esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Method of Local Anode Effect Prediction for Large Aluminum Reduction Cell
###### Abstract  
A method of local anode effect prediction is proposed for the problem that it is difficult to detect the local anode effect in large aluminum reduction cell in real time. Firstly, a fuzzy classification of local anode effect prediction in terms of fuzziness level is proposed considering various working conditions of anode current in the region. Secondly, a current volatility detection method based on time-sliding window density is designed from the problem of uneven current distribution in the region, and the anode currents in the region are classified and tracked for prediction according to the different current volatility. Thirdly, an improved Gated Recurrent Unit (GRU) neural network structure is proposed to improve the prediction accuracy of fluctuating currents. Finally, simulation experiments are conducted based on actual data, and compared with Long Short-Term Memory (LSTM) and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), the proposed method has certain advantages in both prediction time, training time, and the mean absolute error (MAE) and mean square error (MSE), which verifies the effectiveness of the proposed method.  
aluminum electrolysis; local anode effect prediction; neural network; time series tracking prediction 20221212403. [[https://doi.org/10.3390/app122312403](https://doi.org/10.3390/app122312403)]([https://doi.org/10.3390/app122312403](https://doi.org/10.3390/app122312403))
## 3 Results and Discussion
### Microhardness Analysis of the Composite Interface  
Figure 13 shows the results of the microhardness test on the composite interface. As can be clearly seen from the graph, there is a variation in microhardness on both sides of the composite interface. On the carbon steel side, the microhardness first decreases and then increases, with the minimum hardness value (at 138 HV) occurring \(\approx\)25 \(\upmu\)m from the composite interface. On the stainless steel side, the microhardness initially increases and then decreases, finally tending to stabilise, with a maximum hardness of 218 HV at a distance of \(\approx\)20 \(\upmu\)m from the composite interface. In addition, from Figure 14b, the hardness value of the carbon steel substrate is \(\approx\)160 HV, which is slightly less than that of the stainless steel substrate (170 HV).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Test Point Serial Number** & **Cr (\%)** & **Mo (\%)** & **Si (\%)** & **Nb (\%)** & **Ni (\%)** & **C (\%)** & **Mn (\%)** & **Cr\({}_{\text{eq}}\)** & **Ni\({}_{\text{eq}}\)** \\ \hline
1 & 6.9 & 1.01 & 0.47 & 0.12 & 5.2 & 0.10 & 1.55 & 8.68 & 8.98 \\
2 & 7.7 & 1.0  
The microhardness version pattern of the composite interface is closely related to the organisation of the area. Combined with the analysis given in Sections 3.1 and 3.3, it can be seen that the minimum value of microhardness occurs in the decarburised zone on the carbon steel side, which is a pure ferrite phase, and the hardness of ferrite is much lower than pearlite. The microhardness maximum occurs in the composite zone on the stainless steel side, where a large amount of martensite is formed, as well as carbon-chromium compounds, which are much harder than the matrices of austenitic stainless steels.
# From Sensor Networks to Internet of Things. Bluetooth Low Energy, a Standard for This Evolution
## 6 Discussion
### Packet Loss Rate in CSR Devices  
As described above, packets are retransmitted three times in the CSR Mesh as a reliability measure and to ensure that the transmitted packets are received in different devices. When a device receives a packet, it retransmits this packet three times if it is not the destination device. If the same packet is received more than once, it is discarded and no action is taken. We have carried out a study to verify if this way of working is enough to provide the needed performance.  
A simple mesh network was deployed for this experiment. This network had a single broadcaster device and a single observer device (CSR1010). The broadcaster sent packets to the observer. The study has been carried out by evaluating the PLR according to the number of repetitions of each packet. A new packet was transmitted each 5 s for 3000 s. If the repetitions were configured in broadcaster, a new packet and its repetitions were transmitted each 5 s. This experiment tried to evaluate the impact of this measurement on the network, so that the devices were in optimal conditions for transmitting and receiving data: in a direct line of sight and separated 50 cm, minimizing the packet loss due to distance or obstacles.  
Table 1 shows the PLR for packets transmitted once, twice or three times. These results reflect an improvement when the number of transmitted packets grows. A PLR of approximately 16% was observed for packets transmitted only once; 3% when packets were repeated twice and 0% when the number of repeated packets was three. The need of transmitting three times is due to the fact that the BLE radio is not only used to receive packets, it has also to transmit advertisement packets (i.e., other devices can use this device like a bridge) and moreover, the radio must retransmit the received packets.  
As this basic experiment shows, the operating mode taken by CSR mesh topology based on repeating the packets 3 times ensures the arrival of all packets, but increases the traffic of the network, the power consumption and the packet collisions in a saturated wireless frequency band. It could trigger a scalability problem when the number of devices in the network increases.  
