esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulation-Based Implementation, Experimental Results and Discussion
### Packet Overhead Comparison between E-BIOSARP, BIOSARP and SRTLD  
The average packet overhead results for E-BIOSARP, BIOSARP, and SRTLD are compared in Table 8. Figure 13 shows the large differences between E-BIOSARP and BIOSARP on the one hand and SRTLD on the other hand. The results for E-BIOSARP are very close to the results for BIOSARP. The results for E-BIOSARP justify the reduced rediscovery and calculation process, which causes the overhead per packet received to be much less with E-BIOSARP, even when the WSN suffers from malicious activity.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Packet Rate (Packets/s)** & **E-BIOSARP without** & **E-BIOSARP with** & **BIOSARP without** & **SRTLD without** \\
**Malicious Nodes** & **Malicious Nodes** & **Malicious Nodes** & **Malicious Nodes** & **Malicious Nodes** \\ \hline
1 & 0.1142 & 0.1245 & 0.1087 & 0.13 \\
2 & 0.19953 & 0.185057 & 0.1789 & 0.19856 \\
4.84 & 0.314607 & 0.32807 & 0.3113 & 0.374 \\
6.38 & 0.44784 & 0.4458 & 0.41201 & 0.47 \\
10.64 & 0.68321 & 0.6625 & 0.62102 & 0.72 \\
**Average** & **0.3518774** & **0.3491854** & **0.326386** & **0.378512** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Energy consumption comparison.  
Figure 12: Energy consumption comparison.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Packet Rate** & **E-BIOSARP without** & **E-BIOSARP with** & **BIOSARP without** & **SRTLD without** \\
**(Packets/s)** & **Malicious Nodes** & **Malicious Nodes** & **Malicious Nodes** & **Malicious Nodes** \\ \hline
1 & 14.7702 & 21.483 & 13.875 & 64.1652 \\
2 & 9.483 & 8.60725 & 8.274 & 18.65 \\
4.84 & 2.473 & 2.283 & 1.952 & 6.543 \\
6.38 & 2.1063 & 1.6626 & 1.7739 & 8.8 \\
1
## 4 Evaluation Method
### Evaluation Protocol and Metrics  
The datasets were split into 80:20 for training and testing, with a 10% validation set, using 5-fold cross-validation [37]. The evaluation metrics included mean average precision, F1-score, Hamming loss, coverage, ranking loss, and exact match [38, 39].  
The system was implemented using TensorFlow, Keras, and PyTorch in Anaconda Navigator (2022) on an Intel(R) Core(TM) i9-9900K CPU with a GPU with 32 GB memory and 64.0 GB RAM.
# An Adaptive Process Monitoring Method Based on an Improved ADALINE Neural Network
## 4 Experiment and Results Analysis  
### Experimental Details  
In this section, real experiments were performed to verify the availability and validity of the proposed approach by detecting surface roughness conditions. The experimental process is shown in Figure 7. The proposed approach is coded in software and runs on a server with a 2.40 GHz processor and 64 GB RAM. The experiments were designed to collect supporting data to analyze the feasibility of the proposed approach. Surface roughness can affect the wear resistance and fatigue strength of the parts and the matching properties between the parts. In advanced manufacturing fields such as aerospace and microelectronics, high-precision parts are needed; thus, a reasonable range needs to be ensured. The design experiment uses the proposed approach to control the surface roughness quality of the processed parts. The vibration signals were collected during cutting, and the sampling frequency is 50 kHz. Machining was conducted with a spindle speed of the cutter of 600 r/min, feed rate value of 0.25 mm/r, and cutting depth value of 1 mm. Each machining length in the axial direction was 4 mm, and the vibration signals during this period were collected as a sample. According to the designed experiment, 300 sets of data are first collected as historical data; then, under the same working conditions, 100 sets of data are collected for verification.  
Figure 6: Flow chart of the control limit update model.
