esa-sceva/satcom-chunk-collection huggingface dataset:
# Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds
## 3 Minimizing Resource Gap
### Resource Gap  
Assume two servers \(S_{1}\) and \(S_{2}\), with two kinds of resources, \(R_{x}\) and \(R_{y}\). The quantity of \(R_{x}\) and \(R_{y}\) in \(S_{1}\) is (4,2), and the quantity of \(R_{x}\) and \(R_{y}\) in \(S_{2}\) is (2,4). Suppose two applications, \(AP_{1}\) and \(AP_{2}\), with different resource demands, the resource demand of \(AP_{1}\) is (2,1) and resource demand of \(AP_{2}\) is (1,2). Given an algorithm to allocate resources of \(S_{1}\) to \(AP_{2}\), the remaining resource of \(S_{1}\) is (3,0). Thus, in this allocation, three available but unused resources of the \(R_{x}\) are wasted. When the algorithm allocates resources of \(S_{2}\) to \(AP_{1}\), the three available but unused resources of \(R_{y}\) are wasted because of the remaining resource of \(S_{2}\) is (0,3). After the previous resource allocation, the system cannot afford more resource requests of other applications even though the overall system remains available \(R_{x}\) and \(R_{y}\) resources as (3,3). Accordingly, the higher heterogeneous the resource demand is, the more resource gap will be. As a result, the more available but unused resources are wasted in the system.  
To solve the resource wastage problem for the available but unused resources, the MRG algorithm was proposed to calculate the degree of resource wastage and improve resource utilization. The difference between the quantities of exhausted resources and remaining resources is termed as the _resource gap_. When a resource is exhausted and the quantity of remaining resources is the smallest, resource wastage for the available but unused resources is reduced. Figure 1 depicts an example to illustrate the resource gap. Assume three resources \(R_{1}\), \(R_{2}\), and \(R_{3}\) in the system. The initial quantities of \(R_{2}\) and \(R_{3}\) were considerably higher than that of \(R_{1}\). That is, before resource allocation, the original resource gap was two between \(R_{1}\) and \(R_{2}\) and four between \(R_{1}\) and \(R_{3}\). After resource allocation, the resource gap among remaining resources was one between \(R_{1}\) and \(R_{2}\) and two between \(R_{1}\) and \(R_{3}\). Accordingly, the resource gap between heterogeneous resources could be minimized after an efficient resource-allocation policy. Thus, all three resources are well utilized to prevent resource wastage.
# Improving Entity Linking by Introducing Knowledge Graph Structure Information
## 3 Learning Entity Embeddings KGEmbs
### Entity and Relation Embeddings  
In order to demonstrate more intuitively the effectiveness of the knowledge graph structure for entity linking, and also considering the speed differences of each model, we use the TransE model to train entity and relation embeddings on triples, where \(h,t\in E\) (the set of entities) and \(r\in R\) (the set of relations). The main idea is that the functional relation obtained from the edges labeled by \(r\) corresponds to the translation of the embedding; that is, we hope that \(h+r\approx t\) when \((h,r,t)\) holds, while \(h+r\) should be far away from \(t\) otherwise.  
In order to learn entity and relation embeddings, we minimize the following loss:  
\[\mathcal{L}=\sum_{(h,r,t)\in S}\sum_{(h^{\prime},r,t^{\prime})\in S^{\prime}_{ (h,r,t)}}\left[\gamma_{1}+d(\mathbf{h}+\mathbf{r},\mathbf{t})-d\Big{(}\mathbf{ h}^{{}^{\prime}}+\mathbf{r},\mathbf{t}^{{}^{\prime}}\Big{)}\right]_{+} \tag{1}\]  
where \([x]_{+}\) denotes the positive part of \(x\), \(\gamma>0\) is a margin hyperparameter, and \(d(\mathbf{h}+\mathbf{r},\mathbf{t})\) is an indicator to measure similarity. Here we use the \(L_{1}\)-norm, and  
\[S^{{}^{\prime}}_{(h,r,t)}=\left\{\left\{h^{{}^{\prime}},r,t\right\}\middle|h^{ \prime}\in E\right\}\cup\left\{\left(h,r,t^{{}^{\prime}}\right)\middle|t^{{}^ {\prime}}\in E\right\} \tag{2}\]  
The optimization is performed by stochastic gradient descent, and an additional constraint is that the \(L_{2}\)-norm of the embeddings of the entities is \(1\).
# Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
## 6 Conclusions  
RACH congestion problems resulting from massive access by the M2M devices cause severe performance degradation on both the eNB and M2M devices--for example, wasting the uplink  
Figure 11: Cumulative distribution function (CDF) of RA results when optimal access class barring (ACB) is used.  
resources such as RACH and PUSCH, and increasing the connection failure probability hindering the M2M communications.  
We conducted a study of the influence of the capture effect on the RA performance. It was observed that the RA performance could be improved when the Msg3 was captured by the eNB even if collision occurred. Because the capture effect could occur when the received signal powers were differentiated by the eNB, we proposed the Msg3 PR scheme that exploited the power ramping scheme to increase the capture probability.  
A steady-state analysis model was provided to show the performance of the proposed scheme in terms of the RA throughput, the connection failure probability, and the average energy consumption. Numerical results of analysis and simulation results showed that the Msg3 PR scheme with proper parameters increases the RA throughput (and also increased the PUSCH efficiency), even under heavy access loads. Moreover, the proposed scheme reduced the connection failure probability, which disturbs the M2M communications, by slightly increasing the average energy consumption.  
Moreover, by conducting additional simulation, we discussed the benefits of using the proposed scheme and the effects of coexistence with other RA-related technologies. The simulation results show that the proposed algorithm can be combined with other technologies to achieve a larger performance improvement. In future work, further optimization could be performed for RA-related technologies using the Msg3 PR scheme.  
This work was supported by the ICT R&D program of MSIP/IITP, Korea (B0101-17-1276, Access Network Control Techniques for Various IoT Services).  
[PERSON] conceived of the study, proposed the idea and analysis model, designed the simulations, and wrote the paper; [PERSON] participated in the coordination of the research, corrected the manuscript and searched for funding to support the work.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Analog Interface Circuit  
A detailed circuit diagram of the analog interface is shown in Figure 3. The analog interface is basically comprised of a TX and receiver (RX) part. In the RX part, a T/R switch protects low-voltage amplifier circuits, and analog signal conditioning, including amplification and filtering, is performed by a pre-amplifier and an ADC driver. The cascaded amplifiers have the characteristics of a band-pass amplifier with an order of 4, and the overall voltage gain of the RX part is 40 dB.  
Figure 1: Features of this work compared to conventional medical ultrasound system; ROI: region of interest.  
In the TX part of Figure 3, a bipolar pulsing scheme is adopted. Compared with a conventional unipolar high-voltage TX pulser, a bipolar pulser theoretically has a better energy efficiency for the same peak-to-peak voltage swing [17; 18]. In addition, most previous high-voltage bipolar pulsers are based on a switching topology and need several input-control signals, along with multiple power supplies for level-shifting operation [19; 20; 21; 22]. For simple control of the TX pulser, we propose a single-ended-amplifier-based topology as shown in Figure 3. A low-voltage bipolar pulse signal is amplified with a differentiator circuit, which is denoted as an HV transmitter in Figure 3. The low-voltage bipolar pulse signal can be generated with a bipolar analog multiplexer by using a unipolar digital logic signal, which is fed from the FPGA. The output of the HV transmitter is delivered into the transducer via a grass-clipping diode. This grass-clipping diode passes a high-voltage signal during the TX phase and makes the transducer node float during the RX phase [19; 20; 21; 22].  
A timing diagram of the TX part is shown in Figure 4. The upper two signals of _TX_ and _Unipolar pulse_ are fed from the FPGA. The triggering pulse _TX_ encloses the signal _Unipolar pulse_ and is fed into a selection signal of the analog multiplexer (MAX320, Maxim  
Figure 3: Circuit diagram of analog interface; HV: high voltage; T/R: transmit/receive; DC: direct current; ADC: analog-to-digital converter.  
Figure 2: Proposed image-free ultrasound blood-flow-monitoring circuit system; TX: transmitter; AFE: analog front-end; FPGA: field-programmable gate array; RF: radio frequency.  
