esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Order Spherical Harmonic HRTF Restoration Using a Neural Network Approach
## 3 Machine Learning HRTF Restoration
### Model Enhancement
#### 3.3.4 Combining Weight Decay and Dropout  
Combining weight decay and dropout showed the best result in the hold out SADIE II data despite there being a noticeable trade-off with the Bernschutz dataset. This result in Table 7 Model E indicates that by combining weight decay and dropout, the model can generalise better across different unforeseen HRTF subjects (SADIE hold out) but not the measurement method (Bernschutz). As this model performed the best with the SADIE II test data, this became the proposed model to be further analysed in Section 4.  
It was interesting to find that the combination of the two different methods showed a large difference in results, but not with either method individually. It is not clear whether the improvement comes from the combination of the techniques or is from the cumulative regularisation power. This could be an individual research topic to be investigated in the future.
# Prediction and Optimization of Open-Pit Mine Blasting Based on Intelligent Algorithms
## 4 Methods
### Optimization of LSSVM by Genetic Algorithm  
Genetic algorithm (GA) is an optimization algorithm based on natural selection and genetic mechanisms that is used to solve various complex optimization problems [40]. Using GA to optimize the LSSVM algorithm can fully leverage its advantages, such as global search capability, strong adaptability, and ease of parallelization, to find better combinations of model parameters, thus enhancing the performance of the LSSVM model. The process of optimizing hyperparameters in the LSSVM model using GA is illustrated in Figure 3. During the optimization process, the objective function is to minimize the prediction error on the training set, and the RMSE is selected to establish the fitness function, as shown in Equation (6).  
\[\text{fit}(\gamma,\sigma)=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{n}\left(y_{i}- \hat{y}_{i}\right)^{2}} \tag{6}\]  
Among the variables, \(N\) is the number of training samples, \(y_{i}\) is the actual value, and \(\hat{y}_{i}\) is the predicted value.
## 5 Conclusions  
This study investigated the antibacterial capabilities of various plants against food spoilage as well as human pathogenic microorganisms. The focus was to determine the level of antibacterial activity that each plant demonstrated and to what extent it differed among various varieties from distinct locations. The results showed that all the plants under investigation exhibited significant antibacterial activity. _Rumex acetone_ L recorded the highest antimicrobial activity against _Escherichia coli_ and _Salmonella enteritidis_. While this finding is useful, it is important to note that the phytochemical composition of the plants was found to vary significantly among plant varieties obtained from different locations. Even though all the plants demonstrated antibacterial activity, different varieties of the plants had varying degrees of effectiveness against bacteria. _Microoccus luteus_ showed the lowest resistance to all plant samples. Both Gram-negative and Gram-positive bacteria were evaluated, and the results indicated that these plants could potentially be effective against a wide range of bacteria. However, the different chemical compositions of the plants may explain the statistically significant disparities in their antimicrobial activities. The findings of this study imply that the geographical parameters of the locations where these plants grow have a slightly strong influence on their biochemical makeup and biological activities. Therefore, in order to fully understand the potential value of medicinal plants, it is crucial to investigate and compare the phytochemical profiles of different varieties of the same plant obtained from different locations. Such findings could advance our understanding of how to maximize the therapeutic benefits of medicinal plants in disease management. Furthermore, all the plants demonstrated significant antimicrobial activity against the pathogens for which they were tested. Their activities, measured in terms of minimum inhibitory concentrations, appeared to be comparable to what has earlier been reported in the literature. Interestingly, all the plants seemed to exhibit the highest antimicrobial activity (marked by relatively low MICs) against _M. luteus_ and _L. innocua_. However, they also exhibited significant activities against other microorganisms, including _Escherichia coli_, _Proteus mirabilis_, _Pseudomonas fluorescens_, _Pseudomonas fragii_, and _Salmonella enteritidis_. These results demonstrate the value of the plants as medicinal plants of potential clinical and therapeutic value as antimicrobial agents against the aforementioned pathogens. A pertinent recommendation for further research would be to aim to elucidate the particular phytochemicals from the plants that affect the antimicrobial activities observed and to quantify the extent to which synergism among two or more phytochemicals influences the antimicrobial properties of the plant. This is in order to standardize the plants for clinical use.
# Data Sensing and Processing Tensioning System Based on the Internet of Things
## 3 Intelligent Tensioning System Design
### Data Processing  
After receiving a valid job packet, the data-processing layer identifies and extracts it successively. The data-processing layer includes five operations. First, the packet is split, each data item is extracted,  
Figure 8: Packet transport sequence diagram.  