## 3 Results and Discussion
### Comparison of Extraction Results with Effluent and Influent Data  
In Table 2 we have included wastewater influent and effluent analysis results for the investigated APIs from Kristianstad STP, performed by us according to our previously published methodology [34; 35]. The first observation was that all 23 APIs were present in the _influent_ water. Likewise, all investigated APIs except sulfamethoxazole were present in the sewage sludge samples. In the effluent samples, all APIs except ketoconazole and paracetamol could be detected. Based on results from Figure 4 and Table 2 it is likely that the efficient reduction of ketoconazole in the STP primarily is caused by sludge adsorption. Paracetamol on the other hand show much lower sludge concentrations, and the huge loss of paracetamol, with the highest influent values, must be explained by either abiotic or biotic degradation/transformation, or a combination thereof.  
Evidently the sludge concentrations measured in PHWE and presented together with two physicochemical properties in Figure 4, and inlet and outlet concentrations cannot fully explain the observed pattern. Yet, a few more chemical remarks can be made in favour of using charge and aromaticity as valuable additional tools in describing sludge sorption apart from primarily looking at more well-established classical parameters such as log Kow and log \(D\).  
For example, in Table 2 high log \(K\)ow and log \(D\) values can be found for ketoconazole, but not for ciprofloxacin and citalopram. Here charge may aid in better explaining the high concentration of the two latter on sludge. Metoprolol and carbamazepine have approximately the same influent and effluent concentrations, yet metoprolol is 3.5 times more abundant in sludge compared to carbamazepine, despite that log Kow and log \(D\) is higher for carbamazepine than metoprolol. This again shows that the positive charge might have a strong impact on sorption. These results show that neither log Kow nor log \(D\) alone are valid tools too fully describing APIs adsorption to sludge.
# Data Analytics for Optimizing and Predicting Employee Performance
## 3 Methods and Results
### Data Modeling
#### 3.4.1 Cross-Validating the Models  
In the initial step, after having chosen the previously mentioned algorithms, the research study will use the considered dataset to cross-validate these models and observe the results. While there are multiple ways to achieve this, one of the most popular methods used for classification algorithms is the k-fold cross validation technique, together with leave-one-out. When working with bigger datasets, k-fold should be considered as the better option, because the accuracy obtained from the training data is generally too optimistic [34]. When using this option, the method will randomly split a dataset into k separate, roughly equal-sized segments. For each iteration, one segment is used as the test set, while the remaining k - 1 folds are combined to form the training set. A classification algorithm is then applied to this training set to create a model, which is tested on the test set. The algorithm's overall performance is determined by calculating the average accuracy across all k iterations.  
For this particular research, the value for k was considered to be five, which is optimally used for similar tests, and the results obtained for all the chosen algorithms can be seen below in Table 2.  
Having obtained these results, these will indicate the overall performance that could be achieved by providing more accurate measures of these models' real-world effectiveness.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Average Score** \\ \hline Decision trees & 73\% \\ \hline Random forest & 82\% \\ \hline SVM & 78\% \\ \hline KNN & 77\% \\ \hline ANN & 78\% \\ \hline XGB & 79\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cross-validation results for chosen algorithms.
## 4 Experimental Testing
### Manufacturing Workshop Edge Data Processing Test  
In order to test the feasibility of the edge-computing method proposed in this paper in actual manufacturing production, this section conducts real-time data collection and analysis in the actual production process of the assembly experimental platform, and the results are shown in Figures 13 and 14. In the results, it can be seen that when the data acquisition end detects that the equipment data have changed, its data will be aggregated into the edge-computing program, and through the logical analysis and judgment of the program, real-time monitoring of the production process and inventory management can be achieved.
## 2 Literature Review
### State-of-the-Art Architectures for YOLOv10  
In YOLO-based object detection, the backbone architecture is responsible for feature extraction--processing input images to derive meaningful features for object classification and localization. Historically, CNN-based backbones such as CSPNet, EfficientNet, and ConvNeXt have been popular for real-time equipment detection due to their balance of accuracy and speed [5]. Recently, transformer-based architectures (e.g., ViT, Swin Transformer, PVT, MobileViT, and Axial Transformer) have gained traction, offering enhanced accuracy by capturing both local and global dependencies via self-attention [16]. However, this advantage typically comes at the expense of computational efficiency, which can limit real-time applications [17].  
