esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Implementation and Evaluation
### Quality Testing with Different Levels of Noise
#### 5.4.2 Understanding the Impact of Large Windows Size  
For quality testing, we aim to establish the impact of the window size on the ability to detect quality drops. It is imperative that the proper window size be set so that higher levels of drops can be detected in quality before new data improve the quality. This is, however, dependent on the application goals of the IoT systems.  
We calculated the quality of \(\tau_{1}\) by corrupting its data by adding different levels of noise. We started corrupting the data with 1% noise and gradually increased it to 5%, 10%, 12%, 16%, and 20%. It is assumed that the window sizes are \(w_{12}=w_{13}=w_{23}\) for this test. Figure 8 shows the quality assessment of TDQAM over time under different levels of noise. We increased the window size from 150s to 3000s and calculated the quality in the dataset for \(\tau_{1}\). The actual smart broker will use a fixed window; this experiment shows the impact of the window size on the calculation of the quality. Each data series represents the quality calculations under different noise levels induced in the original data stream.  
In Figure 8, it can be seen that for 1-5% noise, the quality score consistently remains higher than 0.8, while for 16% and 20%, the quality is consistently below 0.3. This is because the window size does not have any impact on whether the data is consistently poor or good. For middle-level noises, the quality keeps fluctuating with increasing window size.  
As shown in Figure 9, when the data contains 10% noise, the smart broker requires approximately 220 s to identify a 30% decrease in quality. Detecting a 50% drop takes even longer, around the 1890s, under such low-noise conditions. However, with a 12% noise level, the smart broker can identify a 50% quality drop in just around 680 s. Moreover, it takes \(w=151\) s to detect a 30% drop and \(w=360\) s for a 40% drop, with the lowest \(w\) at 150 s. As the noise level increases, the time needed to detect these quality drops decreases. For instance, between noise levels of 16% and 20%, the smart broker can swiftly detect 70%, 60%, and 50% drops in quality, all within around \(w=151\) s.  
Figure 8: Quality assessment of \(\tau_{1}\) over time with different levels of noise in the data, with a \(\delta=15\) s and window size increasing from 150 s to 3000 s.  
The smart broker determines noise-induced drops in data quality, with detection times varying inversely with noise levels. As the noise level in the data increases, the smart broker becomes more efficient at detecting significant drops in quality, with detection times decreasing notably.
## 3 Data
### AWS Documentation Dataset
#### 3.1.2 Annotation Process  
Answers are provided by authors and reviewed by a pool of five experts. We divided the annotation tasks into four steps, where all four steps are completed by one annotator and verified by one expert. The guidelines given to annotators are stated below:  
1. Question validation: Annotators determine whether a question is valid or invalid. A valid question is a fact-seeking question that has a clear and unambiguous answer. An invalid question is incomprehensible, vague, opinion-seeking, or not plainly a query for factual information. Annotators must determine this by the question's content only.
2. Document selection: Annotators select the right source document from the AWS Documentation corpus that contains the answer. Every valid question in the dataset must have an accompanying source document.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Question** & **Text Answer** & **YNN Answer** \\ \hline What is the maximum number of rows in a dataset in Amazon Forecast? & 1 billion & None \\ Can I stop a DB instance that has a read replica? & You can’t stop a DB instance that has a read replica. & No \\ What is the size of a null attribute in DynamoDB? & length of attribute name + 1 byte & None \\ In Amazon RDS, what is the storage for all DB instances? & 100 TB & None \\ Can I run codePipeline in a VPC? & AWS CodePipeline now supports Amazon Virtual Private Cloud (Amazon VPC) endpoints powered by AWS PrivateLink & Yes \\ Is AWS IoT Greengrass HIPAA compliant? & Third-party auditors assess the security and compliance of AWS IoT Greengrass as part of multiple AWS compliance programs. These include SOC PCI FedRAMP HIPAA and others. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample questions from AWS Documentation dataset.  
3. Text answer: If a question is valid and has a valid source document, annotators select the correct answer from the source document. Every question must have a clear and unambiguous text answer. For example, for the question \"What is the maximum number of rows in a dataset in Amazon Forecast?\", there is one and only one answer which is \"1 billion\".
