esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Spectral Amplitude Coding OCDMA System Utilizing a Single Photodiode Detection
## 4 Results and Discussion
### Absence of EDFA and DCF  
For the sake of comparison, we used three users, but the results can be generalized for any number of users. The comparison of the average BER for three users of the SAC-OCDMA system using EDW codes as signature codes is shown in Figure 3. The different detectors (PIN and APD photodetectors) were investigated at different transmission distances and bit rates of 622 Mbps, 1 Gbps, and 2 Gbps. A better performance was observed when the APD was used. At 622 Mbps, when using the APD,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Quantity** & **Value** \\ \hline RB & Data bit rate & 622 Mbps, 1 Gbps and 2 Gbps \\ Bo & Optical bandwidth & 3.75 MHz \\ Be & Electrical bandwidth & 0.75 \(\times\) RB Hz \\ S & Received power at photodiode & \(-\)10 dBm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Typical parameters used in numerical analysis.  
\begin{table}
\begin{tabular}{c c} \hline \hline Wavelength & 1550 nm \\ Single Mode Fiber (SMF) & Dispersion, D = 17 ps/nm/km \\ Dispersion slope, S = 0.075 \\ ps/nm\({}^{2}\)/km \\ Dispersion Compensating Fiber (DCF) & Dispersion, D =\(-\)85 ps/nm/km \\ Dispersion Slope, S = \(-\)0.0375 \\ ps/nm\({}^{2}\)/km \\ \hline \hline
**Erbium-Doped Fiber Amplifier (EDFA) Parameters** \\ \hline Pumping technique & Forward \\ Pumping power & 50 mW \\ Pumping wavelength & 980 nm \\ \hline \hline
**PIN Parameters** \\ \hline Dark current & 5 nA \\ Thermal noise coefficient & \(1.8\times 10^{-23}\) \\ \hline \hline
**Avalanche Photodiode (APD)Parameters** \\ \hline Gain & 10 dB \\ Dark current & 5 nA \\ Thermal noise coefficient & \(1.8\times 10^{-23}\) W/Hz \\ \hline \hline
**Low Pass Filter** \\ \hline Receiver filter bandwidth & 0.75\(\times\) Bit Rate \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters used in simulation.  
the data of the SAC-OCDMA system could be transmitted over a distance 40 km longer than that when using the PIN detector with a BER less than 10\({}^{-9}\), as shown in Figure 3a. As shown in Figure 3b, at 1 Gbps and a transmission distance of 48 km, the average BER of three users is 1.02 \(\times\) 10\({}^{-5}\) and 4.73 \(\times\) 10\({}^{-10}\) for PIN and APD, respectively. While at 2 Gbps and transmission distance of 36 km, the average BER of three users is4.76 \(\times\) 10\({}^{-5}\) and 1.82 \(\times\) 10\({}^{-5}\) for PIN and APD, respectively, as shown in Figure 3c. Therefore, the system using the APD can transmit data for longer distances than that using the PIN detector.  
Figure 3: SAC-OCDMA system performance in terms of bit error rate (BER) using EDW codes and two different detectors in the absence of erbium-doped fiber amplifier (EDFA) and dispersion compensating fiber (DCF) at bit rates (**a**) 622 Mbps, (**b**) 1 Gbps, and (**c**) 2 Gbps (PIN detector is indicated by the blue line and avalanche photodiode (APD) by the red line).
## 5 Results and Discussion
### Discussion
#### 5.2.2 Agricultural QA Scenario Test  
In order to solve the problem of low accuracy of the question-answering system in the agricultural scenario, we applied the proposed information aggregation model to machine reading comprehension datasets for testing and received the following results in Table 7.  
