esa-sceva/satcom-chunk-collection huggingface dataset:
# A Simple Relationship to Estimate Parameters of the Optimum Compaction Point
## 5 Conclusions  
Clay material can be used as a hydraulic barrier in mine site reclamation. These barriers allow limiting water infiltration to reactive tailings. Consequently, the oxidation of sulfide minerals is limited, and the acid mine drainage production is inhibited. The performance of these hydraulic barriers is mainly related to material properties and to compactive effort and molding water content during cover construction. These cover materials must be compacted to the pre-determined dry unit weight that corresponds usually to a certain percentage of the maximum dry unit weight (\(\gamma_{dmax}\)).  
During the prefeasibility stage of a cover construction, \(\gamma_{dmax}\) and \(w_{Opt}\) can be estimated to obtain an early overview of the conditions that can be anticipated.  
The proposed equations in this paper were defined to provide a simple means to estimate parameters of the optimum compacted point using the liquid limit and the plastic  
Figure 5: Correlation between measured literature data [30] and estimated \(\gamma_{dmax}\) using the literature estimation [30] and author equations.  
Figure 6: Correlation between measured data from [30] and estimated \(w_{Opt}\) using the literature estimation [30] and author equations.  
limit. The comparison between estimated and measured values shows that the proposed equations lead to good estimation of the maximum dry unit weight and the optimum water content. The proposed equation for the estimation of the compacted optimum parameters would be particularly convenient at the first stage of a project when little information is available.  
More work is presently underway to estimate compaction point parameters for other materials with very high plasticity and to estimate the liquid limit and the plastic limit using grain-size parameters.  
This research was supported by Natural Sciences and Engineering Research Council of Canada NSERC.  
The author declares no conflict of interest.
## 2 Principle Analysis and Modelling  
### Principle Analysis  
According to the Kubo formula, the conductivity of graphene includes intra- and interband contributions, which can be described as [33; 34; 21; 35; 36]:  
\[\sigma(\omega,\mu_{c},\Gamma,T)=\frac{je^{2}(\omega\text{-}j\Gamma)}{\pi\hbar^{ 2}}\times\left[\begin{array}{c}\frac{1}{(\omega\text{-}j\Gamma)^{2}}\int \limits_{0}^{\infty}\limits\limits\limits\limits_{\Gamma}\left(\frac{\partial f _{d}(\varepsilon)}{\partial\varepsilon}\frac{\partial f_{d}(\varepsilon)}{ \partial\varepsilon}\right)\text{d}\varepsilon\\ -\int\limits_{0}^{\infty}\limits\limits\limits\limits\limits_{\Gamma}\left( \frac{f_{d}(\varepsilon)-f_{d}(\varepsilon)}{(\omega\text{-}j\Gamma)^{2}-4( \varepsilon/\hbar)^{2}}\right)\text{d}\varepsilon\end{array}\right] \tag{1}\]  
where \(f_{d}(\varepsilon)=\left[\exp(\varepsilon\text{-}\mu_{c})/\left(k_{B}T\right)+ 1\right]^{-1}\) is the Fermi\(-\)Dirac distribution, \(\Gamma\) is the carrier scattering rate (its reciprocal \(\tau=0.2\) ps is the electron relaxation time), \(\hbar\) is the reduced Planck constant, \(T\) is the Kelvin temperature, \(\mu_{c}\) represents the chemical potential of graphene, \(\varepsilon\) is the energy, and \(k_{B}\) is the Boltzmann constant.  
By applying voltage, pumping light or ion doping, the chemical potential of graphene can be changed to achieve conductivity regulation, and the depicted conductivity curves of graphene changing associated with frequency and \(\mu_{c}\) are as follows [33; 34; 35]:  
It can be observed from Figure 1 that in the microwave frequency band and the low terahertz frequency band, the conductivity of graphene was independent of frequency and only changed with the chemical potential, thus the tuning of chemical potential can effectively control the conductivity of graphene.
# Degree-Constrained Steiner Problem in Graphs with Capacity Constraints
## 6 Approximations
### Case of \(0<|V_{1}|\leq 2\)  
In this case, we suppose that \(V_{1}\) is not empty but it contains at most two nodes that are not separators. We propose to analyze the case of \(|V_{1}|=2\), but the result is trivially true if there is only one node in \(V_{1}\).  