A new experiment was carried out to evaluate these new problems in a real network. In this new experiment, a network was deployed using a higher number of network devices to cover a real building, emulating a factory. However, before the network deployment, a coverage study was needed.
# General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning
## 3 Method
Depending on the network architecture, subsequent stages are composed of either convolutional blocks or enhanced convolutional blocks. In the second stage, the length of the spectrum is not changed. In each subsequent stage, the length of the spectrum is halved while the number of channels is doubled. And the convolution block in the later stage combines one-dimensional convolution and multi-head self-attention mechanism. The one-dimensional convolution and the multi-head self-attention mechanism are complementary. The sliding window of one-dimensional convolution is suitable for capturing local information from sequence data. The multi-head self-attention mechanism can simultaneously consider all positions in the sequence data and dynamically adjust the weights between different positions through attention mechanisms, making it more suitable for capturing global  
Figure 3: Network framework. information [35]. This design enables the network to extract spectral feature peaks while also capturing correlations between all such peaks. In the multi-head self-attention mechanism, the convolutional features extracted from the spectrum are divided into several equally sized blocks. Relative position encoding is then added to each block to record the position information of each spectral block. When integrating feature blocks, it is necessary to perform dot multiplication based on the number of attention heads and the initialized weight vector. The resulting vector is divided into multiple groups of Q, K, and V pairs, where Q, K, and V represent the trainable weights of each feature block vector. After training iterations, the network is adjusted based on the loss function. The dot product between different Q and K vectors is calculated to obtain their correlation, which is then scaled to the (0, 1) range using the Softmax function. Next, the dot product is performed across different V vectors to calculate the correlation between different blocks, determining their weights. Higher correlation corresponds to greater weight. The structure of the middle branch is similar to that of the upper and lower branches, but it no longer incorporates the multi-head self-attention mechanism. The input of each stage is a combination of the feature maps from the upper and lower branches, as well as the feature map from the middle branch at the same stage. This design enables the gradual integration of features from each spectrum at multiple scales, facilitating better learning of correlations between spectra. Finally, the feature vectors obtained by fusing the three branches are fed into two classifiers: one for component classification and one for concentration classification.
###### Abstract  
The stringent requirements of industrial communication, especially high reliability and real-time response, are regarded as the main bottlenecks for the widespread adoption of wireless technologies in industrial applications. The integration of 5G and Time-Sensitive Networking (TSN) protocols offers convergence of both wireless and various wired communication technologies for industrial applications. In this article, we describe our 5G and TSN integrated prototype, which achieves high reliability based on the IEEE 802.1 CB Frame Replication and Elimination for Reliability (FRER) scheme. Different 5G systems have been used in various combinations to empirically study the benefits of FRER for 5G communication in real industrial environments. We evaluate the performance of our prototype and validate it for an industrial use case on Smart Sensors for Milling Processes, requiring a latency below 10 ms for 99.99% of the packets sent, which has been achieved in the measurements using FRER. This use case and the high requirements towards latency and reliability demonstrate the benefits of 5G integration with FRER for industrial production.  
5G; frame replication and elimination for reliability; IEEE 802.1 CB; time-sensitive networking; TSN; ultra-reliable and low latency communications; mmW; over-the-air performance evaluation; manufacturing; smart sensors +
Footnote †: journal: Electronics  
0000-0001-5193-3227][PERSON]. [PERSON]
# Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks
## 5 Performance Analysis
### Overhead Analysis
#### 5.2.2 Storage Overhead  
Compare the storage overhead of DSEDA, PDF, and PKAEF. Here, the storage overhead of the elliptic curve domain parameters and the system parameters is overlooked, which are almost the same for all three algorithms. Thus, the key storage overhead is the main consideration. Assume that DSEDA, PDF, and PKAEF are all applied in the sensor networks shown in Figure 1. Suppose that there are \(Nc\) sensor nodes in a cluster, and a 160-bit elliptic curve is chosen for DSEDA, PDF, and PKAEF.  
Regarding DSEDA, each sensor node stores two unique symmetric keys shared with the sink and one symmetric key shared with the CH. Each CH stores two symmetric keys shared with the sink, \(Nc\)\(-\) 1 symmetric keys shared with the sensor nodes in the cluster, and a public/secret key pair. Suppose that the length of a symmetric key is 8 bytes, then the average key storage overhead of a cluster node in DSEDA is \(ST\_DSEDA=(3\times(Nc-1)\times 8+(Nc-1+2)\times 8+40+20)/Nc\) bytes = \(32+44/Nc\) bytes.  
