esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Properties and Microstructure of Polyvinyl Alcohol (PVA) Modified Cement Mortar
## 4 Conclusions  
This study investigated the strength development of cement mortars at the early age (3-28 days) after loading various polyvinyl alcohol (PVA) contents to obtain an optimum proportion. Then, the microstructure and morphology of PVA-modified cement mortar were studied by using DSC and FTIR to reveal its working mechanism. According to the test results in this study, the main achievements were:  
1. PVA has a strong cohesiveness and water retention, and its incorporation reduces the fluidity of cement mortar. The incorporation of 0.2%, 0.6%, 1.0%, and 2.0% PVA reduced the slump value by 13.9%, 31.5%, 41.1%, and 50.5% as compared to the control one, respectively.
2. The mechanical properties of cement mortar show a significant increase at first and then decrease with the increase of PVA content. Cement mortar containing 0.6% PVA has the highest compressive strength, its 28-day compressive strength is 12.1% higher than that of the control one. The incorporating 1.0% PVA increased the flexural strength greatly, its 28-day flexural strength is about 24.8% higher than that of the control one.  
Figure 13: SEM image of cement mortar with 2.0% PVA content at 28 days.  
3. The bulk density and water absorption test results show that the incorporation of 0.6% PVA increases the bulk density of cement mortar by 5.90%, and the corresponding capillary water absorption decreases by 60% as compared to the control mortar, respectively.
4. SEM tests show that three-dimensional PVA networks are formed in cement mortar. The crack-bridging effect of PVA film can be observed in the SEM images. When 2.0% PVA was incorporated, PVA formed heavy films, coating cement particles and preventing them from hydration.
5. DCS and FTIR analyses results manifest the adding of 0.6% PVA accelerates the hydration of the cement matrix, while the incorporation of 2.0% PVA impacts the hydration rate of cement in a negative way.  
Conceptualization, J.F. and G.L.; data curation, S.D. and Z.W.; writing of the original draft, J.F. and Z.W; writing of review and editing, G.L.  
This work is supported by the National Natural Science Foundation of PR China (No. 51708145, No. 51878299), the project of Natural Science Research Foundation of Guizhou Province (No. [2018]1064), and the Science and Technology Plan of Guangdong Province (2015A010105029). The authors are grateful for the above supporters.  
The authors declare no conflict of interest.
# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### What are the Main Problems Deep Learning Is Being Used to Solve?
#### 3.1.4 Device Location Prediction  
Unlike traditional networks, in telecommunication networks, the nodes are characterized by a high mobility; and determining or estimating their mobility behavior is a complex task. Device location prediction has many applications, such as location-based services, mobile access control, mobile multimedia quality of service (QoS) provision, as well as the resource management for mobile computation and storage [31].  
Considering urban scenarios, it is known that movement of people has a high degree of repetition, because they visit regular places in the city such as their own homes and places of work. These patterns can help to build services for specific places in order to increase user experience [32]. In addition, more detailed information about human mobility across the city can be collected using smartphones [33]. This information (combined with other data sources) can be used as input for models to estimate the device and consequently user location with high accuracy.  
In this systematic review, three articles presented models to deal with device location prediction. Two works focused on device location prediction in mmWave systems [34; 35]. In these systems, predicting the device location is a complex task due to the radiation reflected on most visible objects, which creates a rich multi path (interference) environment. In [34], a deep learning model was used to predict user location based on the radiation sent by the obstacles encountered. These carry latent information regarding their relative positions; while in [35], fingerprint historical data was used to estimate the device location over beamformed fingerprints.  
In [36], the authors proposed a deep learning model to predict the device location in ultra-dense networks. Predicting the device location in this scenario is important because the deployment of small cells inevitably leads to more frequent handovers, making the mobility process more challenging. The model was used to predict user mobility and anticipate the handover preparation. The model was designed to estimate the future position of an user based on her/his historical data. If a handover is estimated as being eminent, deep learning model was able to determine the best base station to receive the user.
# A Novel Adaptive Model Predictive Control Based Three-Phase Inverter Current Control Method
## 5 Conclusions  
In this paper, an A-MPC based current control for three-phase converters is proposed. This approach takes use of inverter mathematical model in two-phase rotating coordinate system to calculate the optimal switching state in each cycle. Comparing to existing FCS-MPC based inverter control methods, the proposed method reduces the computational burden and control complexity, and improves power quality. A parameter estimation and adaptive control method is also integrated into the proposed method. The overall control scheme is straightforward and simple to implement. Experimental results are presented to validate the improvement of dynamic performances. Comparing to conventional PI and PR control, the response speed of the A-MPC is faster and the overshoot is smaller. Future research aims to apply the method to other converter topologies, e.g., modular multi-level converters, etc.  