4. YNN answer: In the final step, annotators flag the answer's YNN answer as \"Yes\", \"No\", or \"None\", depending on the question and the source document. Every question must have a clear and unambiguous YNN answer. For example, for the question \"Can I run codePipeline in a VPC?\", there is one and only one answer, which is \"Yes\".
## 3 Methodology
* **Dropout** is a technique used in neural networks to prevent over-fitting. During training, the Dropout layer randomly selects some of the neurons and \"drops\" them, meaning they will be ignored during that particular forward and backward pass. This forces the remaining neurons to learn more robust features that are relevant to the classification task. The probability of dropping a neuron is a hyper-parameter and is usually set to a value between 0.2 and 0.5. * **2 nd Dense** has 3 neurons and uses the softmax (The output of the softmax activation function can be interpreted as the predicted probability of each class, and the predicted class is simply the one with the highest probability.) activation function. It takes the output of the previous layer and maps it to a probability distribution over the three classes--positive, negative, or neutral. This allows the model to make a prediction on which class the input sequence belongs to. In the second model CNN+BOSentiment (ID: CNNBOSentiment), first, we transformed the text data and labels into a format that can be input into a neural network for training and prediction. If we are using BOSentiment as a pre-predicted model, then negation cues were mapped as \([0,1,0,0,0]\), negative words as \([1,0,0,0,0]\), neutral words as \([0,0,0,0,0]\), AnA-words as \([0,0,0,1,0]\), and positive words as \([0,0,0,0,1]\). For this model, we used 6 layers:  
* **Conv2D** adds a 2D convolutional layer with 32 filters of size \(3\times 3\). The activation function used is ReLU;
* **MaxPooling2D((2,2))** adds a max pooling layer that reduces the spatial dimensions of the output of the previous layer. The \((2,2)\) parameter specifies the size of the pooling window. In this case, the layer pools the maximum value of a \(2\times 2\) window in the output. * adds a flattened layer that converts the output of the previous layer to a 1D array. This is necessary because the next layer in the model is a dense layer that requires a 1D input. The last three layers were the same as for the CNN model:
* **1 st Dense*
* (Dense (64, activation='relu')),
* (Dropout (0.4)),
* **2 nd Dense*
* (Dense (3, activation='softmax')). In this section, we describe the methodology and work we conducted to evaluate the performance of different classifiers for sentiment analysis on our data-set. We tested models with and without the use of the BOSentiment model and compared their performance using accuracy, precision, recall, and \(F_{1}\) score metrics. Furthermore, we examined the effect of different parameter values on the accuracy of our convolutional neural networks, and an overview is given in Table 1.  
Table 1 showcases models created based on varying numbers of convolutional layers, filters, their sizes, number of epochs, and batch sizes. The models are divided into two sections: CNN (Convolutional Neural Network) and CNNBOSentiment. In the CNNBOSentiment section, models combining CNN with the BOSentiment model are presented, while the CNN section represents pure CNN models with different utilized layers.
## 2 Materials and Methods
### Data Analysis  
We first conducted a paired \(t\)-test to assess overall differences between \(S_{\text{GPP}}\) and \(S_{\text{ER}}\), as well as across ecosystems. To explore differences within the same vegetation type, we used one-way _ANOVA_ followed by the least significant difference (\(LSD\)) test [41]. _ANOVA_ was performed initially to determine if there were significant differences among group means (\(p=0.05\)) The null hypothesis for _ANOVA_ posits that all group means are equal:  
\[H0;\mu 1=\mu 2=\mu 3=\cdots=\mu k \tag{3}\]  
If the _ANOVA_ results were significant, indicating that at least one group mean differs, we proceeded with the \(LSD\) test. The \(LSD\) value was calculated using the following formula:  
\[LSD=t_{a/2 df_{e}}\sqrt{2\frac{MSE}{n}} \tag{4}\]  
where \(t_{a/2 df_{e}}\) is the critical value from the \(t\)-distribution for a given significance level \(\alpha\) and degrees of freedom \(df_{e}\) from the error term of _ANOVA_; _MSE_ is the mean square error from the _ANOVA_; \(n\) is the number of observations per group. Given the data limitations, in this study, the significance level for the \(LSD\) test was set at \(p=0.05\). We then utilized linear regression to assess the relationship between \(S_{\text{GPP}}\) and \(S_{\text{ER}}\), both overall and within different vegetation functional types.  
