esa-sceva/satcom-chunk-collection huggingface dataset:
# Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems
## 4 Model Development and Training
### Model Initial Tests and Adjustments  
By using the proposed CNN model architecture, in total, three different models were implemented and trained for light intensity, air humidity, and soil water content sensor measurement forecasting (Table 1). Each model utilizes a thermal image matrix of 120 \(\times\) 160 as its input. Model #1 is designed to forecast light intensity, measured in lux. Model #2 focuses on forecasting air humidity percentages. Lastly, Model #3 is for forecasting soil water content, with an output range from 0 to 4095 (which is a sensor value output), where 0 corresponds to the highest water content and 4095 to the lowest.  
Additionally, to optimize each CNN model for forecasting sensor measurements from plant thermal images, hyperparameters such as filter sizes in convolutional layers, learning rates, and numbers of neurons in dense layers can be used [26; 27]. In this case, batch size, epochs, and learning rate parameters were used. The optimization aim was to enhance each model's ability to accurately analyze patterns in thermal images, resulting in more precise sensor measurement forecasts. One of the hyperparameter optimization approaches is to use \"Grid Search\" [28]. It systematically builds and evaluates a model for each combination of parameters specified in a grid. While effective for models with a limited number of hyperparameters, Grid Search can be computationally intensive, especially for larger, more complex models. This search over specified parameter values aims to find the best combination that maximizes the performance of the model [28]. As a result, the following parameters were used in model training and evaluation (see **Training Parameters in Table 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Model \#1** & **Model \#2** & **Model \#3** \\  & **Scale:** & **Scale:** & **Scale:** \\  & **Min Value: 0** & **Min Value: 0\%** & **Min Value: 0** \\  & **Max Value: 65,535** & **Max Value: 100\%** & **Max Value: 4095** \\ \hline \multirow{3}{*}{**Input**} & Thermal image & Thermal image & Thermal image \\  & matrix 120 \(\times\) 160 (\({}^{\circ}\)C) & matrix 120 \(\times\) 160 & matrix 120 \(\times\) 160 \\  & & (\({}^{\circ}\)C) & (\({}^{\circ}\)C) \\ \hline \multirow{3}{*}{**Output**} & Light Intensity (Lux) & Relative & Soil Water Content \\  & (0–65,535) & Air Humidity (\%) & represents the highest \\  & & & and 4095 lowest \\  & & & water content) \\ \hline \multirow{3}{*}{**Training**} & Learning rate: 0.001 & Learning rate: 0.001 & Learning rate: 0.001 \\  & Batch size: 15 & Batch size: 10 & Batch size: 15 \\ \cline{1-1}  & Epochs: 35 & Epochs: 40 & Epochs: 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Models used in sensor measurement forecasting.  
Training for each model was performed 30 times with the optimal training parameters to ensure that the results are consistent between the runs.
## 3 Electron Acceleration with Radially Polarized Laser Beams
### Threshold for Sub-Cycle Acceleration and Attosecond Bunching
Figure 3: Single-electron model of longitudinal acceleration in a 12-fs (FWHM) RPLP (\(k_{0}z_{0}\sim 500\) with \(\lambda_{0}=800\) nm). The electron is initially at rest at \(z=0\). (**a**) At low intensity (\(a_{z}^{2}=0.1\)), the electron experiences a quasi-harmonic motion, with slightly longer excursions in accelerating half field-cycles; (**b**) At the longitudinal acceleration threshold (\(a_{z}^{2}=1\)), this phenomenon is stronger but the final kinetic energy remains relatively small (in the keV range); (**c**) Above threshold (\(a_{z}^{2}=10\)), the electron escapes the laser pulse with a relativistic longitudinal momentum. During the sub-cycle acceleration represented in (**c**), the electron stays locked to the phase of the pulse carrier. To illustrate the different regimes of longitudinal electron acceleration in RPLP, we performed a series of simulations, where the time-dependent Lorentz force equation was integrated. Results are shown in Figure 3. It is observed that above threshold (\(a_{z}^{2}\gtrsim 1\)) the longitudinal electric component of the laser field is strong enough to accelerate an electron initially at rest at focus to a relativistic velocity within a half laser period. In that regime, electrons released in the same half-cycle are naturally bunched together. This is shown in Figure 4, where three-dimensional simulations were done in the single-electron approximation. More realistic simulations that include self-consistent particle interactions and ionization dynamics indicate that, in most conditions, a train of attosecond electron packets--each separated by a laser wavelength--is formed [19]. A single attosecond electron pulse could be produced if a single-cycle RPLP is used.