From Table 7, we can see that our model's \(EM\) and \(F1\) metrics outperform the original BERT model. Such model performance indicates that the proposed method can achieve automatic problems in agricultural scenarios. Figure 10 shows the application after deploying our model to the wechat platform.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **Micro-F1** & **Macro-F1** \\ \hline QANet & 77.2 & 60.4 \\ Match-LSTM & 78.7 & 60.6 \\ BiDAF+self-attention+ELMO & 82.7 & 63.6 \\ Performance Mode (ours) & 84.3 & 64.0 \\ Without \(l_{match}\) (ours) & 82.9 & 63.5 \\ Without \(l_{numatch}\) (ours) & 81.6 & 63.1 \\ Without loss functions (ours) & 81.0 & 62.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation experiments based on contrastive learning.  
Figure 10: Screenshot of automatic question-and-answering in agricultural scenarios based on the wechat platform.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Training Dataset** & **Models** & \multicolumn{2}{c}{**All Dataset**} & \multicolumn{2}{c}{**Subset**} \\  & & **EM** & **F1** & **EM** & **F1** \\ \hline \multirow{2}{*}{SQuAD 1.1} & BERT & 57.44 & 66.31 & 67.43 & 74.18 \\  & Ours & 59.03 & 67.26 & 68.31 & 74.82 \\ \multirow{2}{*}{SQuAD 2.0} & BERT & 55.21 & 62.78 & 65.33 & 73.08 \\  & Ours & 60.37 & 66.03 & 67.90 & 74.05 \\ \multirow{2}{*}{SQuAD 1.1 + 2.0} & BERT & 59.33 & 62.81 & 66.29 & 73.98 \\  & Ours & 62.18 & 63.08 & 70.29 & 77.01 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Testing results by applying the proposed model to agricultural datasets
# Computer Vision Algorithms on a Raspberry Pi 4 for Automated Depalletizing
## 3 Methodology
### Haar Cascade Classifier  
Haar cascade is a machine learning-based object detection method widely used in image processing. It is a classifier trained on positive and negative samples of a target object. Classification is based on features provided by rectangular filters.  
Once trained, a Haar cascade classifier is applied to an image through a sliding window approach. Features are efficiently compared at different scales and positions. If a region of the image matches the learned pattern for the object, the classifier identifies it as a positive detection. The method is computationally efficient, but may have limitations in handling complex backgrounds or objects with varying orientations, as the training data need to encompass diverse instances of the target object for optimal performance.  
The Python code used trains a Haar cascade classifier with OpenCV, creates a positive picture and various negative examples, and generates positive examples [35]. The primary steps are listed as follows:  
1. **Prepare Negative Images**: A folder for negative images was created by copying images from a source directory. A background file (bg.txt) listed the negative images.
2. **Resize and Edit Images**: Positive and negative images were resized to consistent dimensions (the size \(93\times 142\) pixels was used for positive images).
3. **Create Positive Samples**: The _opencv_createsamples_ tool generated positive samples and related information for each positive image in a separate directory for each image's data. The default configuration includes parameters like angles, number of samples, width, and height.
4. **Merge Vector Files**: Positive sample vector files were merged into a single file for training input.
5. **Train Cascade Classifier**: The _opencv_
# Delay Management in Regional Railway Transport
## 2 Materials and Methods
### Background: State-of-the-Art
He examined the dynamic impact zone that was created and used in the retiming/reordering heuristic technique, as part of a conflict prevention strategy (CPS) aimed at assisting dispatchers in considering the relevant parts of the network and the traffic when addressing conflicts. [PERSON] [13] used three models to obtain reliable disruption-length estimation, which can potentially reduce negative impacts caused by disruptions: a disruption-length model, a short-turning model, and a passenger assignment model. The results showed the effects of short (optimistic) and long (pessimistic) estimates on affected passengers, the generalized travel time, and the number of passengers rerouting and transferring. From this background, it is evident that much of the research has dealt with delay management, operational traffic control, dispatching, etc., because these topics are important for transport services. The higher the quality of such services, the more reliable they are, leading to enhanced attractiveness and sustainability. Delay management also includes the determination of transfer waiting times in the timetable planning process. Determining transfer waiting times properly can significantly influence the stability and attractiveness of a timetable. However, no previous research has used CPM for determining transfer waiting times. [PERSON] alone [11] used CPM in considering delay management, but for the purpose of proper time margin position planning rather than for determining transfer waiting times. In this article, the authors used CPM for dealing with this task.