Additionally, to investigate the range and differences in the degree of variation between \(S_{\text{GPP}}\) and \(S_{\text{ER}}\), we calculated and compared their coefficients of variation (\(CV\)).  
\[\text{CV}=\frac{SD}{MEAN} \tag{5}\]  
Linear regression was used to explore the relationships between \(S_{\text{GPP}}\), \(S_{\text{ER}}\), and their variations with AI and mean annual precipitation (MAP), as well as to examine the influence of environmental factors (mean annual temperature, MAT; MAP; and vapor pressure deficit,VPD) on \(S_{\text{GFP}}\) and \(S_{\text{ER}}\) across each ecosystem. For the temporal variation analysis, we first selected sites within each ecosystem that had data spanning more than 10 years. Then, for each site, we focused on the specific time period (2000-2014) and used the Mann-Kendall test and linear regression to detect trends in \(S_{\text{GFP}}\) and \(S_{\text{ER}}\)[42; 43].
### Virtual Telescope  
A virtual telescope (VT) composed of separate satellites, as opposed to a standard telescope, can not only change focal lengths but also demonstrate a longer focal length without manipulating the telescope's structure. Depending on the type of VT, lenses and detectors are used in one pair [15; 16] and the other is the (external) occulter and detector [17; 18]. The detector of the latter is sometimes referred to as an optical instrument or a telescope. The separated satellites are inertially aligned in a \"string-of-pearls\" configuration relative to an inertial frame to create a VT; this is known as inertial alignment hold (IAH). In general astrophysical research, it is possible to demonstrate a relative distance of approximately 1000-72,000 km [15; 17]. In solar science, X-ray images can be captured at 50-500 m [16; 18].
# A Multi-Strategy ALNS for the VRP with Flexible Time Windows and Delivery Locations
## 5 Results and Discussion
### Evaluation of Question Features
#### 5.3.2 The Effect of Delivery Options  
In each instance, MSALNS operates 10 times with or without consideration of optional delivery locations. For different cases, the case with delivery options is called the VRP-CS and the case with a single delivery location is called the VRPSD. As shown in Table 5, the first column represents the instances used; the second column represents the average total cost \(C_{total}\) found by the algorithm in the VRP-CS case, the average delivery cost \(C_{1}\) and the corresponding average customer satisfaction \(C_{2}\); and the third column represents the corresponding items in the VRPSD case. In addition, we further use \(Gap_{1}(\%)\) and \(Gap_{2}(\%)\) to represent the numerical difference between the corresponding parameters. The calculation method is similar to that of Formula (19). Note that for \(C_{total}\) and \(C_{1}\), a negative \(Gap(\%)\) indicates that the VRPDO has achieved a better solution, and for \(C_{2}\), a positive \(Gap(\%)\) indicates that the VRPDO has achieved a better solution. Through experimental data, the VRP-CS with delivery options offers greater flexibility in logistics and distribution. Remarkable results have been achieved in reducing distribution cost and improving customer satisfaction. The total cost of the VRP-CS is reduced by an average of 14.22%. In only one instance, the total cost of the VRP-CS is 0.81% higher than that of the VRPSD, its delivery cost is 3.46% higher, but its customer satisfaction is 16.23% higher. The VRP-CS has an average increase in customer satisfaction of 12.37%, indicating that the VRP-CS with delivery options effectively improves customer satisfaction. The VRP-CS delivery cost has been reduced by an average of 11.30%. In only three instances does the VRP-CS cost more to deliver than VRPSD, but the difference is within 3.5%. In Figure 9, standard deviation is used to represent the error. In terms of delivery cost, there is some volatility in the VRP-CS solutions due to delivery options and satisfaction. In terms of satisfaction, the VRP-CS solution is stable and can achieve better satisfaction. The VRP-CS improves customer satisfaction with only a slight increase in delivery cost (Table 4), demonstrating the effectiveness of the proposed satisfaction-oriented optimization approach and solving the limitations of the simple strategies discussed in Section 2.1. In addition, the use of delivery options improves customer satisfaction while reducing costs (Table 5), highlighting the advantages of flexible delivery strategies, as discussed in Section 2.2. The results show that by considering customer satisfaction, the VRP-CS sacrifices only about 5% of delivery cost while achieving approximately a 15% improvement in customer satisfaction. Moreover, by adopting a delivery model with flexible options, the total cost is reduced by 14.22%; delivery cost is reduced by 11.3%; and customer satisfaction increases by 12.37%. Figure 9: Results of different sub-objectives influenced by delivery options.