# Intelligent Detection Methods of Electrical Connection Faults in RF Circuits
###### Abstract  
Printed circuit boards (PCBs) have a large number of electrical connection nodes. Exposure to harsh environments may lead to connection faults in these nodes. In the present work, intelligent detection methods for electrical connection faults were studied. Specifically, the fault characteristics of connectors, bonding wires and solder balls in the frequency domain were analyzed. The reflection and transmission parameters of an example filter circuit with electrical connection faults were calculated using the Simulation Program with Integrated Circuit Emphasis (SPICE). With these obtained electrical parameters, three machine learning algorithms were used to detect example electrical connection faults for the example circuit. Based upon the performance evaluations of the three algorithms, one can conclude that machine-learning-based intelligent fault detection is a promising technique in diagnosing circuit faults due to electrical connection issues with high accuracy and lower time cost as compared to current manual processes.  
electrical connection fault; intelligent detection; signal integrity Article
# Towards Federated Learning with Byzantine-Robust Client Weighting
## 4 Discussion  
The results of our experiments establish that: (1) in the absence of attacks, model convergence is on par with that of properly using all reported weights, and (2) when attacks do occur, the performance of combining truncation-based preprocessing and robust aggregations incurs almost no penalty in comparison with the performance of using all weights in the lack of attacks, whereas without preprocessing, even robust aggregation methods collapse to a performance that is worse than that of a random classifier.  
When the number of clients is very large, performing server preprocessing and aggregation on the server may become computationally infeasible. We prove that, in this case, truncation-based preprocessing can achieve the same upper bound on \(\alpha^{*}\) with high probability based on the weight values reported from a sufficiently large number of the clients selected IID.  
In future work, we plan to further analyze the trade-off between robustness and the usage of client sample size in rectifying data unbalancedness. We also plan to investigate alternative forms of estimating client importance that may avoid client sample size altogether.  
**Author Contributions:** Conceptualization, A.P. and D.H.; methodology, A.P. and D.H.; software, A.P. and Y.T.; validation, A.P. and Y.T.; formal analysis, A.P. and Y.T.; investigation, A.P. and Y.T.; resources, N/A; data curation, A.P.; writing--original draft preparation, A.P., Y.T. and D.H.; writing--review and editing, A.P., Y.T. and D.H.; visualization, A.P. and Y.T.; supervision, A.P. and D.H.; project administration, D.H.; funding acquisition, D.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Cyber Security Research Center, Ben-Gurion University of the Negev.  
Not applicable.  
Data Availability Statement: Data are available in a publicly accessible repository. The data presented in this study are openly available in arxiv and in the Wolfram Data Repository at [[https://doi.org/10.48550/arXiv.1812.01097](https://doi.org/10.48550/arXiv.1812.01097)]([https://doi.org/10.48550/arXiv.1812.01097](https://doi.org/10.48550/arXiv.1812.01097)) and [[https://doi.org/10.24097/wolfram.62081.data](https://doi.org/10.24097/wolfram.62081.data)]([https://doi.org/10.24097/wolfram.62081.data](https://doi.org/10.24097/wolfram.62081.data)), all accessed on 1 July 2022.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} FL & Federated Learning \\ ERM & Empirical Risk Minimization \\ FedAvg & Federated Averaging \\ SGD & Stochastic Gradient Descent \\ MWP & Maximal Weight Proportion \\ trunc & truncation \\ \end{tabular}
## 4 Discussion and Application
### Noise Characterizations  
As previously discussed, applications, e.g., in biophotonics and sensing, require highly stable and low-noise pulsed sources. We therefore performed relative intensity noise (RIN) measurements of both the pump dissipative soliton erbium doped fiber laser (DS-EDFL) and the FOPO. The measurement system essentially consists in an InGaAs PIN photodetector (DET08 CFC) with a response r(\(\lambda\)) = 0.9 around 1550 nm, an amplifier (MITEQ AU-1442) with a gain of 34 dB, and a noise figure of 1 dB, and an electric spectrum analyzer (Rohde&Schwarz FSV 7 GHz). As the laser delivers high power, and does not have to operate near the threshold either (to avoid additional quantum noise), measurements were performed at the 10% output of coupler C1 using an optical attenuator, while blocking the FOPO feedback branch. Signals were then measured with a power meter and/or sent to the RIN measurement system. Concerning the FOPO characterization, the filtered idler from the 90% output of coupler C2 was sent for RIN measurements in the same way. The attenuator was used to control the power that arrived on the detector (DET08 CFC) in order to characterize its optical response. From preliminary measurements, we confirmed that the photodetector guaranteed a linear response for incident optical powers up to 1.5 mW.  