**Theorem 4**: _In the case of at most two nodes in \(V_{1}\), the cost of a DCMStH can be approximated within a factor \(2\rho\), where \(\rho\) is the ratio of the approximation of the minimum Steiner tree._  
**Proof.** We let \(a\) and \(b\) be the two nodes in \(V_{1}\) and let \(T_{A}\) be an approximated tree of the Steiner tree. Following \(T_{A}\), a walk visiting the nodes of the tree can be computed starting from \(a\) and ending at \(b\). This walk \(W\) contains the edges of \(T_{A}\) at most twice and corresponds to a spanning hierarchy satisfying the degree constraints (the internal nodes in \(W\) have only a degree of two).  
\[c(W)\leq 2*c(T_{A})\leq 2*\rho*c(T_{S})\leq 2*\rho*c(DCMStH).\]  
\(\square\)  
Note that if there are nodes with a degree bound greater than two, a hierarchy with lower cost can be computed.
# SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks
## 4 Performance Analysis
### Fairness Test
\begin{table}
\begin{tabular}{c c c} \hline  & **R0** & **R1** \\ \hline Capacity & 100 Mbps & 100 Mbps \\ \hline Delay & 0 ms & 20 ms \\ \hline Loss & 0\% & 0\% \\ \hline Buffer Size & 100,000 packet & 1000–100,000 packet \\ \hline \end{tabular}
\end{table} 
Table 3: Interprotocol fairness test network setup. Figure 9: Intraprotocol fairness test results. Figure 9(a) illustrates the fairness test results when mixing BBR flow with CUBIC flow using a routing buffer size of 1000 packets, while Figure 9(b) presents these results with a routing buffer size of 100,000 packets. Larger routing buffers result in increased bandwidth allocation for CUBIC flows, whereas smaller buffers allocate most of the bandwidth to BBR flows. Figure 9(c),d depicts fairness test outcomes when combining BRF flow with CUBIC flow using buffer sizes of 1000 packets and 100,000 packets, respectively. These figures demonstrate that when mixed with the CUBIC protocol, BRF exhibits higher fairness compared to BBR.
# Hybrid Modulation Strategy to Eliminate Current Distortion for PV Grid-Tied H6 Inverter
###### Abstract  
This paper proposes a new hybrid modulation mode (HMM) to eliminate the zero-crossing distortion of grid current and enable reactive power provision for a H6 configuration PV (photovoltaic) grid-tied inverter. The common mode voltage, leakage current, and efficiency for the proposed approach are also analyzed. In order to improve grid frequency tracking a novel frequency self-adaptive proportional-integral-resonant (FSAPIR) controller is implemented which reduces error for changes in grid frequency. The proposed approach provides the basis for accurately adjusting the active and reactive current without error to improve the grid support capability of the inverter. Theoretical analysis, simulation, and experiment verify the newly proposed modulation mode and controller.  
proportional-integral-resonant; self-adaptive; common-mode leakage current; hybrid modulation; zero-crossing distortion; reactive power Article
# Cross-View Attention Interaction Fusion Algorithm for Stereo Super-Resolution
## 3 Experimental Results
### Qualitative Analysis
For the SRResNet + our algorithm, the calculated PSNR and SSIM values are the highest at the sign position, which are 25.61 dB and 0.868 dB, respectively; At the air inlet of the automobile engine, the calculated PSNR and SSIM values are also the highest, which are 25.71 dB and 0.869 dB, respectively. Through visual and numerical analysis, it shows that cross-view attention information interaction can further improve the performance of stereo image reconstruction based on single image super-resolution network and has an important impact on stereo super-resolution. Figure 4: Super-resolution visualization comparison on the Flickr 1024 dataset. Figure 5: Super-resolution visualization comparison on the KITTI 2012 dataset.
## 5 Energy Result Analysis and Reliability Evaluation
### Energy Result Analysis
#### 5.1.3 Coefficient of Variation Analysis  
According to the peak pressure data obtained from the previous closed explosive test of the same kind of ignitor, the coefficient of variation of the pressure sample data can be calculated, and the coefficient of variation indicates the degree of dispersion of the data.  
The historical peak pressure data of the ignitor are shown in Table 11.  
It can be seen from Table 11 that the coefficient of variation of the peak pressure is 0.092, and from the previous text, the coefficient of variation of the transferred energy measured by the absorbing cup device is 0.221, while the variation coefficient of the aluminum honeycomb device is 0.109, and the piston device has a variation coefficient of 0.060, while the pin-rod device has a variation coefficient of 0.098.  