In challenging tasks such as construction equipment detection, where small objects and occlusions are common, the extra representational power of transformer backbones may yield higher accuracy, yet they also incur increased inference times [7; 18; 19]. Consequently, selecting the right backbone depends on the specific goals of a project. A highly efficient CNN-based approach might be preferable when real-time monitoring takes precedence, even if its accuracy is slightly lower. Conversely, transformer-based backbones may be more suitable when complex feature relationships must be captured--provided that computational resources and latency constraints can accommodate the additional overhead.  
In light of these considerations, this research proposed comparing transformer-based backbones--ViT, Swin Transformer, and PVT--all integrated within a YOLOv10 framework. The objective was to evaluate their applicability and performance in real construction site scenarios, ultimately determining which models strike the best balance between detection accuracy and computational efficiency for practical field use.
## 4 The New Hybrid Optimization Algorithm
### The LM-D Algorithm  
Due to the inherent limitations of the previously mentioned algorithms, they may not be entirely suitable for the optimization problem outlined in this paper. Given the unique characteristics of robot calibration in practical scenarios, local optimization algorithms typically suffice to converge to the global optimum in most cases, leading to calibration parameters that deviate from the actual parameters. In such situations, the incorporation of global optimization algorithms becomes necessary to ensure convergence to the global optimum.  
Hence, we propose the development of a hybrid algorithm, denoted as the LM-D algorithm, to tackle this issue. The LM-D algorithm integrates the L-M algorithm with the DIRECT algorithm in a mutually iterative process aimed at achieving the global optimum. This approach effectively harmonizes the local search efficiency characteristic of the L-M algorithm with the global convergence capability of the DIRECT algorithm. The complete hybrid algorithm comprises two distinct stages: the local minimization stage and the escape from local minima stage. In the local minimization stage, the L-M algorithm is applied for local optimization, followed by the utilization of the DIRECT algorithm to escape from local minima. Initially, we utilize the L-M algorithm to ascertain a local solution. Subsequently, in order to surpass the constraints of this local optimum, we employ iterative steps through the DIRECT algorithm to unveil a solution superior to the current local optimum. This newly discovered point serves as the initial reference for commencing a new search utilizing the L-M algorithm. This cyclical process persists until the algorithm converges to the global optimum, a condition met when the parameter \(\varepsilon\), functioning as the threshold for the global optimum, is satisfied. Typically, the accuracy of robotic following absolute calibration hovers around 0.2 mm. When the iterative result falls below 0.1 mm, the iteration is considered to have converged to the global optimum, and the current iterative result is accepted.  
This method does not necessitate an exceedingly accurate initial solution and is capable of initiating iterative calculations from any starting position. It demonstrates rapid convergence, even when the initial solution is not in close proximity to the problem's solution, showcasing robustness in its performance. In response to the characteristics of robotic TCP calibration, we have implemented the following improvements to the aforementioned two algorithms.
### Paper's Contribution  
The ambitious features and capabilities of SB communication system make it a candidate for providing aircrafts with real high-speed Internet connectivity. Therefore, in this paper, a novel stratospheric communication network is proposed for broadband high-speed Internet provision for aviation applications. The proposed network is demonstrated, and its coverage geometry and structure are modelled. As the provision of high-speed Internet connectivity is mainly impacted by the atmospheric attenuation, therefore, the proposed network performance at 5G mm-wave bands is analyzed and investigated at different atmospheric attenuation sources such as the permanent atmospheric gaseous absorption and other temporary rain and fog/cloud attenuations to stand on the feasibility of the system and choose the appropriate and reliable operational frequency bands. Finally, the proposed SB-aviation communication network aspects are compared with the very recent satellite systems for global Internet provision for aviation.