## 6 Experimental Results
### Comparison Between Joint-Approach and LDA  
An analyst can be interested in analyzing the difference between the two types of partitions obtained using the two strategies. To this end, ESCAPE compares the best solutions found by the two different methodologies by computing the ARI index, which gives us a quick comparison of the obtained partitions.  
Figure 10: D1 t-SNE representation, TF-IDF weighting scheme, \(K=3\), \(6\), \(10\), and \(19\), respectively.  
Figure 9: Word cloud representation of a subset of topics; dataset D1, TF-IDF weighting scheme, \(K=10\).  
The ARI index between the best partitions of the two methodologies is reported in Table 16. We can observe that the results are quite different, and analyzing only the previous table is not sufficient to draw conclusions on the two methodologies. Since the Boolean-IDF and Boolean-Entropy are very similar in terms of partitions for the joint-approach, we only consider the weight Boolean-Entropy for the comparison with respect to the Boolean-TF weight.  
We recall also that the ARI index penalizes the partitions with different numbers of clusters more than the Rand index; however, especially for the weighting LogTF-Entropy, the comparison value is really poor.  
To analyze the obtained partitions in major detail, ESCAPE includes several graphical representations that are self-explained. These proposed graphical representations are exploited to simplify and synthesize the extracted knowledge patterns in a compact, human-readable, detailed, and exhaustive representation.  
For each experiment, ESCAPE reports the proposed visualization techniques, allowing different stakeholders to easily capture a high-level overview of topic-detection in each corpus.  
We recall that the two highest similarity weighting schemes are the TF-IDF and the Boolean for both the topic modeling approaches. The partitions are not the same because the ARI index tends to 0.554 and 0.790, respectively. Still, analyzing only the values is not sufficient to quantify the similarity between the topics. Below, we have reported the analysis of these two weighting strategies to highlight the main differences between the two approaches.
## 2 Selection of Vertiports  
### UAM Demand Data Collection  
If UAM becomes commercialized, the commuting market is expected to be the most promising. To determine the demand for UAM for commuting, data were collected based on the current commuting population census conducted by Statistics Korea in 2015 [15]. The current residence and commuting administrative districts in the metropolitan area consist of 79 cities, counties, and districts. From the data, the number of the commuting population aged 12 or older in the metropolitan area is 14,335,975. However, there is an assumption that commuters would not use UAM if their commuting area is the same administrative district as their residence, since it would not take much time in these cases. Based on this assumption, the population of commuters analyzed was reduced to 7,959,469. The administrative district with the largest population of commuters is Gangnam-gu, Seoul, with a total of 609,119 people commuting from other administrative districts.  
To visualize the data obtained earlier, we used MATLAB to display it on the map, as shown in Figure 1[16]. The K-means algorithm used in this paper requires the appropriate number of data for clustering so that the data allocated to each administrative area are rounded up and plotted on the map with one point per 5000 persons. The clustering process was repeated many times to obtain optimal locations of the vertiports. The shape of each administrative district was irregular, so it was assumed to be a square of the same size as the area of each district, as shown in Figure 2. Table 2 shows the number of commuters and data in the major administrative districts.  
Figure 1: Commuter data expressed in MATLAB for each district.
