esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Analysis  
### Experimental Materials and Methods  
To further validate the simulation findings, a physical prototype test was carried out in Longxin Village, Jinde Town, Liujiang District, and Liuzhou City. The variety of sugarcane used for this test was Gui Sugar 44, and the diameter of the sugarcane ranged from 25 to 32 mm, with each cut off at the root position. To exclude the interference of other factors on the validation test as much as possible, the length of the whole stalks of the sugarcane used for the test was similar, which was about 2.5 m, and the stems of the sugarcane were in a straight culm state, without obvious bending. After harvesting, sugarcane should be transferred for leaf stripping as soon as possible to avoid affecting the test results due to dehydration. The primary test apparatus consists of a digital display level meter (range \(\times\)90\({}^{\circ}\), accuracy 0.1), an electronic scale (range 0-300 kg, precision 0.01 kg), a tape measure, and a DT-2234B photoelectric tachometer (range 0-99,999 r/min, precision 0.1 r/min). The functional components, such as thickening the cane collection plate and modifying the bracket mounting position, are correctly adjusted based on the actual installation and operation, provided that the influence of sorting and conveying parameters stays constant. Figure 10 depicts the test configuration.  
Based on the simulation results, set the sprocket wheel speed to 24 r/min and the inclination angle of the cane collection plate of the sorting and conveying device to 35\({}^{\circ}\)  
Figure 10: Conveyor for sorting.  
Figure 9: Estimated marginal means for three- sugarcane proportions.  
Then, when the device is operating steadily, the manipulator begins to pick up the sugarcane that is scattered on the loading platform. Since sugarcane leaf stripping equipment uses cane tail feeding first, the sugarcane's tail must point in the same direction as the straight conveying channel when loading. Three complete revolutions of the sorting mechanism (36 rotations total) were used as a series of tests, and records were kept beginning with the second time the sorting hook claw gripped the sugarcane. Ten sets of tests were conducted under typical working conditions, and the number of times the sorting hook claw hooked and the number of sugarcane roots it hooked each time were recorded. The percentage of sorted sugarcane roots for each test group, based on the experimental data that were recorded, was determined, then the validation test was examined. The percentage of sorted roots may be computed using the formula below:  
\[\varepsilon_{i}=\frac{N_{i}}{N_{0}} \tag{15}\]  
where \(N_{i}\) is the number of times \(i\) cane was sorted, and \(i=0\), 1, 2, 3, 4...
## 3 FAQ Classification Using an Encoder-Decoder Neural Network
The first output \(T_{0}\) (the final output vector of the special [CLS] token) of the transformer's encoder is input as an initial value of the RNN decoder, implemented by a gated recurrent unit (GRU) [17] with [PERSON]'s encoder-decoder attention mechanism [18], after passing through a fully connected neural network (FNN). Figure 4 shows the RNN decoder with [PERSON]'s encoder-decoder attention mechanism in detail. Figure 4: Recurrent neural network (RNN) decoder with [PERSON]’s encoder-decoder attention mechanism. As shown in Figure 4, each attention weight \(a_{i}\) is induced by inner products between each output \(T_{i}\) of the transformer's encoder and the first hidden state \(h_{o}\) of the RNN decoder. The attention weights mean how much each output \(T_{i}\) is associated with the hidden state \(h_{o}\). Then, the context vector \(c\) is constructed by the weighted sum of \(a_{i}\) and \(T_{i}\). Finally, the RNN decoder generates an output vector \(V_{o}\) using the FNN-encoded input sentence \(FNN(T_{0})\), the start symbol \(<\)\(S\)\(>\), and the context vector \(c\), as shown in the following equation:  
\[V_{o}=Dec(FNN(T_{0}),
,c). \tag{5}\]  
To supplement the output vector \(V_{o}\) with domain-specific knowledge, we adopt a domain embedding scheme proposed by [14]. We define one class embedding vector per FAQ category, as shown in the following equation. \[V_{C_{t}}=mean(\sum_{k}e_{k}), \tag{6}\]  
where \(V_{C_{t}}\) is a class embedding vector that is calculated as an average of the word embedding vectors, \(e_{k}\)'s, in sentences belonging to the \(t\)-th FAQ category. Finally, to classify input sentences into FAQ categories, we use an FNN. The vector of inner products between the output vector \(V_{o}\) of the RNN decoder and the class embedding matrix \(V_{C}\) is used as an input vector of the FNN.