Figure 7: Packet queue control mechanism.  
and the data are stored to the corresponding data table according to packet type. Second, the pre-alarm parameters and reference values in the rule base are extracted and compared with the corresponding data items. Third, when the value of the data item exceeds the warning reference value, a job problem is automatically generated and written to the problem base. Fourth, job data are synthesized, the synthetic construction record table for each data item is extracted in accordance with the management specification and business rules of tensioning jobs, and the job data are summarized automatically. Fifth, when the tensioning job is started or an alarm event is triggered, the app message service is started to push corresponding prompt messages and job data to administrators. The specific operation flow of the data-processing layer is shown in Figure 9.  
Data synthesis is a key step in the data-processing layer. A series of operations, such as extracting, analyzing, and synthesizing, is performed on flag and conclusion packets in line with the business rules to provide data support for the application layer. The data synthesis process is shown in Figure 10.  
The tensioning jobs are divided into types A, B, and C according to the quantity of jacks in need during tensioning jobs. Type A indicates that the No. 1 and 2 channels are simultaneously operated, Type B indicates that only No. 1 channel is in operation, and Type C indicates that only No. 2 channel is in operation. The specific algorithm for data merging is shown in Algorithm 1.  
Figure 10: Data merging process.  
Figure 9: Data-processing layer operation flow.  
```
1:Begin
2:\(d\leftarrow\) getDataPaket() // get packets
3:if(IsFlag1(\(d\))) { // judge if it is Flag packet 1
4: Initialize queue \(S\);
5: Initialize array\(A\),\(U\);
6: goto step 2;
7: }
8:if(IsConclusion(\(d\))){  put d into queue \(S\); // the operation of enqueue
9: goto step 2;
10: }
11:if(IsFlag2(\(d\))) { // Insert the data in Flag Packet 2
12: \(t\leftarrow\) getType(\(d\)); // get job type
13:if(\(t\)=TypeA or \(t\)=TypeB) {
14:\(A\)[++] \(\leftarrow\) get first flag information from \(d\);
15: }
16:if(\(t\)=TypeA or \(t\)=TypeC) {
17: \(A\)[++] \(\leftarrow\) get second flag information from d;
18: }
19: }
20: while \(S\) is not null //MergeData(\(d\),\(h\)) {
21: \(h\leftarrow\) get data from queue \(S\);
22: \(t\leftarrow\) getType(\(h\)); // get job type
23:if(\(t\)=TypeA or \(t\)=TypeB) {
24:\(U\)[j++] \(\leftarrow\) get first conclusion from \(h\); }
25:if(\(t\)=TypeA or \(t\)=TypeC) {
26: \(U\)[j++] \(\leftarrow\) get second conclusion from \(h\);
27: }
28: }
29:
```  
**Algorithm 1**Algorithm for data merging
## 5 Adoption Case in the Steel Industry  
The case examined was the process in the blast furnace of a North American steel company, where there was a need to optimize the production line by improving the quality and quantity of performance of the blast furnace. As mentioned before, these are complex and dynamic processes that generate hundreds of data types with high variability. This leads to management challenges to maintain consistency of production quality, lower energy consumption, and use less silicon.  
It should be considered that inconsistencies in quality in the casting process generate excess scrap, rework, and production delays, among other issues. Traditional quality control was done through samples that were analyzed in a laboratory. The problem withthis methodology is that if there was an inconsistency, the problem was solved in a reactive way, when it had already occurred.  
On the other hand, it must be considered that the process ran in three eight-hour shifts, and the operators were different, each with their own criteria for and experience in the process. Then, its control varied depending on each person. In addition, the smelting process included a large number of chemical and thermodynamic variables that were very difficult to monitor and control.  
The firm generated real-time data from the production processes through the world-class MES software, OSI PI from OSIsoft [21]. These data were stored in historian-type databases.
# Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars
## 6 Future Work and Challenges
### Future Work
Moreover, flying birds and waterfalls under the UAS airframe during the flight is another concern to be investigated. The discussion on velocity has not been made a part of this study. However, during the rapid rate of ascent and descent of UASs, the effect of velocity needs to be studied. The experimental work is bound to not only validate the theoretical presentations of this work but also appraise the signal-processing techniques to be explored in the next stage. There has been a plethora of work carried out in the domain of machine learning/deep learning (ML/DL) using radar sensors for automotive applications. Surveys in [8] and [68; 69] show the potential research directions and experimental research conducted. ML/DL are traditionally employed in object detection and classification within ADASs. A survey dedicated to their efficacy in the UAS domain is presented in [70]. Based on the extraordinary work being carried out, it is rational to highlight that these techniques hold promising applications in the field of drone altimetry. Beyond their conventional uses, these technologies can be leveraged for advanced functionalities. One such application involves the prediction of interference from neighboring radars or electromagnetic sources, distinguishing it from authentic radar returns. Another innovative approach is the utilization of machine-learning algorithms to discern ground surface types, dynamically adjusting the chirp and frame configurations in response. A particularly noteworthy concept may involve the autonomous adaptation of radar altimeter modes based on the operational state of the UAV, specifically during the landing approach in autopilot mode. Trained on nuanced patterns in radar altitude data, ML/DL models could be leveraged to integrate variables such as the rate of descent, surface type, and current altitude to intelligently switch to a high-accuracy mode. In this heightened accuracy mode, sacrificing the maximum range for precision aligns with the specific operational requirements of precise landing. Concurrently, it offers a redundant safety measure akin to a backup for weight-on-wheels systems. This strategic integration of ML/DL not only optimizes the radar altimeter performance across diverse flight modes but also underscores its potential to elevate overall flight safety.