Regarding PDF, each cluster node stores the system public key (40 bytes) and its secret share of the system private key (20 bytes). Furthermore, when PDF uses pre-computation to reduce the communication overhead for \(k\) sharing, then each cluster node is preloaded with the shares of different \(k\) during the deployment. Suppose that each cluster node stores the shares of different \(k\) for generating \(m\) reports, then the corresponding storage overhead is \(m\times Nc\times 20\) bytes. Thus, the total storage overhead of a cluster node in PDF is \(ST\_PDF=60+m\times Nc\times 20\) bytes. When PDF does not use the pre-computation method, then \(ST\_PDF=60\) bytes.  
Regarding PKAEF, each cluster node stores the cluster public key (40 bytes), its secret share of cluster private key (20 bytes), and \(x_{k}P\) of the other \(Nc-1\) cluster nodes (\((Nc-1)\times 40\) bytes). In total, the key storage overhead of a cluster node in PKAEF is \(ST\_PKAEF=40+20+(Nc-1)\times 40\) bytes = 20 + \(Nc\times 40\) bytes.  
Generally, DSEDA has a higher storage efficiency than PDF and PKAEF, but this is at the expense of the security of the private key. When PDF uses the pre-computation method, then PKAEF is more storage-efficient than PDF. Otherwise, PDF is more storage-efficient than PKAEF, but at the expense of higher communication overhead for report generation. Looking at PKAEF, storing \(x_{k}P\) of the other nodes in the cluster will increase the storage overhead, but this is desirable since it supports PKAEF to verify partial signatures.  
Finally, the comparison results of overhead are provided in Table 2, in which more \"*\" indicates higher overhead, and the key storage overhead of PDF is \"*\" when PDF does not use the pre-computation method, or \"****\" when PDF uses the pre-computation method.
## 6 Simulation Results and Discussions
In DEADS-SRC and DEADS-MRC with elliptical mobility pattern of MSs, data packets have to traverse multiple hops in order to get received at sink because of which their probability of being erroneous is high which results in packet drop. On the other hand, MSs are available for data reception mostly at one hop distance in linear sink mobility scenario because of which packet drop is reduced. Table 2 shows that packet drop rate of DEADS-SRC and DEADS-MRC is lowest with linear mobility pattern of MSs. PAR is shown in Figure 14. DBR and EEDBR have the lowest PAR as the number of packets received at sink are less due to single link having poor link state most of the time. iAMCTD shows higher PAR than DBR and EEDBR as it selects forwarder nodes on the basis of their respective link's SNR. This results in high throughput and increased PAR. Initially CoDBR attains higher PAR as compared to DBR, EEDBR and iAMCTD which later at decreases as throughput decreases. PAR is higher in DEADS-SRC and DEADS-MRC with linear mobility pattern of MSs as the number of packets received at sink in this scenario is high and packet drop is low. Figure 10: Lifetime with linear and elliptical sink mobility patterns. Figure 11: Residual energy possessed by the networks with linear and elliptical sink mobility patterns. Figure 12: Packets received at MSs in the networks with linear and elliptical sink mobility’patterns. Figure 13: Packet drop attained in the networks with linear and elliptical sink mobility patterns. Table 2 shows the improvement attained by our proposed protocol with respect to network lifetime, throughput and packet drop. Zero indicates that both protocols have same value for that specific evaluation metric. Negative value indicates reduced value of a particular metric with respect to compared  
Figure 14: PAR attained in the networks with linear and elliptical sink mobility patterns. protocol. Network lifetime of the proposed scheme is same as iAMCTD but outperforms DBR and EEDBR. Highest throughput and lowest packet drop is achieved by DEADS-SRC and DEADS-MRC with linear mobility pattern of MSs.
## 7 Discussion and Conclusions  
This research work analyzed the behavior of three optimizers when applied to the inverse problem of DOT regarding the speed of convergence and quality of reconstruction. The three optimizers under study, namely Nadam, Adam, and AmsGrad, are enhanced versions of the simple gradient descent algorithm, and have proved to perform very well in solving optimization problems in other areas of applications, especially in Deep Learning model search. The study we performed is based on a carefully designed randomized numerical simulation that aimed to gain credible statistical evidence on the actual performance of these optimizers when applied to solving the DOT inverse problem. We focused our attention on the impact of number of inclusions, the learning rate choice, and initial guess error on the speed of convergence. We also considered the impact of these same parameters on the quality of image reconstruction. The results derived using mainly non-parametric statistical approaches provide a scientifically credible quantification of the actual performance of these optimizers, with respect to the choice of learning rate, and under the constraint of the true numbers of inclusions and the arbitrariness of the initial starting point of the optimization.  
The study provided valuable guidelines in terms of statistical evidence of the importance of the good choice of the learning rate for the three algorithms, and statistically proved the robustness of Nadam and Adam to the initial guess and the number of inclusions; these results can help improve and promote further the application of DOT in practical medical applications. However, we did not study the impact of these parameters on the simultaneous reconstruction of the absorption and scattering coefficients.  