# Performance Analysis of MIMO System with Single RF Link Based on Switched Parasitic Antenna
## 4 Results
### Influence of the Switch  
The influence of the SPDT on the system error rate mainly reflects in two variables, including loss and isolation. Considering the actual rate of input symbols and the switching rate of varactor diodes loaded on the switched parasitic antennas, the input symbol speed should be set to 100 KHz in simulation. The switching time is in the _ns_ level and the system error rate range is small, which is particularly significant when the BER is less than 8 dB.  
Figure 8a describes the impact of the SPDT switch loss on the bit error rate of the system. It is not difficult to find that the system bit error rate increases with switching loss. It will be more obvious when the signal-to-noise ratio is higher than 2 dB. Figure 8b shows that with the decrease of the SPDT switches isolation, the system bit error rate will also increase. Generally, when the system error rate reaches \(10^{-2}\) or lower, it is usually possible to determine the communication system ability to accurately transmit the information.  
Figure 8: Influence of SPDT characteristics on system error rate. (**a**) Influence of the switch loss; (**b**) Influence of the switch isolation.  
Before investigating other characters of the MIMO system, the switching time of SPDT switch is set to 1 ns according to the above simulation results. The loss and the isolation degree are set to 1 dB and 10 dB, respectively. Based on this, the transmitter and the channel should be separately simulated to get the channel matrix. The real part and the imaginary part are imported into the two tkt. files, then use the Channel_Capacity element to determine the system channel capacity at the stable state, as is shown in Figure 9. The solid line represents the channel capacity of the 2 \(\times\) 2 MIMO system and the dotted line compares the channel capacity of the single input single output (SISO) systems. It is not difficult to find that the capacity of the 2 \(\times\) 2 MIMO system is nearly twice that of the SISO system.
## 3 Description of Comprehensive Techno-Economic (CTE) Model  
### Disadvantages and Shortcomings of Existing Models for the Analysis of Telecom Operators  
All previously mentioned models work with a large dose of subjectivism, and yet no model offers a complete picture and overview of the state and potential of telecom operators. This research, which was conducted over the last few years, has defined the need for creating a unique and comprehensive model that could minimize and even eliminate subjectivity in the results.
# Detection of Defective Features in Cerasus Humilis Fruit Based on Hyperspectral Imaging Technology
## 3 Results and Discussion
### Effective Wavelength Selection  
#### 3.3.1 Regression Coefficient Method  
From the RC curve shown in Figure 4, 12 characteristic wavelengths were selected: 950, 998, 1029, 1065, 1122, 1176, 1266, 1342, 1377, 1450, 1533, and 1593 nm.
# Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage
## 2 Scheduling Framework and Pickup Vehicle Data
### Scheduling Framework Based on the IIoT  
A cloud platform represents a valuable tool for widely sharing manufacturing services and solutions by connecting suppliers and customers in large-scale manufacturing networks [17; 18]. Figure 1 shows the framework of scheduling based on the IIoT in a steel company. The IIoT collects data from vehicles and uploads them to the platform. Based on these data, the cloud platform allocates resources for each pickup vehicle operation. It is worth noting that each yard stores mixed steels ans thus contains a variety of different types of steel, which allows pickup vehicles to obtain target goods from different yards. The goal of scheduling is to allocate the most appropriate yard for each vehicle and determine the optimal operation sequence for individual vehicles, both of which are considered in this paper. The results of scheduling generate the decision for the vehicle operation sequence and the yard allocation. Therefore, scheduling is important to increase the efficiency of vehicles and the throughput of the park.