# Passwordless Authentication Using a Combination of Cryptography, Steganography, and Biometrics
## 4 Results
### Testing Operations
#### 4.2.2 Authentication  
Authentication starts when a user clicks on the login button on a service's website and is concluded immediately after they are directed to their account. To obtain the actual average timeframe of the system, while isolating time spent by the user retrieving their device and entering the challenge code, which is variable, we split the duration into the following phases:  
* The time taken to receive the challenge code from the SA server when the login button is clicked averages 543 ms, as shown in Figure 11.  
Figure 11: Average duration to receive challenge code.  
Figure 10: SA account creation results. (**a**) List of services in SA app. (**b**) Biometric Authentication prompt for account creation. (**c**) New Account creation confirmation. (**d**) Average Account creation duration.  
* The period between when the user confirms the login details displayed on the app, enters the challenge code on the SA app (Figure 12a), provides biometric authentication, and is routed to their account; once the user enters the challenge code, it is sent from the SA app to the SA server for confirmation. A response is then sent from the SA server to the service to either approve or reject the user's login request. The duration averaged 282 ms. Summing the average server times, the total time to complete an authentication session was 825 ms. Further, success rates of 100 percent were recorded for each authentication session.
* Our solution, with an 825 ms time frame, significantly minimizes attack vectors. During testing, all attempts were successful within this time frame. Consequently, we demonstrate a viable and secure a solution that enhances the security posture of authentication.
# Analysis of Sentinel-1 Radiometric Stability and Quality for Land Surface Applications
## 3 Data Analysis
### S-1A Radiometric Quality  
The radiometric qualities of S-1A data were investigated by calculating the difference between S-1A and RS-2 \(\sigma^{\circ}\) (in VV at 34\({}^{\circ}\) and 42\({}^{\circ}\)) (\(\Delta\sigma^{\circ}\) = \(\sigma^{\circ}\)\({}_{\rm S-1A}\) - \(\sigma^{\circ}\)\({}_{\rm RS-2}\)) returned for the racetrack. The acquisition dates of the three RS-2 images do not fall within the S-1A period of acquisition. However, we assume that S-1A and RS-2 radar signals returned from this target could be comparable. The results show that S-1A \(\sigma^{\circ}\)-values in the first and the third sub-time series are closer to RS-2 \(\sigma^{\circ}\) than \(\sigma^{\circ}\)-values of the second sub-time series. At 33\({}^{\circ}\), \(\Delta\sigma^{\circ}\) is equal to 0.3, 1.3, and 0.3 dB for the first, second, and third sub-time series, respectively. At 42\({}^{\circ}\), this difference is equal to \(-\)0.1, 0.8, and \(-\)0.2 dB for the first, second, and third sub-time series, respectively. Thus, S-1A images acquired prior to 19 March 2015 (first sub-time series) and after 25 September 2015 (third sub-time series) appear to be well calibrated. However, S-1A data acquired between 19 March 2015 and 25 September 2015 (second sub-time series) present an offset of roughly 1 dB (\(\sigma^{\circ}\) S-1A higher than \(\sigma^{\circ}\) RS-2).
## 4 Results Evaluation
### Model Validation  
When a model is implemented, its validation becomes so essential. A valid model assures the researchers of the model's rightness [91]. Therefore, the model's results are supposed to be useful out of the model, and can be confidently used for policymaking. Validation is generally defined as when the simulated model produces the results that are in a satisfactory range of accuracy, matching up with the real-world data [57]. Validation of computational models has always been a significant concern for simulation specialists [57; 90; 92; 93].  
In the big picture, all validation methods can be classified as qualitative and quantitative methods. Qualitative methods, like face validity, are very subjective and expert-based, while quantitative ones, such as empirical validation approaches, are objective and based on real-world data [91]. Face  validation is the process of showing that the mechanisms and properties of the model look like mechanisms and properties of the real world. It is mainly conducted by subject matter experts (SMEs). Empirical validation assures that the model generates data that can correspond to similar patterns of the real-world data. Selection of validation approaches to ABMs primarily depend on modeling purpose [56]. When ABMs are used to simulate a real-life statistical regularity, as is the case of phenomena-based modeling, empirical validation approaches are often used. Nevertheless, when ABMs are applied to simulate the mental models of system stakeholders and show what will emerge out of them, as is the case of exploratory-based modeling, qualitative validation methods such as face validity are often applied. In addition to type of validation approach, since ABM is a two-level model (i.e., individual agent vs. system's global behavior), it has to be validated both at micro level and macro level. The relationship between validation level and validation type has been presented in Table 7.  