# Optimization of Laser-Assisted Polypropylene Aluminum Joining
###### Abstract  
Laser joining of polymers to metals is a rising research subject due to the potential of considerably reducing the weight of structures. This article deals with the laser joining process between polypropylene and aluminum. Without pre-treatment, laser joining of these materials is not feasible, and the method applied in this study to circumvent this issue is a surface modification of aluminum with a pulsed laser to create mechanical interlocking for the heat conduction laser joining technique. Different patterns and various laser parameters are analyzed with the design of experiments to best understand the effects of each parameter along with microscopic observations. It is found that engraving weakens the mechanical properties of the aluminum samples. The compromise between the engraving depth and the mechanical properties of the samples is optimized, and the engraving process with a 0.28 mm line width, 27.3% density and 150 mm/s speed provides the highest mechanical performance of the assembly with minimum degradation of aluminum samples. Moreover, by adjusting the laser power and using power modulation below 300 W, the decomposition of polypropylene occurring at high temperatures is reduced to a minimum. After the final optimization, the joined samples reliably withstand a maximum force of 1500 N, which is, approximately, a shear strength of 20 MPa.  
laser joining; thermal degradation; laser engraving; design of experiments Article
# Slotted Circular-Patch MIMO Antenna for 5G Applications at Sub-6 GHz
## 3 A 2-Port MIMO System Using Two Flipped Parallel 1 \(\times\) 2 Arrays Antenna
### Performance Comparison of Two 1 \(\times\) 2 Arrays MIMO with Previous Works  
The comparison in Table 5 highlights the performance of the proposed MIMO of two 1 \(\times\) 2 arrays against existing sub-6 GHz 5G antennas. The suggested antenna exhibits higher gain, radiation efficiency, and a wider operational frequency range compared to the previously published antennas [36; 37; 38]. Although the antennas in ref. [27; 39] are relatively smaller in size compared with the proposed antenna, the proposed antenna achieves a greater gain and wider operational bandwidth than it. Additionally, the proposed design presents the smallest value of ECC, which means the best MIMO performance. The proposed MIMO of two 1 \(\times\) 2 arrays design improves max percentage gain compared with previous designs [27; 38; 39] by 26.5%, 43.1%, and 277%, respectively, achieving maximum radiation efficiency of up to 95%.
# Integration of a Three-Dimensional-Printed Titanium Implant in Human Tissues: Case Study
## 4 Discussion
A temporary reduction of the implant to the bone defect was easily achieved by inserting the bony end into the skirt during screw fixation. Moreover, both ends of the host bone did not present cortical atrophy in serial plain radiographs during a 1-year postoperative follow-up evaluation. Several studies have reported the occurrence of cortical atrophy, a common bone remodeling pattern, at the end of the host bone following limb salvage surgery for long bone extremities [28; 29; 30; 31]. Cortical atrophy can result from stress shielding, surgical damage of the periosteum, or impaired blood supply. To protect against cortical bone atrophy, the skirt of the presenting 3D-printed implant surrounding the host bone was designed to provide a large bone contact surface for osseointegration and even stress distribution. However, postoperative follow-up evaluation after one year revealed that the small gap between the rough surface of the skirt and the host cortical surface induced the growth of fibrous tissue rather than new bone formation. Therefore, long-term follow-up evaluation is needed to determine whether this fibrous tissue induces cortical atrophy or cortical bone thickening. Considerable soft tissue adhesion around the implant and fibrous scar tissue was observed. The reattachment of soft tissues to the implant, including tendons, ligaments, joint capsules, and muscles, using the Trevira tube (Attachment tube; Implantcast, Buxtehude, Germany) served as a reliable surgical alternative [25]. However, 3D-printed implants with a rough surface (roughness average of 48 nm) showed direct and robust attachment to the soft tissue without the need for additional material. Moreover, rough and smooth surfaces can be customized throughout 3D-printed implants, such that they are expected to be selectively attached to muscle and joint capsules while preventing the adhesion of neurovascular bundles. Further clinical experience is required to determine whether the histological characteristics of soft tissue adhesion facilitate functional rehabilitation. This study has some limitations. This was a case report of only one patient. Because of ethical considerations, it is difficult to further investigate the tissue integration of 3D-printed implants in humans. To our knowledge, no study has quantitatively analyzed tissue integration of implants in humans. Another major limitation of this study is that we did not perform mechanical tests to compare the adhesion strength and tensile force. Mechanical experiments and histological evaluation could not be performed because of the limited availability of specimens. Ultimately, a histological quantitative analysis was accurately performed as it was considered to be more valuable than mechanical tests, which can damage adhesive tissues.