## 3 Results
### Characteristics of Entrolled Patients
& 8 (20\%) & 8 (24\%) & 0 (0\%) & & 0 (0\%) & 3 (30\%) & 5 (23\%) & \\ \cline{2-10}  & Vascular com. & 20 (49\%) & 17 (52\%) & 3 (38\%) & & 5 (56\%) & 4 (40\%) & 11 (50\%) & \\ \hline \multirow{3}{*}{MSI} & Instable & 1 (11\%) & 1 (14\%) & 0 (0\%) & \multirow{3}{*}{1} & 0 (0\%) & 0 (0\%) & 0 (0\%) & 1 (33\%) & \multirow{3}{*}{0.278} \\ \cline{2-10}  & MSI & 1 (11\%) & 1 (14\%) & 0 (0\%) & & 0 (0\%) & 0 (0\%) & 1 (33\%) & \\ \cline{2-10}  & Stable & 7 (78\%) & 5 (71\%) & 2 (100\%) & & 2 (100\%) & 4 (100\%) & 1 (33\%) & \\ \cline{2-10}  & NA & 32 & 26 & 6 & & 7 & 6 & 19 & \\ \hline \multirow{3}{*}{CDX2} & Positive & 1 (100\%) & 0 (0\%) & 1 (100\%) & \multirow{3}{*}{-} & 1 (100\%) & 0 (0\%) & 0 (0\%) & \multirow{3}{*}{-} \\ \cline{2-10}  & NA & 40 & 33 & 7 & 8 & 10 & 22 & \\ \hline \multirow{3}{*}{KRAS} & Mutated & 5 pT1 (5 N0 e 1 N+), eight patients had a pT2 (6 N0 and 2 N+), 23 patients had a pT3 (18 N+), and four patients had a pT4 (3 N0 and 1 N+). Regarding the 13 patients with stage II, two were at low risk and 11 at high risk for the presence of lymphovascular invasion, T4 or G3-4. The majority of tumors had intermediate (G2; 48%: N = 19) or high (G3-G4; 47% N = 19) histologic grading, while G1 accounted for 5% of tumors in this population (N = 2). A total of 13 patients had left CRC and 28 right CRC (Table 1). Half of the patients (46%; \(n=19\)) received adjuvant chemotherapy, in accordance with clinical stage of disease. Nine patients received fluoropyrimidine monotherapy and 10 patients received polychemotherapy with oxaliplatin and fluoropyrimidine. Six out of eleven patients at stage II at high risk received adjuvant treatment; the rest of them did not receive chemotherapy for age or comorbidity  
Thirteen out of the 17 patients with stage III disease received adjuvant treatment according to tumor stage. At the last follow-up, 19% (\(n=8\)) of patients had disease relapse (Table 1). As expected, the patients with relapse had a history of stage III disease or stage II at high risk.
###### Abstract  
Satellite communication links suffer from arbitrary weather phenomena such as clouds, rain, snow, fog, and dust. Furthermore, when signals approach the ground station, they have to overcome buildings blocking the direct access to the ground station. This work proposes a model to predict the remaining signal strength for the next timeframe after deducting the attenuation and disruption impact caused during its propagation from the satellite to the ground station. The proposed model can be adjusted to comply with any geographic region and a broad spectrum of frequencies. We employ LTSM, an artificial recurrent neural network technology, providing a time-dependent prediction. We can instantly calibrate the satellite outgoing signal strength to overcome the predicted attenuation, resulting in satellite energy saving using this prediction.  
satellite communication; signal propagation; rain attenuation; urban area ground station; SNR; ITU-R; LSTM; neural network +
Footnote †: journal: _electronics_  
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Footnote 0: Copyright** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Cloud-Based Single-Frequency Snapshot RTK Positioning
## 3 Experiment Setup and Results
### Results and Discussions
#### 3.3.1 Baseline Distance Impact  
By analyzing Figures 8 and 9, it can be clearly observed that generally both the mean LRF value and SRTK fix rate drop when the baseline distance increases. When the baseline distance is less than 10 km, this trend does not appear to be very obvious as the SRTK fix rate curve is almost flat and the mean AR ratio variation is also small. When we extend the baseline further, the SRTK fix rate drops more rapidly, which could be caused by the fact that the simple DD measurement model adopted in this research is meant for short baseline scenarios. With 100 ms of integration time, and a baseline shorter than 15 km, more than 90% of snapshots generated valid RTK fixed solutions, regardless of the signal bandwidths been tested. When the baseline was extended to 50 km, the fix rate dropped to approximately 30%, with an average LRF value below 2.  
Figure 8: SRTK fix rate for different bandwidths when 100 ms integration time is used.  
Figure 9: Mean LRF value for different bandwidths when 100 ms integration time is used.