Conceptualization, M.L.; methodology, M.L. and L.M.; software, Z.Y.; validation, M.L., Y.Z. and D.C.; formal analysis, Z.Y.; investigation, L.M.; resources, M.L. and Z.Y.; data curation, M.L.; writing--original draft preparation, M.L. and L.M.; writing--review and editing, M.L.; visualization, Y.Z.; supervision, Y.W.; project administration, Y.W.; funding acquisition, Y.W.  
This research was funded by Qinghai Science and Technology Project \"Key technologies for renewable energy and energy storage integration applications\": 2019-GX-A9  
Figure 15: Experimental results for step current of the proposed A-MPC control method (**a**) real-time current waveform, (**b**) RMS current.
# Pose Estimation Utilizing a Gated Recurrent Unit Network for Visual Localization
## 3 Simulation
### Training and Testing  
To train the GRU network for predicting the yaw angle when a robot or a vehicle is in a cornering section, absolute values of the yaw angle obtained with ground truth data from RTK-GPS are used as the target values and absolute values of the yaw angle obtained from the MVO are taken as the input values. When the yaw angle of the robot or vehicle from RTK-GPS at time \(t\) exceeds a certain threshold, the robot or vehicle is considered to be moving around a cornering section. If the vehicle is judged to be at a cornering section, the 2 yaw angles obtained by the MVO before time \(t\) and another 2 yaw angles also obtained by the MVO after time \(t\) are taken to form 5 historical yaw angles around time \(t\). Note that the time \(t\) is according to the RTK-GPS. Therefore, the dataset can be organized as follows  
\[\begin{array}{l}\mathbb{X}=\{\mathbf{x}_{1},\mathbf{x}_{2},\mathbf{x}_{3}\ldots,\mathbf{x}_ {n}\},\mathbb{Y}=\{y_{1},y_{2},y_{3}\ldots,y_{n}\}\\ \mathbf{x}=\Big{(}\big{|}\psi_{MVO,t-2}\big{|},\big{|}\psi_{MVO,t-1}\big{|},\big{|} \psi_{MVO,t}\big{|},\big{|}\psi_{MVO,t+1}\big{|},\big{|}\psi_{MVO,t+2}\big{|} \Big{)},y=\Big{(}\big{|}\psi_{RTK-GPS,t+3}\big{|}\Big{)}\end{array} \tag{18}\]  
where \(\mathbb{X}\) is the set of feature vectors and \(\mathbb{Y}\) is the set of target values and \(\mathbf{x}\) is a feature vector consisting of yaw angles and \(y\) is a target value. The value for the sequential length is set to 5 after conducting ablation studies for the comparison of the final loss obtained for different values of the sequential length 3, 5, and 7. However, the sequential length can be adjusted depending on the characteristics of the system such as the frame rate of the camera. In this work, the threshold of the lower limit for \(\big{|}\psi_{t}\big{|}\) is set to 0.8 deg and, if the yaw angle exceeds 0.8 deg, the corners can be extracted as shown in Figure 9. This threshold must be set carefully as it may vary depending on the hardware constraints of the robot or vehicle and resolution and type of the sensor.  
The training set consists of 4811 training sequences obtained from the MVO and the ground truth of KITTI dataset consisting of 11 sequences with indices 00-10. Then, 80% of training sequences sare used for training of the GRU network and the remaining 20% are used for network validation (testing). The GRU network is trained using Adam optimizer [41]. The training of the GRU network is done similarly to regression. In other words, The GRU network is trained to reduce the loss \(\mathcal{L}=\frac{1}{n}\sum_{i=1}^{n}\left(f_{\theta}(\mathbf{x}_{i})-y_{i}\right)^ {2}\) where \(f_{\theta}(\mathbf{x}_{i})\) is the prediction of the GRU network. Both the output of the network and the ground truth yaw angle values are real numbers. The model at the epoch with the minimum value of the loss \(\mathcal{L}\) is chosen as the best model for the simulations in the Section 3.2. The minibatch size is set to 32 during training.
