esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Signal Division Method of Bearing Health State
### Health State Division Method  
The key to the classification of bearing health status signals is to identify the fault occurrence time (FOT). The bearing signal typically goes through a stable phase, when the bearing is in the healthy stage. After FOT, the bearing degradation becomes severe and is in the fault stage.  
At the time of failure, the health factor will produce an elbow-point mutation due to the transition from the smooth phase to the failure phase. Therefore, in this paper, within the threshold condition of health factor 0.1, the range of abrupt change in health factor under the threshold condition is first observed to be determined. After that, the first-order differentiation of the RMS health factor within the range is performed to obtain the abrupt change condition of the RMS first-order differentiation point, to determine the health state of the bearing. The principle of bearing health state division is shown in Figure 3.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**RMS**} & \multicolumn{4}{c}{**Kurtosis Factor**} & \multicolumn{4}{c}{**STFT SUM**} \\ \cline{2-10}  & **Tre** & **Mon** & **Rob** & **Tre** & **Mon** & **Rob** & **Tre** & **Mon** & **Rob** \\ \hline Bearing1\_1 & **0.8632** & **0.6393** & **0.7908** & 0.1461 & 0.0328 & 0.7631 & 0.7415 & 0.3934 & 0.7613 \\ Bearing1\_2 & **0.9014** & **0.2625** & **0.7888** & 0.0595 & 0.1375 & 0.6750 & 0.8065 & 0.3500 & 0.7489 \\ Bearing1\_3 & **0.7560** & **0.6306** & **0.7775** & 0.7817 & 0.3121 & 0.7424 & 0.5804 & 0.5541 & 0.7521 \\ Bearing1\_4 & **0.3625** & **0.0083** & **0.7053** & 0.1645 & 0.0248 & 0.6589 & 0.2288 & 0.0248 & 0.6806 \\ Bearing1\_5 & **0.7584** & **0.4118** & **0.6760** & 0.2152 & 0.1373 & 0.8428 & 0.6369 & 0.2941 & 0.6728 \\ Bearing2\_1 & **0.3731** & **0.0449** & **0.7953** & 0.3905 & 0.0612 & 0.7414 & 0.2856 & 0.0082 & 0.7676 \\ Bearing2\_2 & **0.8838** & **0.2625** & **0.7798** & 0.1281 & 0.1000 & 0.7360 & 0.7200 & 0.2500 & 0.7413 \\ Bearing2\_3 & **0.8821** & **0.4812** & **0.8637** & 0.4732 & 0.0564 & 0.7555 & 0.8318 & 0.3835 & 0.8455 \\ Bearing2\_4 & **0.7909** & **0.7561** & **0.6504** & 0.7793 & 0.0244 & 0.6258 & 0.7406 & 0.7561 & 0.6414 \\ Bearing2\_5 & **0.9027** & **0.4911** & **0.8138** & 0.7806 & 0.1716 & 0.8172 & 0.8083 & 0.2840 & 0.7816 \\ Bearing3\_1 & **0.3207** & **0.0445** & **0.8944** & 0.1088 & 0.0051 & 0.9437 & 0.1934 & 0.0572 & 0.8837 \\ Bearing3\_2 & **0.7047** & **0.0790** & **0.8258** & 0.6124 & 0.0357 & 0.8545 & 0.5308 & 0.0156 & 0.8029 \\ Bearing3\_3 & **0.4141** & **0.2
# A Large Capacity Histogram-Based Watermarking Algorithm for Three Consecutive Bins
## 4 Experimental Results and Analysis
### Embedding Capacity and Perceptual Similarity  
In [PERSON] et al.'s method [13], two consecutive bins were combined into a group, and the bin width was set as \(M=2\); thus each of four bins carried a one-bit watermark. The total capacity in their method was \(256/(2\times M)\). If \(M\) is larger (\(M>2\)), the capacity will decrease obviously. However, in the proposed method, each of three consecutive bins was combined into a group to embed a 3-bit watermark. For an image of 8 bit in depth, the maximum embedding capacity of the watermarking algorithm was mathematically calculated as \(256/M\); in other words, this method will implement a 256-bit watermark insertion if the bin width is set as \(M=1\). Therefore, the proposed method is superior in terms of embedding capacity.
