esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Monitoring Results
#### 3.2.3 Summary of CO\({}_{2}\) Assessment for DNVP and SNVP  
Figure 14 shows the time during monitoring when CO\({}_{2}\) concentration is below the recommended limits according to the most relevant IAQ standards.  
Figure 14 plots the results obtained in the three monitored days and groups them into color bars (gray, brown, and blue) to present a summary of time when the CO\({}_{2}\) is below the required limits: the 18 th and the 26 th of November, and the 17 th of December.  
When the WW\({}_{\rm eq}\)R is 2.16%, the 17 th of December (blue bar), the CO\({}_{2}\) concentration is adequate during 100% of the time according to the EN 16798-1 and ASHRAE 62.1, and during 94.83% of the time according to the Health Protocol, but only during 15.52% of the time according to the RITE. However, the RITE is mandatory for new buildings and/or thermal facilities' major renovations, but not for existing buildings.  
When the WW\({}_{\rm eq}\)R is between 0.23% and 1.82%, the 18 th and the 26 th of November (gray and brown bars), then the time within an adequate CO\({}_{2}\) concentration is reduced. During the time of data monitored on the 18 th of November, with a WW\({}_{\rm eq}\)R between 0.23 and 1.07 (gray bar), the IAQ was adequate for the standards between 12.07% and 65.52% of the time. During the time of the data monitored on the 26 th of November, with a WW\({}_{\rm eq}\)R between 1.07 and 1.82 (brown bar), the IAQ was adequate for the standards only between 12.07% and 74.14% of the time.  
According to the IAQ monitoring realized, if RITE is not considered in the analysis, the CO\({}_{2}\) concentration was appropriate during all the time when the WW\({}_{\rm eq}\)R was at least 2.16%, on the 17 th of December. If the WW\({}_{\rm eq}\)R used is between 0.23% and 1.82%, then the IAQ can be considered appropriate between 48.28% and 74.14% of the time.
# Simulation of the Infiltration of Fractured Rock in the Unsaturated Zone
## 2 Mathematical Model
### Split-Boundary and Fracture Network Representation  
The linear FEFVM collocated mesh method employs a contiguous finite-element mesh to accurately discretise different rock-matrix domains, allowing material properties to vary from element to element in a piecewise constant fashion. However, node-wise linear variables are continuous across element boundaries. As a result, this basic FEFVM scheme cannot capture discontinuities of saturation and pressure at material interfaces. To remove this shortcoming, a mesh splitting operator was applied to the input finite element mesh by [PERSON] et al. [51]. It detects nodes located at material interfaces and multiplicates them. As many collocated nodes created as materials are joined at this mesh coordinate. These interface nodes are reconnected to the corresponding mesh patches (Figure 1). In the new development presented here, the mesh splitting operator now also recognises fractures as stand-alone mesh regions. These originally lower-dimensional mesh domains are now retained as isolated regions within split boundaries, supporting unique properties of such discontinuities in the model. To connect the fractures with the rock matrix, the fracture nodes are added to the node manifolds that identify the multiplicated nodes of the split boundaries and the dissected node-centered finite volumes that they belong to. In this way, split mesh patches representing the fractures can now communicate with each other and with the surrounding rock matrix via the finite-volume discretisation that is collocated with the finite-element mesh.
# Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland
## 3 Methodology
### Train-Test Split  
To evaluate the model's generalizability and robustness, multiple train-test split ratios (80-20, 75-25, and 85-15) were tested. These splits ensured that the model's performance could be assessed under varying conditions, balancing the training and testing datasets to reflect real-world scenarios. The 80-20 split consistently yielded the most balanced results, achieving R-squared values exceeding 0.90 across most models. The split provided sufficient data for training while maintaining a robust test set for evaluation. The 75-25 split favored models like neural networks and XGBoost, which demonstrated improved generalization with a slightly larger test set--the increased test size allowed for a more rigorous assessment of these models' predictive capabilities. Meanwhile, the 85-15 split achieved high R-squared values for certain models (e.g., random forest and gradient boosting), but the reduced test set led to slightly diminished generalization, highlighting the trade-off between training and testing data size. Overall, the results indicate that the 80-20 split offers the best balance for most models, while the 75-25 split may be preferred when prioritizing generalization for complex models like neural networks.  