# The Effect of Carbon Nanofibers Surface Properties in Hydrogenation and Dehydrogen Reactions
###### Abstract  
In this study, carbon nanofiber-supported Pd nanoparticles were used in the hydrogenation of cinnamaldehyde and in the dehydrogenation of cinnamyl alcohol. The different graphitisation of the surface of the nanofibers and the amount of oxygen functionalisation significantly affected both activity and selectivity to the various reaction products. In particular, a decrease in nanoparticle dimensions and oxygen content resulted in an increase in overall activity for both of the studied reactions. Moreover, the selectivity to hydrocarbonnamaldehyde enhanced with increasing surface oxygen content in the cinnamaldehyde hydrogenation, while the selectivity to cinnamaldehyde was higher with low-functionalised nanofibers in the cinnamyl alcohol dehydrogenation. Finally, the most active catalyst proved also to be stable in consecutive runs.  
Keywords:palladium; carbon nanofibers; cinnamaldehyde; hydrogenation; cinnamyl alcohol; dehydrogenation +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Data Treatment and Chemometric Analyses  
Deconvolution and spectral alignment of chromatographic peaks based on similarities in mass-to-charge ratio (_m/z_) and retention time were performed in MassHunter Profinder. Features identified as silanes/siloxanes (products of SPME degradation) were removed. VOCs that did not appear in at least 50% of either Control or Cancer Weeks 1-3 samples were also excluded. To normalize the data, MS Total Useful Signal (MSTUS) was calculated and applied to remove unwanted non-biological intraclass variation [36]. Finally, MSTUS values were autoscaled (z-scored) to obtain a matrix with similar signal range. Univariate statistical analysis was implemented (two-tailed Student's _T_-test) on the Control group against tumor-bearing mice with urine collected on days 2, 5 and 6 (Week 1), urine collected on days 8, 9, 12 and 13 (Week 2) and finally, urine collected on days 16, 17 and 19 (Week 3). These analyses were implemented to identify VOCs differentially expressed (_p_-value \(<\) 0.05) between Cancer Weeks 1-3 and Control as well as between Cancer Week 1 and Cancer Week 3. _p_-values were adjusted utilizing the Benjamini-Hochberg procedure [37] to account for false discovery rates (FDR). Hierarchical heatmaps were generated for VOCs statistically significant by _p_-value \(<\) 0.05 for Control against Weeks 1-3 and Week 1 against Week 3 to visualize changes in VOC concentration induced by cancer injection and progression.  
Principal component analysis (PCA) was performed using all VOCs with _p_-value \(<\) 0.05 between the Control group and all Cancer groups. PCA was also implemented on a smaller group of VOCs with the lowest _p_-value (for Cancer/Control and Cancer Week 1/Week 3) to separate Control, Cancer Week 1 and Cancer Week 3. The matrix of VOCs was then subject to supervised linear discriminant analysis coupled with forward feature selection (iterative LDA, iLDA) [38] to build predictive classification models. PCA and iLDA were performed independently of each other in parallel. iLDA was used to develop VOC panels separating Control vs. Cancer, Cancer Week 1 vs. Cancer Week 3 and lastly, Cancer Week 1 vs. Cancer Week 3 vs. Control. Leave one out cross validation (LOOCV) and fivefold cross validation (partitioned 1000 times, median value utilized) were performed to determine if the models were overfit [39]. Receiver operator characteristic (ROC) curves for each model were built to visualize classification accuracies. If the area under the curve (AUC) of the ROC differed more than 0.10 between the training and cross validation data sets, the model was deemed overfit.