The performed RIN measurements using this system are shown in Figure 15a. Our pump laser exhibited a good RIN of \(-154\) dBc/Hz at \(\lambda\)p = 1566 nm, comparable to the system used in Ref. [6]. The FOPO increased it to around \(-140\) dBc/Hz, considering only the filtered idler signal in the \(-3\) dB-bandwidth between 1655 nm and 1685 nm, which represents a satisfying performance in the frame bio-imaging applications. More investigations are however now needed to further enhance the RIN of the whole system beyond this value.  
Figure 14: Generation of signal and idler wavelengths close to 1300 nm and 1900 nm respectively.  
Figure 15: (**a**) Measured RIN of both pump and idler pulses. These measurements correspond to a 1.4 mA optical current. (**b**) Pump and idler spectra at 1566 nm and 1668 nm, respectively. The inset represents the pump and idler autocorrelation traces.
# Real-Time Volumetric Ultrasound Research Platform with 1024 Parallel Transmit and Receive Channels
## 2 Materials and Methods
### Multi-Channel Electronics
#### 2.3.4 Transducer Connector Board  
In addition to some control lines and status LEDs, this PCB integrates the socket for connecting the ultrasound transducer. The connector chosen is the ITT Cannon ZIF DLM-360, which is widely used in medical technology and is specially designed for the high pin count of ultrasound arrays and for the high transmission voltages of up to +/\(-\)100 V. Transducers with other pin-outs can easily be connected to the system via adapters.
## 5 Experiments
### Framework Tuning  
The proposed framework is implemented using the Keras ([[https://keras.io/](https://keras.io/)]([https://keras.io/](https://keras.io/))) deep learning library using Python, running on top of the Tensorflow ([[https://www.tensorflow.org/](https://www.tensorflow.org/)]([https://www.tensorflow.org/](https://www.tensorflow.org/))) backend. All hidden layers are equipped with the Rectified Linear Unit (ReLU) non-linearity which allows the networks to converge faster [53]. For the final fully connected neural network layer, the sigmoid non-linear activation function is used. Optimization is performed by ADAM optimizer algorithm [54]. The Adam optimization algorithm is a powerful extension to stochastic gradient descent and has been successfully applied into deep learning applications in various domains such as computer vision and natural language processing. Layer weights are initialized with the Glorot uniform initializer. The framework is trained in mini-batches of size 32 for 20 epochs, with learning rate = 0.001, beta_1 = 0.9, beta_2 = 0.999, and epsilon = 1e-07. The final framework has 35,092 parameters to optimize. The optimum parameters for training epochs, batch size, learning rate and activation function are selected through grid search. Although window size is an important hyper parameter for time series analysis, our framework is not very sensitive to it, as it is explained in the next section. We have tested the performance of the proposed framework using window sizes 12, 6, and 3. The anomaly detection results given in Section 5.5.1 were generated by the framework with window size set to 12 (\(T=12\)) and sliding step size set to 1.  