All train-test splits were drawn from the available historical dataset (2012-2023), with no separate future time period held out for forecasting. The model's performance is, therefore, evaluated on held-out contemporaneous data from this timeframe rather than on any data beyond 2023. In practical terms, the current phase of the study makes predictions only within the period of the observed data; it does not extend to forecasting future crime rates. This approach ensures that evaluation is based on known outcomes. While the methodology is forward-compatible (i.e., it could be applied to predict future crime rates, given appropriate historical training data), implementing such forecasts is beyond the scope of the present study.
# Automatic Configurable Hardware Code Generation for Software-Defined Radios+
## 1 Introduction
We aim to bridge the semantic gap between the high-level model using a dataflow model and the low-level model of hardware which is largely described using finite-state machines (FSMs). Our approach targets the problem domain of SDR in which the resultant solutions run on the FPGA platform, more specifically, our contributions are as follows:  
* Present the operational analysis of the SDF-AP model and provide additional semantics that facilitate the description of SDF-AP model analyses. * Define the computation methods for buffer size allocation and the latency under 1-periodic scheduling [11] and throughput constraints. * Reduce the behavioural gap between the SDF-AP model and the hardware model. * Define four optimization techniques for low-level hardware design synthesis. * Ensure that the generated hardware system faithfully conforms to the high-level application descriptions using SDF-AP model. * Evaluate our hardware implementation approach using eight different SDR applications. This paper proceeds as follows: in Section 2, we start by reviewing the dataflow used in this work along with the model syntax that will be used in most parts of the paper. Next, we present the analysis of the dataflow model together with scheduling and buffer sizing methods in Section 3. We then redefine the SDF-AP model in a form of a transition system that makes SDF-AP model easy to analyse in Section 4, followed by a low-level hardware implementation in Section 5 and the conformance analysis in Section 7. Experimental results, using a case study approach in which eight representative SDR applications are developed, are presented in Section 8. Section 9 proceeds by highlighting related work we have investigated that was done by other researchers and which we use to compare our approaches to. Finally, in Section 10, we provide our conclusions and future plans.
## 2 Materials and Methods
### Data Processing
#### 2.4.2 Data Processing for Model Coordinate Extraction  
DJI Terra 3.4.4 software was used to process the generated DOM image data and DSM data, which were analyzed in ArcGIS 10.2 with 10 ground checkpoints for coordinate data extraction. The coordinates were extracted by adding the point file, ensuring that the coordinate system and DOM image coordinates were consistent. Spatial analysis was then performed on the 10 ground checkpoints to extract points and obtain elevation values. Subsequently, data management tools were employed to add X-coordinate and Y-coordinate values, ultimately outputting the coordinates of the 10 ground checkpoints. The checkpoint coordinates were successfully extracted, as discussed in detail below.
# Motion Planning in UAV-Aided Data Collection with Dynamic Jamming
### Contributions and Organization  
The goal of this study is the examination of UAV-aided data collection from deployed low-power ground SNs in dynamic jamming environments. The motion planning of the UAV is designed by considering the communication capability, motion primitives, and the low-power SNs when the sensors transmit data. The major contributions of this paper are as follows:  
* An optimization framework was developed to minimize the energy consumption of UAV data collection tasks with communication link quality while maintaining constraints such as the maximum UAV speed, obstacle avoidance, and minimum data requirements per SNs.
* Based on the introduction of relaxation variables and the application of SCA, we rewrite the nonconvex constraints of the original problem and fit the discrete points through B-spline curves. To ensure that it is applicable to dynamic environments, we further devise a motion planning approach, which is dependent on reprogramming and updated in real time with a dynamic jamming environment.
