Big{(}\Sigma^{(\omega)}\Big{)}=\frac{d}{2}\ln\left(2\pi\text{e} \right)+\frac{1}{2}\sum_{i=1}^{d}\ln\Big{(}\Sigma_{i}^{(\omega)}\Big{)}, \omega\in\{M,D,V\} \tag{17}\]  
We will use this measure in our experiments where a scalar uncertainty value is needed, i.e., for correlation analyses (Sections 4.2 and 4.3.2) and for the utilization of uncertainty to predict the extent to which an input is out-of-distribution (Section 4.4).
## 3 Results and Discussion
### Radiation Pattern Performance
#### 3.2.4 Rectangular Radiation Characteristic  
Figure 11 displays some of the outcomes of the RPT antenna's radiation pattern with a different slot structure. Figure 11a,b show the 2 GHz radiation patterns between the RPT, FCPT, and HWSPT antennas in the E-plane. Figure 11c,d show the radiation patterns comparison of the RPT to the ESEPT, and MLPT antennas at 4 GHz, respectively. At a frequency of 2 GHz, the main lobe RPT is 8.89 dBi, the side lobe level (SLL) is \(-0.7\) dB, the Angular width (3 dB) is \(15.2^{\circ}\) and the main lobe direction is \(0^{\circ}\). The FCPT structure results of 11.6 dBi main lobe, \(-4.7\) dB sidelobe level, \(14.1^{\circ}\) angular widths, and \(0^{\circ}\) main lobe direction. While the HWSPT structure generates a major lobe of 11.7 dBi, the main lobe with an angular width of \(14.6^{\circ}\), and the main lobe direction of \(0^{\circ}\), as well as a sidelobe level of \(-4.6\) dB. The antenna performance of the FCPT and HWSPT structures at 2 GHz is superior to RPT in the main lobe, SLL, and Angular width (3 dB). For instance, At the frequency of 4 GHz, ESEPT generates the main lobe of 12.9 dBi, the main lobe direction of \(0^{\circ}\), an angular width of \(7.4^{\circ}\), and a side lobe level of \(-2.5\) dB. At the frequency of 4 GHz, the performance of the RPT and ESEPT antenna radiation is almost the same, with only an increase in the directivity of 0.8 dBi. At 4 GHz, the MLPT produces a main lobe of 16.6 dBi, a Mainlobe direction of \(0^{\circ}\), an angular width of \(23.1^{\circ}\), and a side lobe level of \(-11.1\) dB. The directivity of MLPT increased by 4.5 dBi due to the addition of the lens structure and metamaterial so that electromagnetic waves are embedded in the metamaterial structure.  
Figure 10: Beamwidth of (**a**). Element and 1 \(\times\) 2 Regular Palm Tree (RPT-CVA), 1 \(\times\) 2 Front Cut Palm Tree (FCPT-CVA), 1 \(\times\) 2 Middle Cut Palm Tree (MCPT-CVA), 1 \(\times\) 2 Back Cut Palm Tree (BCPT-CVA), 1 \(\times\) 2 Complete Cut Palm Tree (CCPT-CVA), 1 \(\times\) 2 Left Tilt Palm Tree (LTPT-CVA), 1 \(\times\) 2 Right Tilt Palm Tree (RTPT-CVA), 1 \(\times\) 2 Hilbert Fractal Structure Palm Tree (HFSPT-CVA), and (**b**). Element and 1 \(\times\) 2 Regular Palm Tree (RPT-CVA), 1 \(\times\) 2 Koch Fractal Structure Palm Tree (KFSPT-CVA), 1 \(\times\) 2 Exponential Slot Edge Palm Tree (ESEPT-CVA), 1 \(\times\) 2 Vertical Wave Structure Palm Tree (WSPT-CVA), 1 \(\times\) 2 Horizontal Wave Structure Palm Tree (HWSPT-CVA), 1 \(\times\) 2 Regular Lens Palm Tree (RLPT-CVA), 1 \(\times\) 2 Elips Lens Palm Tree (ELPT-CVA), and 1 \(\times\) 2 Metamaterial Lens Palm Tree (MLPT-CVA).