## 4 Experiment and Results
### Comparative Test of Different Occlusion Degrees  
In this section, we used the improved model to conduct comparative experiments on test sets A and B, respectively, among them, dataset A is the slightly occluded citrus fruit image, and dataset B is the heavily occluded citrus fruit image, the results as shown in Table 3. The recognition accuracy of the improved model for slightly occluded citrus fruit images is 96.01%, which is higher than that of the basic model by 0.57%, and the recognition accuracy of heavily occluded citrus fruit images is 89.41%, which is higher than that of the basic model by 2.55%. Experiments show that the model can improve the detection effect of citrus fruits under different occlusion degrees, which proves the effectiveness of the method proposed in this paper.
# Resilience Evaluation of Multi-Path Routing against Network Attacks and Failures
## 6 Conclusions  
We evaluated multi-path routing on MMP and compared MMP with SP and FMP on a global network. The experimental results show that when using MMP routing, the network performance is affected less by a bandwidth consumption attack than when using SP or FMP routing. In addition, when making a choice among paths under a multi-path situation, selecting more paths with a better network status than paths under an attack causes less effect on the performance of the network under attack.  
We verified that the effectiveness of multi-path with real networks deployed globally, especially MMP routing, shows more resilience than FMP used currently. Using the global network, we show that FMP mitigates certain cases of network problems, but MMP gains impressively lower loss rate by comparison.  
Conceptualization, H.A., Y.N., and H.L.; methodology, H.A. and Y.N.; software, Y.N. and A.P.; validation, H.A., H.L., and A.P.; formal analysis, H.A. and H.L.; investigation, H.A.; resources, H.A., Y.N., H.L., and A.P.; data curation, Y.N.; writing--original draft preparation, H.A. and Y.N.; writing--review and editing, H.A., H.L., and A.P.; visualization, H.A. and Y.N.; supervision, H.A. and H.L.; project administration, H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported in part by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2019-0-01343 Regional strategic Industry convergence security core talent training business, No.2019-0-01697 Development of automated vulnerability discovery technologies for blockchain platform security, and No.IITP-2021-2020-0-01819 ICT creative consilience program).  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Related Works
Although these related works discussed many interesting strategies and aspects, they are not sufficient to evaluate the goodness of SRE methodologies because:  
* Issue A: Evaluation criteria coverage. SRE methodologies aim at covering the whole SRE process (i.e., elicitation, evaluation and documentation) [22]. From our study, we noted that the majority of the evaluation studies except [29] lack a full emphasis on the whole SRE process [35]. While some works [21, 22, 31] concentrate on evaluating the extent of support to security requirements elicitation at earlier stages; some others [27] focus on evaluating the extent of support to documentation in terms of reusable patterns or modelling initiatives. * Issue B: Evaluation criteria affirmation. The evaluation criteria must result from a systematic process or standard method, such as [36], to facilitate reusability. However, in the majority of works, the proposed evaluation criteria were ad hoc and lack affirmation on why the criteria were good enough to be considered for the evaluation [28, 31, 32]. In this aspect only [29] can be noted as an exceptional case as the evaluation is based on the international standard [30]. * Issue C: Lack of stakeholders' involvement. This issue concerns the involvement of stakeholders' views while formulating the evaluation criteria. As highlighted in [37], the requirements process is a human endeavour, and so the requirements method or tool must be able to support the need for stakeholders to communicate their ideas and obtain feedback. In our context, stakeholders refer to the SRE methodology users (e.g., requirements engineers). However, from our observation we found that none of these comparative studies involved SRE experts while formulating the evaluation criteria for an SRE methodology. Moreover, almost all of the works acknowledge (either implicitly/explicitly) the significance of involving stakeholders' perspectives in the SRE process. For instance, [22] includes a criterion that concerns the integration of stakeholders' views to support multilateral security requirements analysis. Altogether, we can conclude that there exists no generic evaluation methodology that can help in identifying the best suitable SRE methodology. Evaluation of an SRE methodology is an enduring problem. As long as new SRE methodologies keep arriving, this necessity of evaluation persists. Having this in mind, we decided to develop a generic evaluation methodology independent from the SRE context of use so that it can be instantiated to varying SRE contexts (e.g., network security, software security).
# Quantitative Assessment of Upper- Limb Volume: Implications for Lymphedema Rehabilitation?
## 4 Methods for Volumetric Assessment  
### Water Displacement Method  
The water displacement method is currently considered the gold standard for the volumetric assessment of lymphedema [7]. It is based on Archimedes' principle, which states that the volume of an object can be determined by measuring the amount of water it displaces when immersed [36].  