## 3 Experimental Results and Analysis
### Experimental Results
#### 3.4.2 Experimental Analysis of Group Weighting Module  
In order to verify the influence of the number of GWM groups on the network segmentation accuracy, this section conducts experiments on multiple different grouping situations of GWM based on the addition of MSL-FPN to obtain the number of groups when it improves the network segmentation accuracy best. First of all, we set the number of groups as 1, 2, 4, and 8, and they are denoted as G1, G2 and G3, G4. The experimental results are shown in Table 2, under the same experimental conditions for GWM to experiment with different structures. The average segmentation accuracy of GWM with the number of groups of 1, 2, 4 and 8 is 35.3%, 36.6%, 37.9%, and 36.6%, respectively, which is 3.1%, 4.4%, 5.7%, and 4.4% higher than that of CWVIS. It can be seen from Table 2 that the network segmentation effect is the best when the number of groups is set to 4, indicating that GWM has the best effect on the depth semantic information supplement of mask features and channel interaction in the subspace under this configuration.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **AP** & **AP-50** & **AP-75** & **AR-1** & **AR-10** & **FPS** \\ \hline CWVIS & 32.2 & 57.6 & 32.4 & 34.9 & 38.7 & 16.4 \\ CWVIS + sub3 + sub2 & 35.8 & 63.7 & 34.6 & 36.2 & 40.7 & 16.0 \\ CWVIS + sub3 + sub2 + sub1 & 36.6 & 68.8 & 35.5 & 37.5 & 41.7 & 15.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the results of multi-scale localization feature pyramid experiments of different structures.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **AP** & **AP-50** & **AP-75** & **AR-1** & **AR-10** & **FPS** \\ \hline CWVIS & 32.2 & 57.6 & 32.4 & 34.9 & 38.7 & 16.4 \\ CWVIS + SIR + G1 & 35.3 & 63.0 & 37.7 & 35.8 & 40.6 & 14.8 \\ CWVIS + SIR + G2 & 36.6 & 65.3 & 34.5 & 36.9 & 41.5 & 15 \\ CWVIS + SIR + G3 & 37.9 & 68.9 & 39.7 & 38.9 & 43.3 & 14.6 \\ CWVIS + SIR + G4 & 36.6 & 64.4 & 35.6 & 37.5 & 41.1 & 15.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the influence of the number of block weights on the segmentation accuracy.
# Modeling of Positive Switching Impulse Discharge of UHV Transmission Line Air Gaps
## 3 Discharge Characteristics
### Influence of Line Height  
Previous testing studies have shown that in transmission lines, with the increase of line height, the influence of height on the air gap breakdown voltage is reduced [2]. UHV transmission line towers are enormous in height, thus it is difficult to judge the influence of line height on breakdown voltage. In order to investigate the influence of line height in UHV transmission lines, the breakdown voltage of air gaps with different heights was studied using the breakdown voltage calculation method. Since the line height in UHV transmission lines is generally above 25 m, the conductor-tower gaps with line heights of 25 m, 30 m, 35 m, and 40 m were selected to study in this paper.  
The gap diagrammatic sketch and solid model are shown in Figure 3. The radius of the conductor was 0.05 m and the length was 20 m. The gap length d was the distance from the surface of conductor to the tower leg, which was 5 m. The line height h was the distance from the surface of the conductor to the ground. The breakdown voltage of four different line height gaps was calculated by the breakdown voltage calculation method. The results are shown in Figure 4. The results show that the variety of breakdown voltage was not more than 1%. Slight changes were possibly due to the insufficient height of the tower. However, it was concluded that the line height had little effect on the breakdown voltage of air gaps in UHV transmission lines.
# GPU-Accelerated CNN Inference for Onboard DQN-Based Routing in Dynamic LEO Satellite Networks
## 4 Proposed Method  
The main objective of this research is to present a novel system architecture that optimizes GPU performance to enhance the computational efficiency of large-scale data processing, considering the OBC of low-orbit satellites. As shown in Figure 2, the proposed system architecture is organized hierarchically. One of the primary advantages of this design is its ability to perform parallel computation. By leveraging the GPU's large number of compute cores, thousands of threads can execute simultaneously, enhancing parallel computation efficiency. In CNNs, many operations involve large matrix computations, and the GPU's multi-threaded structure allows these computations to be performed concurrently, achieving much faster speeds compared to a CPU. However, memory management is a crucial factor in performance optimization when performing GPU-based computations. In large-scale operations like CNNs, inefficient memory management creates bottlenecks, causing computational threads to wait unnecessarily for data, which hinders the GPU's ability to fully utilize its parallel processing power. To address this, we pre-allocated memory for weights and biases directly into the GPU's memory. Pre-allocating memory space  
Figure 1: Flowchart of onboard routing inference systems.  
maximizes computational performance by minimizing runtime overhead from frequent memory allocation and deallocation. This also improves memory access speeds. Additionally, asynchronous operations are easier to implement when data is pre-allocated in device memory, as there is no need to transfer data during CNN operations. By parallelizing kernel execution and memory transfer, we further maximized the computational resources of the GPU.