In face micro validation, SMEs analyze how much the properties, input parameters, and behavioral rules of agents are in face similar to the reality. In face macro validation, SMEs analyze how much model's outputs are in face similar to the reality. In the empirical micro validations, researchers want to know how much the model's input parameters, properties, and behavioral rules correspond to real-world data. This type of validation is often referred to as direct calibration [94] or input empirical validation [90]. In empirical macro validation, the prime purpose is to understand how much the outputs of an implemented model correspond to the real-world data. Since the model of this study is phenomena-based trying to simulate the diffusion of three MTTs, so the empirical validation is used at two levels.
## 2 Background and Related Work
### Traffic Interference in a Converged Network  
Traffic isolation is needed to protect HTS traffic from interfering with other traffic to provide the required guarantees on timeliness. Isolation could be in the time domain (referred to as temporal isolation) using shaping (other techniques include frame-preemption [12] and guard-banding [13]) or spatially (referred to as spatial isolation) using routing and queuing in different queues. In general, both techniques can be combined for finding better solutions (e.g., solving a joint routing and scheduling optimization problem such as [14]) or by fixing some aspects of the overall problem to achieve a simpler problem formulation. Examples are to solve the scheduling problem for a given traffic path (routing problem is solved first), or to study the effect of routing HTS traffic over its scheduling (scheduling problem is solved first [15]). Finally, some works such as [16, 17] propose a traffic isolation model based on network calculus where they protect the traffic from interference without the need for strict isolation techniques (i.e., usage of a different queue for every stream and coordinate the transmission time of the different flows). In the rest of the paper, we focus on shaping due to its importance as a temporal traffic isolation technique and the novelties brought to it by the TSN effort.
# A Medical Image Segmentation Network with Multi-Scale and Dual-Branch Attention
## 3 Method
### Parallel Dual-Branch Attention Module  
Currently, attention mechanisms are widely applied in skip connections, while network models that modify the upsampling part are relatively rare. The typical CBAM [43] sequentially employs channel attention and spatial attention to enhance feature representation. However, this sequential method may propagate errors from the first attention module, affecting the performance of subsequent attention modules. To address this problem, we designed a novel parallel dual-branch attention module PDA with residual connections to improve the robustness and performance of the model. PDA can simultaneously focus on different dimensions of the feature map, capturing essential global and local semantic information more comprehensively. Furthermore, the PDA module effectively enhances the model's ability to segment target regions. The PDA primarily computes attention weights from two branches, focusing on extracting features from both channel and spatial dimensions. This approach effectively addresses the problem of import and information  
Figure 2: Multi-receptive field densely connected module.  
loss in the process of extracting semantic features and provides precise feature details, enabling the decoder to accurately reconstruct the feature map to its original size. The features obtained from the MRD, along with the low-frequency features from the encoder, are fed into the PDA for feature integration and more refined feature extraction. Within the decoder, attention modules are incorporated from deeper to shallower layers, enhancing the detailed information of the lesions and highlighting the inter-scale relationships. This structure is illustrated in Figure 3.  
For an input feature map \(x\in R^{CsHsW}\), where C denotes the number of channels, \(H\) represents the height of the feature map, and \(W\) denotes the width of the feature map, the computation formula for the \(F_{1}\) branch is as follows:  
\[W_{c}=sigmoid(g(Conv_{1+1}(Conv_{3+3}(x)))) \tag{5}\]  
Among them, \(Conv_{1+1}\) represents a convolution kernel of size 1 \(\times\) 1, and \(Conv_{3\times 3}\) represents a convolution kernel of size 3 \(\times\) 3. \(g(a)=\frac{1}{WH}\sum_{i=1,j=1}^{W,H}a_{v}\), performs channel average pooling for the feature map \(\alpha\). The calculation formula of the \(F_{2}\) branch is as follows:  
\[x_{1}=Conv_{1+1}(x) \tag{6}\]  
\[Ms=h(Conv_{3+3}(x_{1}))\times x_{1} \tag{7}\]  
Among them, \(h(a)=\frac{1}{\zeta}\sum_{i=1}^{\zeta}a_{i}\) represents the average value computed across the channels of the feature map \(\alpha\).  
Figure 3: Parallel dual-branch attention module.  
Finally, the output expression of the PDA module is as follows:  
\[\text{Y}=\text{Conv}_{1*1}(x;M_{s}\times W_{c}) \tag{8}\]  
Among them, \(\text{Conv}_{1*1}\) represents a convolution kernel of size \(1\times 1\).