# Understanding Unsupervised Deep Learning for Text Line Segmentation
## 6 Understanding the Unsupervised Segmentation
### Sense of Network Depth  
This section aims to show that the proposed method is applicable using different CNN architectures. We explore the effect of the depth of networks on the qualitative results. Given that the baseline experiment uses AlexNet, we experiment with deeper networks by adding a single layer at each time, following the network architecture of VGG16. AlexNet contains five convolutional layers, three fully connected layers, and three pooling layers. VGG16 contains 13 convolutional layers, three fully connected layers, and five pooling layers. We experiment with shallower networks by removing a single layer at each time from the AlexNet architecture, until we are left with only three convolutional layers. We could not run a network with only two convolutional layers, as the number of parameters (more than \(500,000,000\)) exhausted the GPU memory (the experiments were conducted on a machine with an Nvidia RTX 3060 GPU). It should be noted that the model parameter size increases as the number of pooling layers decreases.  
Figure 15 shows the impact of network depth on the qualitative results. It shows the psuedo-RGB image of the same document alongside a visualization of the salient features of a patch from the document. As one would expect, deeper networks have wide receptive fields and the shallower networks have narrow receptive fields. However, they can all be used to segment the text lines. Therefore, our method can generalize by using shallower or deeper networks, as long as the network's receptive field is narrow enough to represent the text line pattern. Figure 16 visualizes the saliency of the ResNet50 network, and we can see that the features from adjacent text lines overlap, indicating that the network sees wider regions with more complex features. Given the input patch size \(350\times 350\), the output tensor size from the last convolutional layer is \(5\times 5\) which results in a loss of detail, but leads to complex features that are a combination of simple features. These complex features are not useful for clustering the central windows into text line regions and interline regions during the pseudo-RGB image generation phase, because they might correspond to any spatial location and do not form a pattern that is at the frequency of the text line pattern.
# Embedded AI-Based Digi-Healthcare
## 4 Design and Implementation
### Hardware Development
#### 4.1.3 Sensor Results & Prototype Design  
A recently published study investigates the accuracy of MAX30102 and MLX90614 and compares them to industry-standard devices. After rigorous testing and comparisons, the research concluded that these sensors offer almost 99% accuracy for SpO\({}_{2}\) measurements and 98% for heart-rate measurements. On the other hand, the non-contact-based IR thermometer has an accuracy of 99.62% in measuring body temperature. This high accuracy in measurements of these key vital parameters of the human body makes them suitable candidates for this architecture [15].  
Contrarily, the AD8232 sensor (ECG sensor by SparkFun) which was to be used to extract the ECG signal of the patient did not perform as expected. It was observed that there was a high rate of error in the values obtained by this sensor. The signal received from this sensor is highly erroneous, noisy, and unstable. After failed attempts of applying various filters to get the required ECG signal, the sensor's performance was deemed unsuitable for the application.  
For the design of the wireless sensor network, 3D models were developed for the enclosure of the biosensors to ensure ease of wearability. For the MAX30102, a 3D finger oximeter model was designed. For the MLX90614, ESP32, and displays, a wristwatch design was developed. The models were 3D printed and integrated as shown in Figure 5. It is important to mention that this developed device was powered through a Lithium-Ion 720 mAh battery, which had an approximated battery life of around 10 h of continuous use.
## 3 Results
### Dataset Properties  
Here, we provide some important characteristics of our data. As mentioned previously, in the context of ML we do not have a single dataset; instead, we slide windows and produce datasets for different time points.  
In Figure 3, we present the amount of churned customers at different times points in the data.  