Therefore, the coefficients of variation of the transferred energy measured by the aluminum honeycomb, piston, and pin-rod type of devices are close to the coefficient of variation of the peak pressure, while the coefficient of variation of the transferred energy measured by the absorbing cup device is significantly larger than the coefficient of variation of the peak pressure. The authors think this is probably caused by the nut connecting the absorbing cup to the pin. Part of the absorbing cup is in contact with the nut after being folded inward to a certain extent during the deformation process; therefore, the absorbing cup is subjected to the force of the nut, resulting in uncertainty of the folding.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Value** \\ \hline \multirow{3}{*}{Peak pressure (MPa)} & 26.29, 33.71, 32.04, 30.3, 32.59, 32.29, 28.08, 27.53, 31.08, \\  & 28.40, 26.26, 27.69, 29.27, 25.07, 31.91, 26.45, 31.67, \\ \cline{1-1}  & 26.68, 26.77 \\ \cline{1-1}  & 29.132 \\ \cline{1-1} Sample standard deviation (MPa) & 2.673 \\ \cline{1-1} Sample coefficient of variation & 0.092 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The historical peak pressure data of the ignitor.  
Thus, the transferred energy measured by the absorbing cup device is not reliable and cannot be used to evaluate the reliability of the pin puller.  
The energies measured by the other three devices are reliable, but the piston device only tested the output capacity of the ignitor, and did not take the actual working environment of the pin puller into consideration, so these data cannot be used for reliability evaluation either.  
Although the energy measured by the aluminum honeycomb device is relatively small, it can still be regarded as a conservative assessment of the reliability of the pin puller, while the transferred energy measured by the pin-rod device can be perfectly used as a performance parameter to evaluate the reliability of the pin puller.
# Analysing the Performance of a Trust-Based AODV in the Presence of a Flooding Attack
## 7 Simulation and Evaluation
### The Overhead  
End-to-end delay is one of the QoS metrics that can be used to measure the overhead. QoS should be always measured when adding any kind of mechanism to a routing protocol. It is important to determine the trade-off between the cost and the improvement achieved.  
Figure 7 shows that the more work added to the AODV protocol, the greater the effect on the end-to-end delay metric. End-to-end delay is a very sensitive metric and is likely to be affected by adding any load to the protocol. The simulation shows that the end-to-end delay increased by around 0.2 s in TAODV, which seems slight compared to the improvement in the throughput metric and the reduction in the number of fake REQs.
## 4 Method
### MSF Method  
Due to degree truncation and filtering processing, the gridded TWSC from the SH solutions inevitably has signal attenuation and leakage [6]. Therefore, it is necessary to modify the TWSC gridded data. Additionally, the scale factor, an effective method, is often used for signal recovery [41; 42]. However, since the signal recovery is within the scope of the entire study region, it is unreasonable to only rely on the scale factor obtained from the regional weighted average to recover the signal of the entire region [30]. Additionally, the TWSC time series may be diversity on different scales, so the MSF method was used to recover the Swarm and GRACE TWSC signals in our study. The specific processing flow is as follows:  
1. The gridded TWSC data were provided by the Global Land Data Assimilation system (GLDAS) the Catchment Land Surface Model (CLSM), which is the sum of soil moisture storage, snow water equivalent storage, plant canopy water and groundwater. Additionally, TWSC time series (denoted as \(TWSC_{original}\)) in the study region was calculated by spatial averaging.
2. \(TWSC_{original}\) was split into the long-term trend items \(S_{original}^{trend}\), seasonal items \(S_{original}^{seasonal}\) and residual items \(S_{original}^{residual}\).
3. The original TWSC gridded data was expanded into 60-degree (GRACE) or 40-degree (Swarm) SH coefficients, and then it was converted into TWSC gridded data (\(TWSC_{convert}\)) using the same processing method as GRACE or Swarm data.
4. Same as the second step, \(TWSC_{convert}\) was split into the long-term trend items \(S_{convert}^{trend}\), seasonal items \(S_{convert}^{seasonal}\) and residual items \(S_{convert}^{residual}\).
5. According to the least square method, the scale factors of the above three signal components (\(k^{trend}\), \(k^{seasonal}\) and \(k^{residual}\)) were obtained, respectively.
6. The time series of TWSC from GRACE or Swarm data were also split into the long-term trend items \(S_{gravity}^{trend}\) seasonal items \(S_{gravity}^{seasonal}\) and residual items \(S_{gravity}^{residual}\).