## 2 Materials and Methods
### Methods
#### 2.3.3 Validation of Downscaled SIF  
The SIF from the SCOPE 2.1 model was compared with the downscaled SIF at the two flux sites to assess the effectiveness of the downscaled SIF. SCOPE is a one-dimensional model capable of simulating radiation transfer, energy balance, and photosynthesis, as well as SIF from individual leaves within the canopy and the total emitted SIF spectrum of chlorophyll fluorescence [49]. It is widely used for seasonal SIF estimation. The simulation range was set to 500 m around the flux towers, assuming homogeneity of the forest canopy and uniform atmospheric conditions within this range. The input parameters for the SCOPE model in this study are detailed in Appendix A. To match the timing of the downscaled SIF, the simulated half-hourly SIF was aggregated to 8 days.
# Parameter-Free Ordered Partial Match Alignment with Hidden State Time Warping
## 2 Materials and Methods
### Hyperparameters  
In addition to the hyperparameters that DTW has, HSTW has three additional hyperparameters: \(\gamma\), \(\alpha\), and \(\beta\). These three hyperparameters have clear interpretations: \(\gamma\) is a skip penalty for a horizontal transition, \(\alpha\) is a skip penalty for a vertical transition, and \(\beta\) is a plane transition penalty. These hyperparameters can be set to achieve different types of behavior. We describe our method of setting these hyperparameters for our two tasks of interest.  
We set the hyperparameters on our (subsequence alignment) speech verification task to perform well in the presence of common tampering operations such as insertions, deletions, and replacements. For the deletion penalty \(\gamma\), we found that the optimal setting is a small, positive number. We want the cost to be small in order to allow skipping in the reference dimension when there is a deletion edit in the query. If we set \(\gamma\) to zero, however, it allows for arbitrarily long skips, which has undesirable behavior. We use \(\gamma=3\) in all of our reported results, and we find that system performance is relatively insensitive to values within the range \(0.5<\gamma<10\). For the insertion penalty \(\alpha\), we found that the optimal value strongly depends on the values in the cost matrix. If \(\alpha\) is too large, the algorithm cannot follow vertical alignment paths corresponding to insertions. If \(\alpha\) is too small, the optimal subsequence path consists of simply skipping across the entire cost matrix. Ideally, we want the penalty to be greater than the cost between two matching elements but smaller than the cost between non-matching elements. Accordingly, we set \(\alpha\) in a data-dependent way: we calculate the minimum cost in each row of the cost matrix \(C\) (i.e., the minimum cost for each query frame), and then set \(\alpha\) to \(c_{\alpha}=2.4\) times the median of these row minima. We find system performance to be insensitive to values across the range \(2<c_{\alpha}<3\). For the hidden-to-visible transition penalty \(\beta\), we find that \(\beta=c_{\beta}*(\gamma+\alpha)\) with \(c_{\beta}=33\) works well, and that system performance is insensitive to values within the range \(30<c_{\beta}<40\). This penalty prevents the alignment path from coming up to the visible plane unless it can \"consume\" enough low-cost elements.  
We set the hyperparameters on our music alignment task in the same manner but with one difference: we always set \(\gamma=\alpha\) since the alignment task is symmetric. Based on our training data, we used \(c_{\gamma}=10\) and \(c_{\beta}=150\).
# Energy Efficient Routing Protocol in Sensor Networks Using Genetic Algorithm
###### Abstract  
In this paper, we examine routing protocols with the shortest path in sensor networks. In doing this, we propose a genetic algorithm (GA)-based Ad Hoc On-Demand Multipath Distance Vector routing protocol (GA-AOMDV). We utilize a fitness function that optimizes routes based on the energy consumption in their nodes. We compare this algorithm with other existing ad hoc routing protocols including LEACH-GA, GA-AODV, AODV, DSR, EPAR, EBAR_BPS. Results prove that our protocol enhances the network performance in terms of packet delivery ratio, throughput, round trip time and energy consumption. GA-AOMDV protocol achieves average gain that is 7 to 22% over other protocols. Therefore, our protocol extends the network lifetime for data communications.  