# A New Online Service for the Validation of Multi-GNSS Orbits Using SLR
## 3 Results  
This section summarizes the results of the SLR validation of MGEX CODE's orbit for the period January 2016-August 2017 performed using GOVUS. The results for the period January 2016-June 2016 were confronted with the latest articles on multi-GNSS orbit quality [1]. We show changes of validation results after the switch of the reference frame from SLRF2008 to SLRF2014. Moreover, results for period January 2016-August 2017 were presented for the particular types of satellites GLONASS M, K1, M+, Galileo FOC, IOV, BeiDoI GSO, MEO and QZSS. Galileo FOC satellites, which were launched in 2014 into extended, highly eccentric orbits (marked as FOC*), have been separated from the other FOC satellites. We also indicate different approaches to the selection of laser stations in the validation  
Figure 4: SLR residuals as a function of time; grey dots represents the change of \(|\beta|\) angles in time.  
process. This section highlights the actual place for GOVUS within MGEX activities in the context of orbit validation and proposes the directions for future developments.
# Aerial Coverage Analysis of Cellular Systems at LTE and mmWave Frequencies Using 3D City Models
## 2 System Model
### Scenarios
#### 2.2.2 Coverage Probability  
Coverage for a specific location in 3D space is calculated as true or false depending on whether the SINR at that given location is above a certain threshold. The performance of every link was estimated using the coverage probability at several altitudes, which is calculated by dividing the number of locations at a certain altitude where there exists coverage by the total number of considered locations at that altitude. Coverage probability represents how likely a UAV at a certain altitude will have a reliable link to any BS. The target SINR depends on the throughput requirement. UAVs could only need a command and control downlink, which results in an estimated data rate of only 60-100 kbps for the downlink [27]. As stated in [27], this requires a minimum SINR value of \(-6\) dB. By focusing on the lowest possible rate, a high coverage probability and hence best possible reliability is achieved.
## 3 Results
### Disease Recognition Results  
Figure 6 shows the recognition results of the nectarine disease identification. Figure 6c shows the fruit cracking recognition result with a feature vector dimension of 9. Table 2 shows the corresponding confusion matrix results, which are respectively expressed in numerical form and percentage form. As can be seen from Table 2, the sum of the rows is the total pixels of the predicted result for each category, the sum of the columns is the total pixels of the ground truth values for each category, and the diagonal elements are the correct prediction pixels of all categories. By calculating the sum of the diagonal elements, it can be concluded that the correct prediction pixels of all categories is 61910, and the OA is 94.47%. The classification accuracies of diseased, normal, and background parts are relatively high, achieving more than 91%, and the classification accuracy of the background parts is the highest at 98.10%, proving that this method has a better effect.  
The UA and PA of each category can also be calculated using the confusion matrix. Table 3 shows the UA and PA corresponding to Figure 6c. It can be seen that the UA and PA of the background and normal parts are both higher, reaching over 91%; the UA of the diseased part is the lowest at 55.34%. This is mainly because the border between the nectarine and the background is blurred with shadows, and it is easy to be confused with the diseased part; therefore, most of the edge of the nectarine parts and a few background parts are identified as being diseased.
# False Alarm Reduction Method for Weakness Static Analysis Using BERT Model
## 3 False Alarm Reduction Method for Reliability Analysis of Static Analysis Results
### BERT-Based Line-Level Weakness Analysis Model  
#### 3.1.1 Preparing Training Data  
Data cleanup, normalization, and feature extraction: Unnecessary comments should be removed within the source code that do not affect code execution or cause weaknesses in the system. In addition, information related to weaknesses should be normalized because the function name contains the sentences \"bad()\" and \"good()\", which affect the training of the model. In addition, empty lines or areas that were not helpful for training for weakness analysis were deleted to minimize the noise of the model. Through this work, the accuracy of the model can be improved by tokenizing only the necessary information and training.  
Tokenization: To use a pretrained model, the input data must be transformed into an appropriate format so that each sentence can be sent to a pretrained model to obtain its embedding. We used _neulab_/_codebert-cpp_[39] pretrained language model to generate a vector  
Figure 1: Architecture of false alarm reduction method on weakness static analysis using BERT model: (*) represents files with c or cpp extensions.  
representation of the source code. Here, tokenization is used to make it easier to assign meaning to paragraphs and sentences by dividing them into smaller units, such as in NLP. Typical sub-tokenizer methods include Byte Pair Encoding (BPE) [40], SentencePiece [41], and Word-Piece [42]. Because C/C++ programming languages have a higher complexity of syntax or semantics than other languages, Out-Of-Vocabulary (OOV) situations occur during machine learning. Therefore, in this study, the BPE algorithm in which words are cut into meaningful patterns and tokenized was selected.  