7. The three components obtained by decomposition in step six were multiplied by the three scale factors in step five. The signal modification result \(S^{\text{modified}}\) is expressed by the following formula: \[S^{\text{modified}}=k^{trend}S_{gravity}^{trend}+k^{seasonal}S_{gravity}^{ seasonal}+k^{residual}S_{gravity}^{residual}\] (3)  
The results of scale factors of four GRACE and Swarm SH solutions were shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**SH Solution** & \(\mathbf{k^{trend}}\) & \(\mathbf{k^{seasonal}}\) & \(\mathbf{k^{residual}}\) \\ \hline CSR-GRACE & 1.171 & 1.171 & 1.000 \\ GFZ-GRACE & 1.171 & 1.171 & 1.000 \\ JPL-GRACE & 1.171 & 1.171 & 1.000 \\ ITSG-GRACE & 1.171 & 1.171 & 1.000 \\ Swarm & 1.173 & 1.173 & 1.000 \\ \hline \end{tabular}
\end{table}
Table 2: The scale factors of four GRACE SH solutions and one Swarm solution.
# An Architecture for Reliable Transportation of Delicate Goods
## 5 Workflow
### Merchandise Transmission
#### 5.2.2 Merchandise Receipt Protocol  
The merchandise receipt protocol is executed after the iteration protocol (Figure 5) by the operator who wants to formally make the reception of the merchandise. From the smartphone, the operator chooses the merchandise and requests the reception, sending to the cloud server the merchandise temporary identifier (Figure 8--message 1). Notice that the reception option is only available if the state of the merchandise is DEPOSITED.  
The authentication procedure is similar to the delivery protocol. Once the Closed Access BLE Services are available, the mobile app requests the Data token to the cloud server (Figure 8--message 3), passing once more the merchandise temporary identifier. The Data token is produced encrypt symmetrically the merchandise temporary identifier, the next operator identifier, the associated info and the maximum time to collect the  
Figure 8: Merchandise receipt protocol.  
merchandise after the next delivery. This token is used to update the state of the device (Figure 8--message 4). Once done, the Closed Access BLE service is disabled and the mobile app is notified by the device to update the state (Figure 8--message 4.2), disabling the option to receive the merchandise and enabling the option to deliver the merchandise.
## 3 Results
### Sensors Comparison  
To check the effect of shadow on climatic data collection, several sensors were compared: the weather station and three locations (Figure 1) with multiple sensors (Figure 2). It must be noted that the weather station records air temperature, whereas the two air-quality measurement devices record the temperature of the device itself, as, at this time, the sensor is not shielded. Therefore, it can be said that the air-quality sensors show the global temperature values (Equation (4)). The air-quality measurement devices are in shadow to differing levels, and this can be observed in Figure 8: at 8:00 a.m., the temperature of the 4815 device increases more than the reference temperature of the weather station. The same happens later, around 2:00 p.m. for the other device, 4816. These sensors are positioned in location 1 and 2 in Figure 1, respectively.  
We extracted the predicted mean vote comfort index every 15 min for the 27 th of July 2022 (Figure 9) to compare the relative relation of comfort values with recorded temperature values. The specific day was chosen from days belonging to the hot season (summer) and non-rainy days. A day with high temperatures and strong illumination provides a more variable daily comfort index and can thus help discriminate sensor and model behavior. It is expected that the shadow-casting prediction from the 3D model will mirror the temporal distribution of Figure 8. As matter of fact, we can see in Figure 9 that the two sensors predict the same comfort index around 14:00 (2:00 PM) which matches the distribution that we see in Figure 8.  
A final cross-validation was performed directly using the solar radiation from pyranometers and the shade fraction from ray-casting illumination, at the coordinates of the pyranometers, using the 3D model. Figure 10 below shows how the values measured by the pyranometers every 15 min and the illumination fraction modelled at identical timestamps are highly correlated. A quantification by a simple linear model provides a coefficient of determination (R-squared) of 0.83 and 0.86 respectively for sensor 4815 and 4816.  
Figure 8: Hourly statistics over the period from 1 June to 27 July for the weather station and the air-quality sensors.
## 3 Results and Discussions  
### Judgment of Hazardous Solid Waste Properties of Petrochemical Excess Sludge  
#### 3.1.1 Risk Analysis of Petrochemical Excess Sludge Leaching  
Leaching Toxicity of Heavy Metal Elements in Petrochemical Excess Sludge  
The determined values of inorganic elements in the leachate after the leaching experiments of Ah, Bl and Cq wet sludge having a water content of 85% are shown in Table 1. It can be seen from Table 1 that the measured values of Cu, Zn, Pb, Cd, Be, Ni, Ag, Ba, Hg, As, and Cr\({}^{+6}\) of Ah, Bl, and Cq wet sludge are far below the limit value, indicating that the Ah, Bl, and Cq sludge samples do not have the leaching toxicity of these inorganic elements.  