Keywords:index terms--AODV; AOMDV; DSR; energy; genetic; optimization; performance; routing; sensors; network +
Footnote †: : 2021
# Improved Food-Processing Techniques to Reduce Isoflavones in Soy-Based Foodstuffs
## 3 Results  
### Laboratory Scale  
The results of the statistical analysis at laboratory scale can be found in Figure S1 in the Supplementary Materials, which exposes the five factors tested, and their values. It can be seen that only two factors, the temperature and the matrix form, seem to influence mainly the IF removal rate. As for the matter:water volume ratio, contact time and stirring speed, their variations did not result in significant differences in the IF removal rate (\(p>0.05\)). The latter appears to be optimal at room temperature and for the smallest particles. With regard to the effect of the nature of soy matter, the data indicate that bounded dehulled beans give the best IF removal result, followed by soy cake (formed from crushed beans) and finally dehulled beans. However, the first two were not retained, because firstly, bounding was not seen as a manageable solution at pilot or industrial scale, and secondly, the treatment water in the case of soy cake was highly turbid, indicating that a large quantity of soluble matter had leaked into it.  
As shown in Figure 1A, at laboratory scale, the mean IF removal from raw soybeans was \(14.7\pm 5.23\%\), while it was \(42.5\pm 7.56\%\) for dehulled beans. This effect was obtained regardless of the other experimental conditions, in line with the statistical Taguchi analysis (Figure S1), from which the type of soy matter was identified as one main influencing factor. Indeed, it was not possible to significantly reduce IF content in whole soybeans, while up to 60% of the initial GEN could be removed by working on dehulled beans at room temperature for 10 min. It appeared that the beans' envelope could limit extraction of IFs.
## 1 Introduction
### Literature Review  
In wireless channels where, in addition to thermal noise, there is also multipath signal propagation, the problem of imperfect phase estimation from the received signal becomes even more actual. A number of distinct statistical models have been proposed so far in order to represent the effect of multipath fading, such as Rayleigh, Rice, Nakagami-\(m\), Nakagami-\(q\), etc. [2]. When multipath fading follows Nakagami-\(m\) distribution, its influence on error probability in conjunction with phase noise is considered in [3], while shadowed multipath fading is analyzed in a similar scenario in [4].The influence of the phase noise on the error probability when multipath fading is modeled by Nakagami-\(m\) distribution was considered in [3], while the effect of imperfect reference signal extraction on the error probability over a shadowed multipath fading was analyzed in [4]. [PERSON] and [PERSON] in [5] presented a novel treatment in estimating the effect of phase noise on error performance of binary phase-shift keying (BPSK) and quaternary phase-shift keying (QPSK) with equal gain combined under Rayleigh, Rician and Nakagami-\(m\) fading environment. These papers show that communication systems performance can be significantly impaired by imperfect reference carrier recovery in addition to multipath fading effects alone, when signals propagate over fading channels of Rayleigh, Rician, Nakagami-\(m\) and shadowed Nakagami-\(m\) type.  
Recently, several papers suggested models for mm-Wave channels [1; 7; 8; 9; 10; 11; 12; 13]. All of these distributions are from the group of fluctuating multiple-ray models [1]. In a case when two dominant line-of-sight (specular) waves reach the receiver, alongside the diffuse wave components of the transmission, there is a suitable fading model of so-called Two-Way Diffuse-Power (TWDP) that was initially proposed by [PERSON] et al. in [7]. In the paper that introduces the TWDP model, the authors also present an approximate expression for probability density function (PDF) regarding the statistical variations of the signal envelope. The fading model has been continuously studied by other researchers [8; 9; 10; 11; 12; 13]. In [8], [PERSON] et al. discuss shortcomings of approximate expression from [7], and derive exact and suitable approximate expressions for determining bit error rate (BER) in cases when the BPSK signal is detected after being transmitted over a TWDP channel while retaining large SNR values. In [9; 10], [PERSON] et al. develop novel expressions for system performance metrics and present an interesting result showing that the received signal envelope conforming to the TWDP fading model encompasses a closed-form moment generation function (MGF). The authors of [11; 12] propose a novel aspect on TWDP channel model parameters that differs in comparison with parametrization formerly used in [7; 8; 9; 10]. This parametrization is actually based on formula originally derived in [14]. An experimental analysis has shown recently that this fading model is well suited for signal description in outdoor and indoor propagation environments in the 60 GHz range [13].