* We present a CFC-based path initialization method, which enables the initial path to meet the constraints on the communication rate and the amount of collected data. In addition, we present a safe flight path correction method based on a geometric method for fast obstacle avoidance, which ensures that the initial path is safe.
* Simulation results validate the performance of the proposed algorithm under different parameter configurations and compare to the benchmark algorithms.  
The remainder of this paper is structured as follows: In Section 2, we describe the investigated scenario and formulate the optimization problem through path discretization. Section 3 presents the designed non-convex solution for the problem. The simulation results in Section 4 validate the effectiveness of the presented algorithm. Lastly, the paper is summarized in Section 5.
# Fusion of Telecommunications and IT Services Boosted by Application Programming Interfaces
## 3 Continuation of Existing in Services and Replacement Possibilities
### CAP/INAP-Based Service Invocation  
The seamless continuation of CAP/INAP services for IMS subscribers can be enabled by the IM-SSF role of the IMS Application Server (IMS AS), which can even be integrated with the MMTEL/SCC AS (Multimedia Telephony/Service Centralization and Continuity Application Server) roles (Figure 5) [47; 48; 49]. Since IMS anchoring of the subscribers is optional during the call, IN services can be invoked from the 2G/3G domain as well.  
There are some drawbacks of this solution, as follows:  
* This approach requires the maintenance of the IN platform, and the service development needs telecommunication (SS7) experts. These products are mature and may be even close to the end of their life cycle;
* Although 3 GPP has specified the IP Multimedia Service Switching Function (IM-SSF), it is not delivered by all IMS core vendors. Therefore, continuation of existing IN services has become very challenging in many MNO networks;
* CAP/INAP services tend to follow the standard protocol specifications strictly; introducing protocol extensions may lead to incompatibility.
# Unsupervised Pre-Training for Voice Activation
## 2 Related Works
### Unsupervised Pre-Training for Speech  
Unsupervised pre-training is one of the methods to cope with limited resources and generally improve the quality of the resulting neural network [27]. The idea is to use a large corpus with no pre-existing labels to learn patterns in data and then to fine-tune the model on the data with labels.  
There are works on how to apply pre-training in voice-related problems. For example, the authors of [28] used per-layer pre-training to improve the quality of deep neural network-based ASR.  
A promising way to perform unsupervised pre-training for speech is to learn audio features instead of using classical MFCCs or log-Mel filter banks features. For example, a problem-agnostic speech encoder [29] is a feature extractor trained by jointly optimizing multiple self-supervised objectives. Autoregressive predictive coding [30] and contrastive predictive coding [31] are feature extractors trained with objective of predicting some future frames or information about them by having access to the information about the current and some past frames. The authors of these works tested audio features on problems of speech recognition, speaker identification, phone classification and speech translation.  
In our paper, we use the wav2 vec model [22]. It is a simple multi-layer convolutional neural network optimized via a noise contrastive binary classification task. The authors of [22] reported outperforming the best reported character-based system in the literature while using two orders of magnitude less labeled training data on the ASR task.  
To the best of our knowledge, our work is the first attempt to use pre-trained audio features like wav2 vec for a voice activation problem in a low-resource setup.
# Research on High-Performance Fourier Transform Algorithms Based on the NPU
## 4 Methods
### Acceleration Effect and Discussion
#### 5.2.3 Analysis of the Acceleration Effect of MV-2 FFT  
We measured the single execution time separately for two implementations of the MV-2 FFT algorithm, namely the MV-2 FFTm and the MV-2 FFTv, and analyzed the results to evaluate the impact of processing unit selection on performance.  