Integrated Inc., San Jose, CA, USA), as shown in Figure 3. The default output of the analog multiplexer is DC voltage of 1.65 V. When the triggering signal _TX_ is enabled, the inverted _Unipolar pulse_ is delivered into the output of the analog multiplexer. As mentioned before, the output of the multiplexer is amplified by the HV transmitter (THS3120, Texas Instruments Inc., Dallas, TX, USA), as shown in Figure 3. Accordingly, the HV transmitter has a common voltage of zero and generates amplified alternating signals. The peak-to-peak value of the HV transmitter output is determined by the output voltage range of the operational amplifier (op amp).
## 3 Results and Validation
### Validation
#### 3.2.1 Validation of SRGAN Model and Super-Resolved NDVI
at high altitudes may not be accurately captured by PSNR and SSIM. Furthermore, remote sensing data in this region can be significantly influenced by cloud cover and atmospheric conditions, potentially compromising the precision of these two metrics. Consequently, subsequent work may need additional ecological and environmental indicators to facilitate a comprehensive evaluation. Alternatively, an error propagation model could be used to quantify the uncertainties of SRGAN in land cover detection, including simulations of the effects of cloud cover and poor data quality, to evaluate their impact on the final land cover classification results. Furthermore, integrating time series structural information and surface feature data can help constrain errors introduced by super-resolution methods. Since land features change in predictable patterns over time, existing time series data can be used to backtrack to periods with missing data, thereby limiting error propagation. In addition to quantitative analysis, we visually interpreted the results of super-resolution (see Figure 14). Figure 14a,b display the original AVHRR NDVI data from the Qilian Mountains area in June 1986. Figure 14b-f present super-resolution results at 1 km and 250 m. The left column shows overall maps of the study area, while the right column provides detailed enlargements of the black boxed region. Figure 14g illustrates the super-resolution of Figure 14e to 30 m, and Figure 14h shows the nearest neighbor resampling of Figure 14e to 30 m. After applying super-resolution to the coarse spatial resolution AVHRR data, not only did the image resolution increase, but the details and texture characteristics became richer, better preserving high-frequency information and providing clearer outlines of structures in the image. A comparison between 30 m resampled data and super-resolution data revealed that the resampling method did not introduce blurring, noise, or artifacts. This indicates that when the magnification factor of SR is too high, as in this study where it increased from 4 km to 30 m by almost 130 times, it does not necessarily result in a clear high-resolution image but may introduce noise that distorts the image. Therefore, selecting the appropriate magnification factor is crucial for the accuracy and results of SR models. Figure 14: June 1986 NDVI super-resolution results (The left column (**a**,**c**,**e**) displays the results of SR NDVI data in the Qilian Mountains from original resolution to 1 km and further to 250 m; the right column (**b**,**d**,**f**) shows enlarged details of the black box area and (**g**,**h**) show 30 m SR data and resampled data, respectively).
## 3 Field Test  
### Engineering Background  
The Haoshanyu Tunnel of Qinglan Expressway is located at the junction of Zhangjiapo Town and Shiqiao Town in Yiyuan County. The Haoshanyu Tunnel is a separate one-way double-track tunnel. The starting and ending pile number of the left line of the tunnel are K221 + 597\(\sim\)K222 + 297, the length is 700 m. The starting and ending mileage of the right line is YK221 + 617\(\sim\)YK222 + 332, the length is 715 m. The Haoshanyu Tunnel belongs to the middle tunnel. As shown in Figure 1.  
Fire prevention and noise reduction are two of the key points in the tunnel operation. The previous main fire prevention method used in the Haoshanyu Tunnel was spraying fire retardant coatings. However, due to the cool and humid environment in the tunnel and the lack of ventilation, the fire retardant coatings are prone to mildew and discoloration. During the review of the Haoshanyu tunnel, it was found that the average fire retardant coating of the Haoshanyu tunnel peeled off about 6 pcs/80 m\({}^{2}\), as shown in Figure 2. Therefore, measures need to be taken to enhance the tunnel fire performance.  
According to statistics from the transport sector, the traffic volume in the Haoshanyu tunnel has increased year by year, and the equivalent total traffic volume increasing from 23,550 vehicles per day in 2017 to 33,773 vehicles per day in 2020, as shown in Figure 3. With the increase of traffic flow, the noise pollution in the tunnel becomes more and more serious. Therefore, it is necessary to take noise reduction measures.  