```
# p: current spatial context (e.g., sensor)
# L: Full time series of current spatial context (multivariate)
# T: window size
# s: sliding step size
# d: number of channels (depth of 3d spatio-temporal data matrix)
# seqs: 3D tensor data for the current spatio-temporal data point
1: function seqs = ExtractSubsequences(\(p\), \(L\), \(T\), \(s\), \(d\))
2: start_indx = 0
3: end_indx = start_indx + T
4:\(n=0\) # n is the number of subsequences
5: neighbour_list = getNearestNeighbours(p, d-1)
6: while (end_indx < length(\(L\))):
7:\(\#\) get timestamp of data at start_indx and end_indx
8: start_date_time = getTimestamp(\(L\), start_indx)
9: end_date_time = getTimestamp(\(L\), end_indx)
10:\(\#\) check if there is a big time gap between start and end indexes
11: If (end_date_time - start_date_time) < (2 \(\times\) \(T\)):
12:\(\#\) get the subsequence from the long time series data
13: seqs[n, 0] = \(L\)[start_indx_end_indx]
14:\(\#\) get data from (d-1) nearest neighbours and attach to seqs
15: seqs[n, 1:d] = getDataFromNeighbours(T,
16: start_date_time,
17: end_date_time,
18: neighbour_list)
19: start_indx = start_indx + s
20: end_indx = start_indx + T
21: n = n + 1
22: return seqs
```  
**Algorithm 1** Sliding window algorithm used in subsequence generation
# AI-Based Approach to Firewall Rule Refinement on High-Performance Computing Service Network
###### Abstract
To address the three previously mentioned issues, we collected, integrated, and analyzed events from the network security equipment of the Supercomputer Nurion over a four-year period in the existing HPC service environment. This process involved the following steps: parsing, extracting, pre-processing, training, evaluating, and detecting. First, we removed consecutive duplicate values from the raw data and parsed the features necessary for extraction. Because of the vast amount of raw data, we developed a parser capable of parallel processing in an HPC cluster environment. A continuous increase in abnormal detection counts was discovered when traffic data where policies were erroneously added to the firewall rules owing to human error were analyzed. We defined patterns at the moments when the firewall event state changed to PASS-DROP-PASS or DROP-PASS, and extracted data monthly, classifying the patterns as firewall rule policy errors. Using this extracted four-year dataset of firewall rule policy errors and by accurately adding firewall rule policy data, we created training datasets in equal proportions. To achieve the highest accuracy, we used various pre-processing methods, algorithms, and techniques. We developed machine-learning classification algorithms and deep-learning models and explored suitable methods using various data-scaling techniques. We compared the results classified by the NN model with the cross-entropy loss function and attempted to improve the accuracy by refining the features and training the model with finer granularity. The contributions of this study can be summarized as follows:  
* Enhancement of network security in the HPC service environment: We analyzed over four years of event data from the network security equipment of the Supercomputer Nurion to improve the network security in the HPC service environment. * Detection and analysis of firewall rule policy errors: We analyzed traffic data with policies erroneously added owing to human error to identify continuous abnormal detection. We also defined patterns when the firewall event state changes to categorize firewall policy errors. * Application of machine-learning algorithms and deep learning models: We trained the datasets using classification algorithms such as naive Bayes, KNN, OneR, J48, logistic regression, SVM, and an NN model with cross-entropy loss function to detect security events and increase accuracy. * Various attempts to improve accuracy: We improved the accuracy of our analysis using various techniques such as data pre-processing, algorithms, and feature refinement. This paper is outlined as follows: Section 2 provides an introduction to related works. Section 3 presents the overall architecture and methods for firewall data refinement. This includes details on data pre-processing and the implemented training model. Section 4 presents a comparative analysis and discussion of the results. In the final section, the conclusions and directions for future work are described.
## 3 \(l\)-\(k\)-Nearest-Neighbors Query Processing
### \(l\)-\(k\)NN Boundary Estimation  
The H-node disseminates a query message to the other sensor nodes. The query message is progressively propagated to sensor nodes farther from the H-node. Because we do not have information about the sensor node distribution, we must estimate a maximum boundary for the \(l\)- \(k\)NN query (\(l\)-\(k\)NNB) by considering the worst case. A large \(l\)-\(k\)NNB guarantees correct query results but incurs large energy consumption and slow query responses. However, a small \(l\)-\(k\)NNB decreases query accuracy because some query results are located outside of the \(l\)-\(k\)NNB.  