Token and position embedding: Each token in the source code consists of several tokens that depend heavily on the context (peripheral token) and location between the tokens in the function. Therefore, it is important to capture the code context and its location within the function when predicting the weakness at the function level. The purpose of this step is to perform encoding and embedding that capture the meaning of the code token and its corresponding position in the input sequence (code or line). BERT has three embedding layers: token, sentence, and position embedding [34].
## 4 Experiments
### Objective Evaluation  
We used MCD [37] as a measure for the objective evaluation. MCD measures the distance between a pair of spectral features. MCD is calculated as follows:  
\[\text{MCD}=\frac{10}{\ln 10}\sqrt{2{\sum_{i=1}^{N}}\left(m_{k,i}^{t}-m_{k,i}^{ \tau}\right)^{2}}\, \tag{11}\]  
where \(N\), \(m^{t}\), \(m^{c}\), and \(k\) denote the mel-cepstral coefficient (MCC) dimension, target speech MCC, converted speech MCC, and the frame index, respectively. We used a 16-dimensional MCC. Since the converted spectral features may have different lengths from the target, the dynamic time warping (DTW) algorithm was applied to compensate for the length difference. A lower MCD value between the converted speech and the target speech indicates a better voice conversion performance. Table 2 summarizes the average MCDs and 95% confidence intervals of the converted speeches by MaskCycleGAN and the proposed wav2 wav. The proposed method outperformed MaskCycleGAN in both intra-gender and inter-gender cases, with an average MCDof 6.06 and a 95% confidence interval of \(\pm\)0.08, demonstrating superior performance and more stable learning than the baseline method. An ablation experiment was performed to analyze the proposed method further. First, we replaced the MelGAN with the HiFi-GAN for the baseline MaskCycleGAN, which we call \"spec2 spec\" in Table 3. The same amount of training data was used to train both vocoders. Using the HiFi-GAN vocoder improved the MCD over the baseline MelGAN vocoder. Next, we replaced the analysis module of the \"spec2 spec\" with the feature extracting CNN (Figure 4), which is called \"wav2 spec\" in the table. That is, \"wav2 spec\" took waveforms as input and outputs spectral features; then, it reconstructed waveforms using a separately trained HiFi-GAN vocoder. By integrating the analysis and mapping modules, the voice conversion performance was improved further. The third variant, called \"spec2 wav\", did not use the feature extracting CNN, but used the vocoder as the generator. That is, only the mapping and reconstruction modules were integrated, leaving the analysis module standalone. It can be seen in the table that \"spec2 wav\" showed a better performance than \"wav2 spec\", indicating that merging the mapping and reconstruction modules was more effective than merging the analysis and mapping modules. The last row of Table 3 represents the proposed wav2 wav that integrates all three modules: analysis, mapping, and reconstruction, which achieved the best performance among all the variants. To analyze the spec2 wav and wav2 wav further, we looked into the learned weights of the feature-extracting CNN. The weights of the first CNN layer were not changed much from the initial weights. However, the second CNN layer, which initially corresponded to the mel-filterbanks, changed a lot. Figure 5 shows the learned weights of the second CNN layer after the training was completed. The learned weights were quite different from the original filterbank coefficients, where they covered wider frequency bands for each filter. Also, they were different from speaker to speaker.
# User-Centric Cell-Free Massive MIMO with Low-Resolution ADCs for Massive Access
## 5 Simulation Results  
### Simulation Setting  
In this section, we validate the closed-form SE expression proposed in Lemma 1 for user-centric cell-free massive MIMO systems under massive access scenarios. In this paper, we consider a scenario with \(L=50\) APs, in which all APs and UEs are randomly distributed within a square coverage area of 0.5 km \(\times\) 0.5 km. Furthermore, we assume that all APs are equipped with half-wavelength-spaced uniform linear arrays with \(N=4\). We apply the wrap-around technique to approximate an infinitely large network with 800 antennas / km\({}^{2}\). The AP-UE association algorithm proposed in Section 4 is applied, and the pilot assignment algorithm is performed using the UE-group method as employed in [21]. This method minimizes pilot contamination by grouping UEs with minimal overlapping APs into the same pilot group. Each UE is assigned a single orthogonal pilot sequence for all its AP connections, ensuring simplicity and coherence in channel estimation. By minimizing the intersection of APs among UEs within the same pilot group, the user-group method effectively reduces pilot contamination, maintaining system performance even in massive access scenarios. To obtain large-scale propagation conditions, we utilize the path loss and shadow fading models from the 3 GPP Urban Microcell model in ([35], Tab.7.4.1-1). Moreover, we adopt the system configuration of [36], in which the bandwidth is \(B=20\) MHz and the maximum UE transmit power is \(\rho=100\) mW. Also, the coherence blocks contain \(\tau_{c}=200\) channel, which could be achieved through a 2 ms coherence time and a 100 kHz coherence bandwidth. Furthermore, we assume that the channel is utilized for uplink pilot transmission for a duration of \(\tau_{p}=10\), and the remainder is used for data transmission. Table 2 shows the simulation parameters used in this paper.
# Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion
## 2 Materials and Methods
### Description of ET Models
#### 2.1.3 End-Member-Based Soil and Vegetation Energy Partitioning, ESVEP  
ESVEP is based on a trapezoid \(T_{rad}-f_{cover}\) framework, in which it considers fluxes acting in a \"parallel\" soil and canopy system [44]. As in TSEB-PT, ESVEP partitions \(T_{rad}\) as a linear weight of emitted radiance. Other similar models to ESVEP are HTEM [59] and TTEM [60], but ESVEP solves the trapezoid in a pixel-per-pixel basis overcoming the need for homogeneous weather forcing and roughness (Equation (6a)).  
\[T_{S,max} =\frac{r_{a}\left(R_{n,S}-G\right)}{\rho_{a}C_{p}}+T_{A} \tag{6a}\] \[T_{C,max} =\frac{r_{a}R_{n,C}}{\rho_{a}C_{p}}\frac{\gamma\left(1+r_{b,dry}/ r_{a}\right)}{\Delta+\gamma\left(1+r_{b,dry}/r_{a}\right)}-\frac{vpd}{\Delta+ \gamma\left(1+r_{b,dry}/r_{a}\right)}+T_{A}\] (6b) \[T_{S,min} =\frac{r_{a}\left(R_{n,S}-G\right)}{\rho_{a}C_{p}}\frac{\gamma }{\Delta+\gamma}-\frac{vpd}{\Delta+\gamma}+T_{A}\] (6c) \[T_{C,min} =\frac{r_{a}R_{n,C}}{\rho_{a}C_{p}}\frac{\gamma\left(1+r_{b,wet}/ r_{a}\right)}{\Delta+\gamma\left(1+r_{b,wet}/r_{a}\right)}-\frac{vpd}{\Delta+ \gamma\left(1+r_{b,wet}/r_{a}\right)}+T_{A} \tag{6d}\]  
where \(r_{a}\) is the aerodynamic resistance, \(r_{b,dry}\) and \(r_{b,wet}\) are resistances of dry and wet canopy respectively, \(\rho_{a}\) is the density or air, \(C_{p}\) is specific heat capacity at constant pressure, \(\gamma\) is psychometric constant and \(vpd\) is vapour pressure deficit of the air.
## 5 Conclusions  
An offline diurnal calibration was able to characterize the optical sensor's response and allowed us to resolve diurnal and seasonal PRI patterns. Corrected PRI values from SRS sensor proved to be appropriate proxies of light use efficiency. Our results contribute to the growing research indicating that continuous PRI sensors can be used to track diurnal and seasonal changes in efficiency. Still, it is important to define facultative and constitutive proportions within the PRI observations and how this may change over different ecosystems and growth stages. Studies on the explicit effect of environmental conditions on PRI signals will also prove to be important since the higher resolution of continuous measurements will need to be accurately characterized. In general, we see continuous PRI sensors as having numerous benefits for explicitly examining the cause of changing PRI, LUE, and productivity over time and space. For this reason, we see this technology of great value for the flux, remote sensing and modeling community.  
Conceptualization, S.C. and A.S.-A.; Methodology, S.C.; Formal Analysis, S.C.; Investigation, S.C.; Resources, A.S.-A.; Data Curation, A.S.-A.; Writing-Original Draft Preparation, S.C.; Writing-Review & Editing, S.C. and A.S.-A.; Supervision, A.S.-A.; Project Administration, A.S.-A.; Funding Acquisition, A.S.-A.  
**Funding:** This research was funded by a grant from the Canada Foundation for Innovation (CFI), Discovery Grant from the National Science and Engineering Research Council of Canada (NSERC) and IBM Alberta Center for Advance.  
**Acknowledgments:** We would also like to acknowledge the support of EMEND, and DMI Peace River Pulp Division, [PERSON], [PERSON], and [PERSON], for their technical and logistical support. Thank you to [PERSON] for her patience and support.  
**Conflicts of Interest:** The authors declare no conflict of interest.