The closed-cell aluminum foam board have great fire resistance and heat insulation performance [27; 28; 29]. The existing research shows that the thermal conductivity of aluminum foam is very low, and the aluminum foam will not produce toxic and harmful gases at high temperature. So, it can be used as a substitute for the traditional thermal insulation refractory in tunnels. As for the sound absorption properties of closed-cell aluminum foams, it has been preliminarily confirmed [17]. Since the closed-cell aluminum foam board has the properties of fire prevention and sound absorption, it meets the needs of the Haoshanyu Tunnel. The research on the sound absorption performance of closed-cell aluminum foam board in the tunnel was carried out based on the installing project of aluminum foam board in the Haoshanyu Tunnel. The Haoshanyu Tunnel has also become the first highway tunnel in China to install aluminum foam boards.
# MT-FBERT: Malicious Traffic Detection Based on Efficient Federated Learning of BERT
## 4 Experiments
### Overall Comparisons  
We evaluated the performance of MT-FBERT in malicious traffic detection by comparing it with three baselines on four malicious traffic datasets. As shown in Table 2, MT-FBERT achieves statistically significant performance improvements over all comparative methods on both the CICMalAnal2017 and IoT-22 datasets, exhibiting superior results across all evaluation metrics. In addition, MT-FBERT reaches 99% in all metrics on both the USTC-TFC2016 and IoT-23 datasets. From Table 2, we can find that the TFE-GNN and YaTC methods achieve perfect scores across all metrics on USTC-TFC2016 and IoT-22 datasets. The reason is that these two datasets only contain two types of network traffic data: normal and malicious flows. Furthermore, we perform the cross-dataset evaluation on USTC-TFC2016 and IoT-22 datasets. Training the models on the USTC-TFC2016 dataset and then testing them on IoT-22, and vice-versa. The results are illustrated in Table 3. From the table, we can see that MT-FBERT and YaTC demonstrate more stable detection performance compared with other baselines. Because MT-FBERT and YaTC are the pre-training models that have good robustness and generalization. To comprehensively evaluate the performance of MT-FBERT and baselines across different datasets, we calculated the average values of AC, PR, RC, and F1 over the four datasets, denoted as Avg. AC, Avg. PR, Avg. RC, and Avg. F1 respectively. Figure 5 illustrates the comparative results of MT-FBERT against other baselines in terms of Avg. AC, Avg. PR, Avg. RC and Avg. F1. From the figure, we can see that MT-FBERT achieves 0.9176 for Avg. AC, 0.9146 for Avg. PR, 0.9178 for Avg. RC, and 0.9221 for Avg. F1. Compared with baselines, MT-FBERT demonstrates superior performance in terms of the highest average values of all metrics over the four datasets. These results indicate that the MT-FBERT performs robustly across various datasets, maintaining consistent malicious traffic detection performance regardless of the differences in dataset characteristics. This highlights its strong generalizability and reliability for malicious traffic detection. In addition, the \(t\)-test is conducted to validate if there are significant improvements between MT-FBERT and the baselines. Taking FlowPrint as an example, the Null Hypothesis (H0) assumes no significant improvements between MT-FBERT and FlowPrint. Alternate Hypothesis (H1): A significant difference between MT-FBERT and FlowPrint makes the improvement significant. First, we calculate the means and standard deviations of the MT-FBERT and FlowPrint based on AC, PR, RC, and F1 in Table 1, donated as \((\overline{x}_{1},\overline{x}_{2})\) and \((s_{1},s_{2})\). Second, we calculate the observed value as:  
\[t_{observed\_value}=\frac{\overline{x}_{1}-\overline{x}_{2}}{\sqrt{\frac{ \overline{s}_{1}^{2}}{\overline{s}_{1}}+\frac{\overline{s}_{2}^{2}}{\overline {s}_{2}}}} \tag{11}\]
###### Abstract  
The main aim of this study was to evaluate the applicability of the co-kriging method for modeling the vertical movements of the Earth's crust based on data acquired with the use of precision leveling techniques and measurements conducted by permanent Global Navigation Satellite System (GNSS) stations. Data were processed with the use of empirical, theoretical, and directional variograms (semivariograms), as well as variogram maps. Large-scale spatial variability was determined using polynomial regression. The relationships between the length of the semi-major and semi-minor axes vs. the root mean square (RMS) and the standard error of the estimate were analyzed. The relationships between the anisotropic direction and the number of lags were determined, and other parameters were calculated. Preliminary data fitting produced non-stationary surfaces. The leveling data were anisotropic, and the GNSS data were isotropic. Nugget effects were observed in both datasets, in particular in the GNSS data. The size of the ellipse was strongly correlated with the RMS and \(\sigma\) (average standard deviation of prediction). The anisotropy angle was determined using the number of lags. Co-kriging was found to not be a suitable method for modeling the vertical movements of the Earth's crust based on data from various sources. The final result was strongly influenced by the initial dataset. The obtained results show how the method of combining data sets (interpolation, network adjustment) affected the final cartographic model.  