1. Analysis of Execution Time Change with Input Size  
Compared with MM-2 DFT, MM-2 FFT exhibited a higher execution time under the order of the input matrix size equal to 1024. Nevertheless, as the size of the input matrix increases, MM-2 FFT's execution time increases slower than that of MM-2 DFT. Eventually, when the order of the input square matrix reaches 2048, MM-2 FFTv's time efficiency surpasses that of MM-2 DFT. This observation implies that when the input data size is small, the scheduling overhead involved by the divide-and-conquer method outweighs the potential benefits of that algorithm. As the size of the input matrix increases, however, the calculation complexity advantage of the divide-and-conquer approach used in MM-2 FFT gradually becomes more prominent.  
2. Analysis of Performance Difference Between MV-2 FFTm and MV-2 FFTv  
When evaluating the two implementations of MV-2 FFT, the comparison result demonstrates that MV-2 FFTv outperforms MV-2 FFTm on all tested input sizes. This performance difference grows as the input size increases, ranging from 10% to 22% improvement as the order of the input square matrix grows from 64 to 2048. This computation time difference is caused by the different handling of those MV-2 FFT implementations to calculate \(D\times B\) in ALGORITHM 2. Specifically, MV-2 FFm calculates multiplication with the cube unit. In contrast, MV-2 FFv replaces matrix multiplication with vector multiplication, taking advantage of the sparsity property of the matrix, thereby accelerating computation.
# Performance Evaluation of China's First Ocean Dynamic Environment Satellite Constellation
## 3 Results
### Significant Wave Height (SWH) Verification
#### 3.3.1 Comparison SWH with the Buoy Data  
In this study, fifty NDBC buoys with water depths greater than 1000 m were used to assess the quality of the HY-2B/C/D satellites' SWH data. In this study, there are 354, 389, and 197 matching points between NDBC buoys and HY-2B/C/D at less than 50 km apart within 30 min, respectively. The spatial distribution of the buoys is shown in Figure 5.  
As shown in Table 3, the SWH measurements of the HY-2B, HY-2C, and HY-2D satellites had deviations. The average deviation of the HY-2B/C/D SWH were 0 m, \(-\)0.02 m, and 0.15 m, respectively. The root-mean-squared errors were 0.23 m, 0.25 m, and 0.30 m, respectively, with standard deviation of 0.23 m, 0.25 m, and 0.26 m, respectively. The NDBC buoy data are an important reference to evaluate the SWH from the radar altimeters, and the results show that the SWH data from the three satellite radar altimeters are in good agreement with the buoy data.  
Figure 5: Positional distribution of the buoys.  
The data of SWH \(<\) 0.5 m in altimeter data are generally not accurate enough, and similarly, when SWH is greater than 8 m, which is a giant wave, there are errors in the measurement, so the reliability of the data is somewhat controversial. In the validation of SWH, there was no significant difference between the HY-2B/D data and the NDBC matched data pairs. The data quality of HY-2B/D was stable and consistent. For HY-2C, this study recalculates the relevant parameters after excluding the three pairs of data with large differences, where the correlation coefficient is 0.9832 and the linear relationship between them is SWH\({}_{\rm NDBC}\) = 0.9415 \(\times\) SWH\({}_{\rm HY-2C}\) + 0.1122.
# Providing Email Privacy by Preventing Webmail from Loading Malicious XSS Payloads
## 5 Experiment
### Dataset
#### 5.2.3 Dataset and analysis  
We integrated the malicious and benign samples, and put them into data processing module. After processing, we used word2 vec to train the corpus and then extracted features to form the experimental dataset.  
Analyzing the malicious sample of our experimental dataset, we discovered some interesting conclusions. The number of Webmail XSS samples contributed by enterprises is shown in Table 7. We found that qq Webmail provided the most samples, which is 19; The sina Webmail provided 16 samples; Tom, 21 cn, 139 mail, 189 mail, sohu, 163 mail, we Webmail have provided more than 10 samples. It warns us that even Internet enterprises who focus on cybersecurity still have so many XSS vulnerabilities.  