## 5 Test Results
### Filtration Behavior of Silt  
Twenty filtration tests were performed using silt (internally unstable soil) in combination with four different geotextiles (GT1-GT4) to assess their filtration behavior under normal stresses, and the variation of fine particles retained inside the geotextile fibers was measured after the tests as shown in Table 5. Table 8 summarizes the results of filtration tests performed on combinations of each geotextile and silt under five different magnitudes of normal stress. Figure 6 shows the trends in the \(GR\) and \(k_{\mathrm{sg}}\) values of the silt with geotextile samples GT1, GT2, GT3 and GT4 over time under various normal stresses. As can be seen that the variation trend of \(GR\) and \(k_{\mathrm{sg}}\) values with normal stress was similar to that of the silty clay-geotextile system. The \(GR\) value ranged from 1.0 to 2.5 for all filtration tests in silt-geotextile combinations. A larger \(GR\) value was noticed in test cases using geotextile with larger thicknesses. This was due to the fact that thickness affected tortuosity, which reflected the path or distance travelled by a fine particle in the pore channel across the fabric. Thus, over filtration time, fine particles were more likely to become entrapped in the thick geotextiles. Additionally, the \(GR\) value of the M-GT4 combination was greater than that M-GT1 combination. This probably explained why GT4 retained more fines at the end of the filtration test than GT1 (Table 5). The permeability coefficient values for the silt with all geotextile samples decreased by one order of magnitude at a normal stress of 25 kPa. This could be attributed to the internal instability of silt, which induced the obvious migration of fine particles under the action of the flow drag force. The filtration channel in the geotextile filter could thus capture those moving fine particles, resulting in the erosion of fines near the geotextile interface and clogging of the geotextile, accompanied by a significant decrease in the permeability of the silt-geotextile combinations. Moreover, it was discovered that the \(k_{\mathrm{sg}}\) values were greater than \(k_{\mathrm{sg}}\) for all silt-geotextile combinations (Table 8), resulting in a \(GR\) greater than 1.0, even though the GR values are still less than the clogging limit of 3 set by the U.S. Army Corps of Engineers [20], indicating that excessive clogging did not occur.
# Low-Order Spherical Harmonic HRTF Restoration Using a Neural Network Approach
## 3 Machine Learning HRTF Restoration
### Baseline Model
This demonstrates that the extra variety of measurements helps the model generalise better across different measurement methods. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multicolumn{6}{c}{**Comparison of Results with and without Data from ARI, ITA and RIEC (Lower is Better).**} \\ \hline
**Model** & **Overall Mean** & **Training** & **Validation** & **SADIE 20** & **Bernschutz** & **Test Mean** \\ \hline Baseline (without extra data) & 45.28 & 18.29 & 20.55 & 51.33 & 90.93 & 71.13 \\ \hline Baseline (with extra data) & 44.46 & 28.17 & 30.17 & 52.34 & 67.17 & 59.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mean squared error (MSE) with and without data from ARI, ITA and RIEC demonstrating that using additional data improves the result with the Bernschutz test data significantly. Figure 8: Right MSE during training (blue: training loss, orange: validation loss, green: hold out test data Subject 20 from SADIE II, red: Bernschutz KU100), demonstrating that the model over-fit the training data. Figure 7: Left MSE during training (blue: training loss, orange: validation loss, green: hold out test data Subject 20 from SADIE II, red: Bernschutz KU100), which shows that the model over-fit the training data. The most ideal way reduce over-fitting is to train with more data. However, given the limited HRTF measurements available, different regularisation techniques can be utilised to improve the baseline result, which will be discussed in this section. Figures 7 and 8 show there were some differences between the SADIE hold out test data and Bernschutz KU100 data, although the difference was less on the left channel. However, the average MSE test error of the two channels was approximately the same (left: 59.874, right: 59.633). Further investigations were required to establish the cause of the differences in the left and right channels. On the other hand, according to Figures 7 and 8, it is interesting to see that the results with the Bernschutz KU100 data also suffered from over-fitting. As there were different KU100 HRTFs in the training data, and the KU100 measurements in the SADIE II database were very similar to the Bernschutz KU100 measurements, it was unexpected to see the model perform quite poorly when comparing the training and validation results. More oddly, in the later sessions, it showed that the Bernschutz KU100 measurements did not seem to benefit from any regularisation methods. One plausible hypothesis is that the current model only trained with 6 HRTF databases which represents 6 different measurement setups. The model requires a larger variety of inputs to be able to generalise across different measurement setups and methods.