Leaching Toxicity Analysis of Organic Matter in Petrochemical Excess Sludge  
The measured values of non-volatile and volatile organic compounds in the petrochemical excess sludge leachate are shown in Tables 2 and 3, respectively. It can be seen from Tables 2 and 3 that the non-volatile organic compounds and volatile organic compounds in the leaching solution of Ah, Bl, and Cq excess sludge samples were not detected except for the dibutyl phthalate and carbon tetrachloride of the Bl sample. It was detected that the content of non-volatile organic matter and volatile organic compounds in all sample leachates were below the limit.  
From analysis of leaching toxicity of inorganic elements and organic compounds in petrochemical excess sludge, it was found that all the measured substances did not exceed their limit values. This may be attributed to the current management measures for sewage integration in various refining and chemical enterprises. The sewage that is incorporated into the sewage treatment plant needs to be evaluated and analyzed in advance, in which those with high toxic content are not allowed to be incorporated into the sewage treatment plant pipe network and needs to be isolated for specific disposal.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multicolumn{2}{c}{**Leaching Concentration (mg/L)**} & \multirow{2}{*}{**Limit Value [34]**} \\ \cline{2-2} \cline{4-4}  & **Ah** & & **Bl** & **Cq** \\ \hline Cu & 0.0348 & 0.0081 & 0.0130 & 100 \\ Zn & 0.162 & 0.080 & 1.020 & 100 \\ Pb & 0.009 & 0.004 & 0.003 & 5 \\ Cd & 0.00006 & 0.0011 & 0.0008 & 1 \\ Be & N.D. & N.D. & 0.0002 & 0.02 \\ Ni & 0.0867 & 0.0160 & 0.1021 & 5 \\ Ag & 0.0032 & 0.0005 & 0.0016 & 5 \\ Ba & 0.066 & 0.339 & 2.618 & 100 \\ Cr\({}^{+6}\) & 0.004 & N.D. & N.D. & 5 \\ Hg & N.D. & N.D. & N.D. & 0.1 \\ As & 0.0011 & 0.0009 & 0.0026 & 5 \\ \hline \hline \end{tabular} Note: N.D.: not detected, below detection limit.  
\end{table}
Table 1: Determination of inorganic elements in excess sludge leachate.
# Borrow from Source Models: Efficient Infrared Object Detection with Limited Examples
## 1 Introduction
Despite the fact that an RGB model is designed for visual images, it still can detect most infrared targets when given a thermal image. However, the precise categories and bounding boxes are hard to be predicted by it due to the difference between two domains. Therefore, we combine all category predictions as a foreground soft label, which is regarded as the source knowledge to be transferred. Then, we look for where to add the source supervision. Different from ground-truth supervision on the final DH, we propose a Background Suppression Module (BSM) to receive the source knowledge. BSM is inserted after FEN to enhance the feature maps and produce a foreground prediction at the same time. By calculating the transfer loss between the foreground prediction and the soft label, we introduce source supervision into the training process of the infrared detector, as shown in Figure 1.  
Theoretically, our transfer approach SMG can be implemented in any visual detection networks effortlessly. In this paper, we choose two popular frameworks, CenterNet [19] and YOLOv3 [18], as instantiations, and the frameworks we proposed are named SMG-C and SMG-Y, respectively. To validate the performance of SMG, we conduct extensive experiments on two infrared benchmarks, FLIR [23] and Infrared Aerial Target (IAT) [15]. Experimental results show that SMG is an effective method to boost detection accuracy especially when there are limited training examples. On FLIR, using only a quarter of training data, SMG-Y obtains higher mAP than the original YOLOv3 finetuned on the entire dataset. Furthermore, compared to other infrared detectors, both SMG-C and SMG-Y achieve state-of-the-art accuracy and inference speed. The main contributions are described as the following three folds:  
* First, we propose a cross-domain transfer approach SMG, which easily converts a visual RGB detection framework to an infrared detector. * Second, SMG decreases the data dependency for an infrared network. The detectors with SMG maintain remarkable performance even if trained on the small-scale datasets. * Third, two proposed instantiations of SMG, SMG-C and SMG-Y, outperform other advanced approaches in accuracy and speed, showing that SMG is a preferable strategy for infrared detection. The structure of this paper is as follows. In Section 2, we briefly present some aspects related to our work. Section 3 shows the proposed method SMG in detail. Extensive experiments and ablation studies are conducted in Sections 4 and 5, respectively. We explain why SMG works well and analyze the failure cases of our detectors in Section 6. Finally, the summary is drawn in Section 7.