We had statistics on the types of malicious samples, and the statistical results are shown in Figure 5. We found that most attacks are initiated in the content of the email. This is because the content is the body of the email and allows us to insert HTML tags, which makes it more possible to exist XSS vulnerabilities and easier to hide attack payloads. The Sender and Subject fields are also a part of attack payload. We observed the sample and found that only two cases occurred in the Receiver field, which both used the Webmail's own vulnerability, so we decided to ignore this field. The attachment is still noteworthy when design the Webmail system, and the number of attacks initiated by the filename and attachment has reached 20% in the malicious sample. If combining the time to analyze, we found that in the early days, most enterprises' Webmail XSS vulnerabilities are evenly distributed in various fields. As time passed and the bug is fixed, the XSS vulnerability is slowly concentrated in the content field. This is understandable because the filtering of other fields are easier than the content field.  
\begin{table}
\begin{tabular}{c c} \hline
**Webmail** & **Sample Number** \\ \hline qq & 19 \\ sina & 16 \\ tom & 15 \\
21 cn & 14 \\
139 mail & 13 \\
189 mail & 13 \\ sohu & 13 \\
163 mail & 10 \\ wo & 10 \\ \hline \end{tabular}
\end{table}
Table 7: Malicious Sample Number for Webmail.  
Figure 5: Field types in malicious samples.  
For experiments, we divided the experimental dataset into a train-dataset and a test-dataset (6:4) by Scikit-learn tools. Due to the amount of data, it is not appropriate to separate the verification-dataset from the original dataset. Therefore, we used the 10-fold cross verification to verify the dataset. We used SVM (Support Vector Machine) as the verification algorithm and calculated the average value of 10-fold cross-validation. Finally, the accuracy obtained by verification reached 0.9968, which means the dataset has extremely high training value. In the first experiment, we compared the effects of deep learning (Bidirectional-RNN) and general machine learning. In the second experiment, we studied whether the Bidirectional-RNN model with attention mechanism preforms better.
## Materials and Methods
### Data Collection and Statistical Analysis
#### 2.3.2 SPAD Value and Chlorophyll Estimation  
A chlorophyll meter (SPAD-502, Konica Minolta Inc., Osaka, Japan) measured the SPAD value of the third uppermost matured leaf, prior to harvesting the samples. Three measurements were taken for each sample. The 10 mg fresh samples were taken from young, fully developed leaves to estimate the chlorophyll concentration, where 80% cold acetone was used to extract chlorophyll. The absorbance of the supernatant was measured with a spectrophotometer (Biochrom Libra S22, Biochrom Co. Ltd., Holliston, MA, USA) at 663 nm and 645 nm after centrifugation at 3000 rpm. Calculations were done according to [PERSON] et al. [20].
# Investigation of Railway Network Capacity by Means of Dynamic Flows
## 4 Exemplary Application to a Sample Network
### Calculation of the Input Parameters
The resulting capacities are rounded down to the next integer. Instead of the STRELE-formula, other methods can also be used to specify an upper limit for the line capacity. For example, a limit for utilization according to UIC Code 406 could be provided. Through iterative addition of train services (cf. procedure described in Section 4.3), where it is checked in each iteration whether this limit can be adhered to or if it is already exceeded by the scheduled trains, the optimal utilization can be determined. For nodes, a capacity equivalent to that for lines does not exist. Instead, a realistic limit on the number of trains that can pass a node is approximated by including the number of tracks that can be occupied by stopping trains at the same time. On top of that, route conflicts between the different trains are considered. The minimum time interval between two conflicting trains is set to two minutes (see Section 3.2.2 for details). Moreover, the following assumptions are made for dwell times. Passenger trains have a minimum dwell time of five minutes at their start and destination stations as well as stations where they change the direction of travel. At other planned stops on their routes, they have a minimum dwell time of one minute and a maximum dwell time of two minutes. Additionally, passenger trains are not allowed to stop where they are not planned to stop. For freight trains, there are no restrictions regarding dwell times. The following Section 4.3 elaborates on how the optimal number of trains is determined, before the results for the network described in Section 4.1 are given in Section 4.4.