Let \(R\) be the radius of the \(l\)-\(k\)NNB. According to the definition of an \(l\)-\(k\)NN query, we need to find the \(k\) sensor nodes nearest to the H-node that are at least distance \(l\) apart. Therefore, \(R\) is calculated as in Equation (2). All query results are then guaranteed to be located within the radius \(R\):  
\[R=k\times l \tag{2}\]  
A sensor node can send messages to other nodes within the sensor's radio range. If it sends messages to other nodes located outside its range, the query message traverses several hops between the source and destination. Therefore, we need to estimate \(l\)-\(k\)NNB differently according to the ratio of \(l\) to the sensor radio range \(r\). We refine Equation (2) as Equation (3) with consideration of the ratio:  
\[T_{l}=\left\{\begin{aligned} & k\times r,\textit{if }l<r\\ & k\times 2\times r,\textit{if }l=r\\ & k\times(\left[\frac{l}{r}\right]+1)\times r,\textit{if }l>r\end{aligned}\right. \tag{3}\]  
If \(l<r\), \(l\)-\(k\)NNB is estimated as \(k\times r\). A single \(r\) is enough to maintain the distance \(l\) between two Q-nodes because \(l\) is shorter than \(r\). If \(l=r\), \(l\)-\(k\)NNB is estimated as \(k\times 2\times r\). Although \(l\) is equal to \(r\), we need \(2\times r\) to ensure the distance \(l\) is maintained between two Q-nodes. This is because of the possibility that no sensor node exists on the circumference of \(r\). If \(l>r\), \(l\)-\(k\)NNB is estimated as \(k\times(\left[\frac{l}{r}\right]+1)\times r\). To maintain \(l\), we need one more \(r\) than for the case of \(l=r\) because \(l\) is larger than \(r\).  
Figure 3 shows examples of l-kNNBs with various ratios of r to l when k is 2. Figure 3a-c represent estimates of \(l\)-\(k\)NNBs for the cases of \(r>l\), \(r=l\) and \(r<l\), repectively. According to Equation (3), \(R\) is estimated as \(2\times r\), \(2\times 2\times r\) and \(2\times(2+1)\times r\) when \(l\) is \(\frac{1}{2}r\), \(r\) and \(2r\), respectively. The distance \(d\) between the Q-nodes is longer than \(l\) in all cases.
# Evaluation of Ototoxic Effects of Cisplatin in a Rat Model: A Dose-Response Study
## 1 Introduction
Superoxide dismutase regulates the process of turning superoxide anions into H\({}_{2}\)O\({}_{2}\). In response to high levels of hydrogen peroxide, SOD1 can enter the nucleus. The enzyme in the nucleus interacts with DNA and binds to the promoter sequence of target genes that regulate their expression at the transcriptional level [20; 22; 23]. Nitrotyrosine is also known to be a marker of oxidative stress in cells and tissues. Oxidative stress caused by reactive nitrogen species (RNS) is also called \"nitrative stress\", with the first term referring to excess production of the NO radical and the second term indicating excess formation of peroxynitrite. Nitration of tyrosine by RNS, including peroxynitride and nitrogen dioxide, results in the production of 3-nitrotyrosine. This compound is found in numerous pathological conditions and is, therefore, considered a marker of NO-dependent oxidative stress. It is recognized as a marker of cell damage and inflammation and also serves as an indicator of nitric oxide (NO) production. The 3-nitrotyrosine is involved in protein nitration at the level of tyrosine residues, an irreversible and potentially harmful process that involves conformational modification of the protein and consequent loss of its functionality [24]. This study is based on previous research that shows that giving cisplatin (CDDP) in three daily doses, rather than one dose, results in comparable hearing loss but significantly reduces mortality in rats [11]. This administration protocol closely mirrors human chemotherapy regimens, making it more suitable for long-term preclinical studies on ototoxicity prevention. Specifically, the goal is to develop an in vivo model of cisplatin-induced ototoxicity and nephrotoxicity that exhibits noticeable but not life-threatening damage. This study aims to examine the mechanisms of ototoxicity caused by drug exposure through functional, morphological, and immunohistochemical analyses, while nephrotoxic damage will also be documented to provide a reference for systemic effects in future ototoxicity prevention studies. The findings of this study will serve as a basis for future research on protective compounds that can mitigate or prevent cisplatin-induced ototoxicity in healthy animals, i.e., those without oncological diseases, with nephrotoxic damage serving as an internal control.