As can be seen, the churn rate varies depending on the time window. As previously mentioned, we used the partial churn definition as the main label in our research. Note that the difference between full and partial churn is the number days of absence: 90 days for full churn and 40 for partial churn. We believe that 3 months is too long a period as processes are becoming more digitalized and faster; thus, we followed the observation in our previous study [24] of the hypothetical optimality of the partial churner definition and used it as the main way of labeling the data. However, from this definition it can be concluded that the full churner labels could form a subset of partial churn labels; if the client was absent for 90 days, they were necessarily absent for a 40-day period as well. The above is only true if the data are the same; in both cases, we filtered the data by dropping clients who were already churned according to the 40-day definition. As a consequence, if we used partial churn labels instead of full churn labels to train the model and predicted the full churn, it could be expected that this would increase recall and decrease precision, since it would increase both true and false positives. The hypothetical motivation for doing this could be to include more more recent data--however, in the context of our current research, the main reason for the use of partial churn labels instead of full churn labels is the fact that we used the partial churn definition as the main one. Thus, we will also provide results for the full churn definition for readers who still want to see how well the models can solving prediction problems with the full model definition.  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline bootstrap\_type & No \\ depth & 5 \\ grow\_policy & SymmetricTree \\ l2\_leaf\_reg & 0.357 \\ learning\_rate & 0.058 \\ random\_strength & 0.089 \\ rsm & 0.734 \\ eval\_metric & F1 \\ early\_stopping\_rounds & 50 \\ verbose & 100 \\ iterations & 121 \\ \hline \end{tabular}
\end{table}
Table 4: Final set of hyperparameters obtained using Hyperopt on the 90-day dataset.  
From the churn rate, it is evident that the data are imbalanced; thus, we applied the random oversampling method to balance them. Note that this balancing was independently applied only to the learning part of every cross fold, completely removing any data leakage between the training and validation data and preserving the efficient early stopping during the model training.
## 1 Introduction
Inspired by the ability of the encoder-decoder network employed for image/signal processing [36], a deep CNN-based model is established to realize the onset time picking and detection of microseismic waveforms. A Gaussian-like distribution is introduced to represent the probability of the onset time of micro-fracture waveforms. Given a noisy input signal, the proposed model can generate two individual outputs: one is defined as the Gaussian-like representation of the onset time picking and the other is the category of microseismic waveforms, which is further divided into two types: micro-fracture and noise (including blasting, mechanical, and noise) waveforms. Microseismic records from Micang Mountain and Grand Canyon tunnels are used for model training, validation, and testing. The performance of the proposed method is compared with existing methods on the onset time picking and detection using semisynthetic noisy waveforms, which are constructed by superimposing micro-fractures with real noise waveforms. The proposed method is also applied to test the microseismic records in other projects under different geological conditions and construction environments. In summary, the proposed method can provide a strategy that connects two neural networks in series and simultaneously achieves the onset time picking and detection of micro-fracture waveforms, which can save time and ensure the accuracy of microseismic signal processing. This study also provides a reference for the multi-task processing of seismic/microsicnic data.
# A Novel Deep Reinforcement Learning Approach for Task Offloading in MEC Systems
## 3 System Model  
We consider an MEC system with a set of MDs \(\mathcal{M}=\{1,2,\dots,M\}\) and a set of EDs \(\mathcal{N}=\{1,2,\dots,N\}\), as shown in Figure 1. Each MD is equipped with an antenna, and it communicates with the BS via wireless channels. Furthermore, the connection between the BS and the MEC server is through optic fibers or copper wires [25]. In the following, we will illustrate the system model and formulate the problem.
## 2 Data Acquisition and Pre-Processing  
The driving data acquisition platform is developed on the basis of a simulator, as shown in Figure 2. The vehicle state variables, driver's operation information, and physiological signals are collected. The impact of the cognitive load on driving safety and physiological characteristics for the cognitive load has been investigated previously [28]. This paper only focuses on the quantitative analysis of the driving style. Therefore, the impact of the cognitive load on driving style and driving behavior is not considered in detail.