ground deformation; cartographic modeling; co-kriging; vertical crustal movements +
Footnote †: journal: Applied Physics Letters
# Keyframe Insertion: Enabling Low-Latency Random Access and Packet Loss Repair
## 4 Results
### Impact on Quality
#### 4.3.3 PSNR Decrease  
For completeness, Table 4 shows the average decrease in PSNR. Compared to using VMAF, \(\text{K}_{\text{INS}}\) performs better than \(\text{K}_{\text{src}}\) in more cases (e.g., also when \(\text{QP}_{\text{NS}}=\text{QP}_{\text{CS}}=37\)). Additionally, when increasing the \(\text{QP}_{\text{NS}}\) value (with a constant \(\text{QP}_{\text{CS}}\) value), the quality decrease remains approximately constant. In contrast, using VMAF, the quality decreased more for high QP values. This difference between PSNR and VMAF is explained by the logarithmic scale of PSNR. For example, for \(\text{K}_{\text{src}}\), both \(\text{QP}_{\text{NS}}=\text{QP}_{\text{CS}}=27\) and \(\text{QP}_{\text{NS}}=\text{QP}_{\text{CS}}=37\) have an equal decrease in PSNR of 0.32 dB. However, the PSNR of \(\text{NS}_{\text{src}}\) with \(\text{QP}_{\text{NS}}=27\) is larger than the PSNR of \(\text{NS}_{\text{SRC}}\) with \(\text{QP}_{\text{NS}}=37\). Due to the logarithmic scale, a decrease of 0.32 dB from a large initial PSNR is caused by less pixel changes than from a small initial PSNR. Furthermore, no quality increase compared to \(\text{NS}_{\text{SRC}}\) is observed for \(\text{QP}_{\text{NS}}=37\) and \(\text{QP}_{\text{CS}}\leq 32\) in terms of PSNR. Last, we can see a that the quality decreases less when using a larger \(\text{QP}_{\text{CS}}\) value (while keeping \(\text{QP}_{\text{NS}}\) constant), which is similar to the observation in Table 2.
## 2 Signal Processing and Height Inversion
### Height Inversion
#### 2.2.2 Height Retrieval and Precision Calculation  
After delay estimation and error correction, the vertical distance from the sea surface to the reference ellipsoid can be computed as:  
\[\text{SSH}=H_{e}^{\text{obs}}-H_{e}^{\text{m}}=H_{e}^{\text{obs}}-\frac{\tau_ {\text{sp}}^{\text{m}}}{2\times\sin(e)} \tag{5}\]  
where \(H_{e}^{\text{m}}\) is the height of the aircraft on the WGS84 reference ellipsoid obtained by using bistatic geometry. A fitted piecewise linear function is subtracted from the measured SSH sequence to generate zero mean, near-white noise residuals. The precision is obtained by calculating the standard deviation of the SSH residuals for each trajectory.  
\[\sigma_{h}^{\text{obs}}(T_{c})=\sqrt{\left\langle\left|\text{SSH}_{\text{ residual}}^{\text{k}}-\left\langle\text{SSH}_{\text{residual}}^{\text{k}} \right\rangle\right|^{2}\right\rangle} \tag{6}\]  
It is primarily due to the following considerations that linear fitting is used instead of the geoid model. The altimetry precision is affected by zero-mean random error, which is mainly due to the random nature of the received signals caused by thermal noise and speckle noise. The SSH residual after subtracting the linear fit can be used to evaluate randomness. In contrast, precision will be affected by the errors in the geoid model [27].