In future work, we aim to combine Nadam and AmsGrad and construct an algorithm that switches back and forth between AmsGrad and Nadam in a controlled fashion, where the AmsGrad optimizer will be used to accelerate the speed of convergence and Nadam to obtain a good quality of reconstructed images.  
[PERSON] conceived the idea, executed the simulations, analyzed the data, and wrote the paper. [PERSON], A.L. and M.A. supervised the findings of this work, provided ideas and directions, analyzed the data, and reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 Discussion
### Chair Type and Postural Outcomes  
Contrary to our third hypothesis, the chair type did not significantly influence postural variability for any of the joint angles measured. This finding challenges the expectation that more supportive or adjustable chairs, such as ergonomic chairs, would lead to more consistent posture adoption during 'correct' sitting and greater variability during 'comfortable' sitting. Instead, postural variability remained high across all chair types, suggesting that intrinsic factors such as individual differences in proprioception, comfort thresholds, and motor control may play a larger role in postural variability than chair design alone [21; 31]. The market for ergonomic chairs has grown significantly, valued at USD 10.4 billion in 2024 and projected to reach USD 19.12 billion by 2033, as awareness of health and wellness in workplace environments continues to rise, reflecting growing recognition of the impact of seating design on health outcomes [49].  
However, the chair type did significantly affect mean joint angles. Specifically, HI decreased progressively from the stool (165.3\({}^{\circ}\)) to the computer chair (158.2\({}^{\circ}\)) to the ergonomic chair (153.0\({}^{\circ}\)), indicating that back and head support may encourage a more forward head posture. This effect could be a result of users adjusting their head position to maintain gaze orientation while reclining or leaning back, which may increase cervical spine loading [50; 51]. Additionally, we observed a significant interaction between the chair type and sitting strategy for TA. While 'correct' sitting promoted a more upright trunk in the computer and ergonomic chairs, the opposite was observed with the stool, where 'correct' sitting resulted in the most extended trunk posture. This interaction suggests that individuals' perceptions of 'correct' posture are influenced by the biomechanical affordances and constraints of the seating surface [52].
## 2 Methods
### Large Object Detection Algorithm  
The goal of the large object detection algorithm is to distinguish large structures within the collected images. Each 3D volume image collected by our OCT device was a tilt stack of 417 image slices (B-scans). Each slice represented a 2D cross-section of the volume with the X dimension on the horizontal axis and the Z dimension on the vertical axis. The Y coordinate of each image represents its order in the stack. Each image slice was analyzed sequentially.  
First, the image was thresholded to convert each pixel to either black or white, to highlight structures. A higher threshold value resulted in fewer potential objects. This made the process faster, but also lowered accuracy and increased the chances of cropping out portions of the TM that were less intense. Then, the image was blurred to reduce the chance of one object being split up into multiple smaller objects. However, increasing the blur value too much increased the risk of artifacts or middle ear structures appearing to be part of the TM. Next, each large aggregation of white pixels was identified and contour detection was performed. Finally, the size of each aggregation was calculated. If it was higher than the minimum size we defined, the aggregation of white pixels was termed an \"object\" and the coordinates were saved. A higher minimum size requirement sped up the algorithm but could miss some of the TM objects. Thus, there were three parameters we considered when detecting objects: the threshold value, the blur value, and the minimum size. We iteratively varied these parameters until we achieved a decent balance between accuracy and sped.  
From the images collected from the eight participants used for training, this algorithm identified roughly 45,000 large objects. We then went through these manually one-by-one and found that ~43,000 objects were non-TM and ~2000 objects were TM. The reason that there were fewer TM objects was that about half of the slices within each volume stack contained the TM and this would be identified as only one object. In contrast, each slice had multiple distinct artifacts, each identified as separate objects.
# Detection of Thermal Strain in Steel Rails with BOTDA
## 3 Results and Discussion
### Vertical Distribution of Thermal Strain on the Rail Specim  
As indicated from Figure 2, the lateral profile of the rail sample is not regular and, in order to precisely verify the climate adaptability of the rail using the Brillouin frequency shift of the fiber glued on it, thermal-strain distribution should be taken into consideration. During the lab test, five loops of optical fiber were fixed in different positions of the rail to quantify this effect. The Brillouin frequency variation with the temperature of Fiber B in different vertical positions on the rail can be found in Figure 5a, where Positions 1-5 represent different positions from the bottom to the top. The Brillouin coefficient is defined as the ratio of the Brillouin frequency shift with temperature for simplicity. The Brillouin coefficients at different positions are also shown in Figure 5b, with the error bar and the dashed line representing the average value of all the Brillouin coefficients. According to the figure, the deviations from the average value of all values in different positions are within measurement accuracy, which indicates the position independence of the thermal strain.