## 6 Carboxymethyl Cellulose (CMC)
### CMC-Based Hydrogels  
Hydrogels based on CMC have been more extensively studied than nanofibrous mats because CMC is a generally good candidate for hydrogel formation due to its coordination ability and biodegradability and hydrogel formation in the presence of metallic ions [101; 103; 109]. [PERSON] et al. were pioneers of using CMC hydrogels as wound-dressings, and they prepared silver nanoparticle-containing CMC hydrogels by in situ silver nitrate reduction [103]. Their hydrogel dressing was found to be highly absorbent, biocompatible, and proliferation-inducing for skin cells. The 50 ppm composition of silver nanoparticles in the hydrogel was highly effective against Gram-negative and Gram-positive bacteria strains, indicating an ideal dressing for treatment of infected wounds [103]. More recent studies have noticeably shifted towards hydrogels with CMC and at least another polymer component such as collagen and poly(ethylene glycol) [81; 102]. Through this strategy, the wound-dressing can take advantage of all the materials' promising properties while potentially alleviating the less desirable properties [110; 111; 112].  
[PERSON] et al. reported one of the most important studies with this approach [81]. They used a mixture of collagen and CMC to take advantage of collagen's antioxidant potential and its improvement capability for biocompatibility and hydrophilicity. They investigated the effects of different concentrations, molecular weights, and substitution degrees on modified CMC's radical scavenging properties. They found that CMC's scavenging effects improved upon increasing concentrations and degrees of substitution, showing a promising material that can reduce reactive oxygen species (ROS), a vital capability for wound-dressing applications [81].
## 2 Related Works
[16] proposed a framework for mobile offloading scheduling based on online machine learning. The framework provided an online training mechanism for the machine learning-based runtime scheduler, which supported a flexible policy. Through the observation of previous offloading decisions and their correctness, it can adapt scheduling decisions dynamically. [PERSON] et al. [17] focused on reducing overall resource consumption for computing offloading in mobile edge networks. They obtained features composed of a \"hyperprofile\" and position nodes by predicting costs of offloading a particular task. Then a hyperprofile-based solution was formalized and a machine learning techniques based to predict metrics for computation offloading was explored in this paper. However, existing researches have some limitations. Papers [6; 7; 8; 10] only consider the energy and delay as optimization objects that ignore the management of cloud resources. Moreover, some works [11; 12; 14] focus on resource management for task offloading, which do not consider utilization rate of cloud resources. These works rarely consider utilization rate of PM in cloud server and delay caused by arranging the offloaded task to different PMs in the cloud simultaneously. They do not consider the detail of the remote cloud, which has an impact on offloaded tasks. The bandwidth between the different PMs of the cloud and mobile devices are different because PMs of the cloud are distributed geographically in a real environment. This can affect the transmission time that a task is offloaded from mobile device to the PM. Moreover, a waiting time in the cloud for the offloaded task will be taken into consideration. Besides, the resources of the cloud are also limited. If the number of offloaded tasks is too large due to the popularity of mobile devices, the utilization rate of cloud resources should be considered to avoid waste of cloud resources. Our paper intends to study scheduling the offloaded tasks to optimal PM by trading off between utilization rate of physical machine and delay, in which the delay comprises of the waiting time, the execution time of offloaded task and data transmission time. We model the problem using DRL to obtain an optimal policy for task offloading, which is more effective than traditional reinforcement learning when facing to high-dimension state space and action space. Different from existing studies [15; 16; 17] that applied machine learning technologies for offloading based on related historical data, we intend to study an online learning method based on DRL where a weighted reward is introduced for tradeoff between utilization rate of PM and delay. Furthermore, we propose a two-layered reinforcement learning (TLRL) algorithm for task offloading to improve learning speed, where the dimensions of state space and action space are reduced by utilizing the k-NN [18] to classify the PMs in the remote cloud.