In the volumetric assessment of lymphedema, the water displacement method involves submerging the affected limb in a water-filled container and measuring the amount of water displaced. The volume change can be determined by subtracting the initial water volume from the final volume after immersing the limb, providing an estimation of limb volume [36].  
The water displacement method offers several advantages in the assessment of upper-limb lymphedema. First, it is a relatively simple and cost-effective technique that does not require sophisticated equipment. It can be easily performed in a clinical or research setting with readily available materials. Additionally, if performed correctly, the water displacement method provides accurate measurements, allowing for the reliable tracking of limb volume changes over time [7; 37].  
However, it is important to acknowledge several limitations. In particular, water displacement requires complete limb immersion, which may be challenging for patients with mobility limitations or wounds [7; 38]. Additionally, the method relies on the accurate measurement of the water volume, which can be subject to human error [7; 38].  
Several studies have investigated the utility of the water displacement method in assessing limb volume in lymphedema, supporting its effectiveness in quantifying edema and monitoring the response to treatment interventions [8; 37]. However, it is worth noting that technological advancements have introduced alternative volumetric assessment methods that offer greater precision and convenience [9; 10; 38; 39; 40].
# Sparse Weighting for Pyramid Pooling-Based SAR Image Target Recognition
## 3 Contrast Experiment Results and Analysis  
### Dataset and Experimental Conditions  
This section presents the experimental results on the MSTAR [22] public database to assess the performance of the presented algorithm. Three types of vehicles are included in the dataset. They are BMP2 armored personnel carriers and BTR70 and T72 main battle tanks with depression angles at 17 degrees and 15 degrees. Every image in the dataset is 128 \(\times\) 128 pixels, covering the full 0-to-360 degree aspect range. The resolution of each image is 0.3 m \(\times\) 0.3 m. The training set contained the SAR target images whose depression was 17 degrees. The other images with a depression of 15 degrees were regarded as the testing set. The visible light images are illustrated in Figure 3. Table 1 lists the details of the training set and testing set.  
To reduce the negative interaction of the redundant background, a 64 \(\times\) 64 pixels sub-image was cropped from the center of every image. Moreover, the amplitude of the sub-image was normalized. Herein, the experiment result, which is reported, was the average classification result of running 10 times.  
In this experiment, the SIFT [14] was applied as the local feature. We extracted the SIFT features from patches densely located by six pixels on the image, under the scales of 16 \(\times\) 16. Furthermore, every SIFT descriptor had the dimension of 128.
## 2 Materials and Methods
### Test Methods  
#### 2.3.2 Nuclear Magnetic Resonance (NMR)  
The photo of NMR equipment is shown in Figure 3. When the porous medium was saturated, the NMR \(T_{2}\) spectrum could reflect its pore structure. Each peak represented the water signal within a certain range of pore size. The peak area represented the water content and the pore size in this range, as confirmed by many studies and practical applications [21]. The total transverse relaxation time in the porous media can be expressed by  
\[\frac{1}{T_{2}}=\frac{1}{T_{2B}}+\frac{1}{T_{2S}}+\frac{1}{T_{2D}} \tag{1}\]  
where \(T_{2}\) is the total relaxation time of the water in pores ascertained via the Carr-Purcell-Meiboom-Gill sequence, \(T_{2B}\) is the bulk fluid relaxation time, \(T_{2S}\) is the surface relaxation time, and \(T_{2D}\) is the diffusion relaxation time in a magnetic-field gradient. We assumed that the condition for the fast-diffusion regime was fulfilled and \(T_{2D}\) can be neglected [22; 23]. Given that the soil sample had been washed with distilled water, \(T_{2B}\) was negligible; thus, in Equation (1), \(T_{2}\) is dominated by \(T_{2S}\), i.e.,  
\[\frac{1}{T_{2}}\approx\frac{1}{T_{2S}} \tag{2}\]  
Theoretical analysis [24] has shown that \(T_{2}\) surface can be represented by  
\[\frac{1}{T_{2}}\approx\frac{1}{T_{2S}}=\rho_{2}\left(\frac{S}{V}\right)_{pore} =F_{\rm s}\frac{\rho_{2}}{r} \tag{3}\]  
where \(\rho_{2}\) is the surface relaxation coefficient, \(\left(\frac{S}{V}\right)_{pore}\) is the ratio of the surface area \(S\) to the pore volume \(V\), and \(r\) (\(\upmu\)m) is the pore radius. \(F_{\rm s}\) is a shape factor, which assumes a  
Figure 2: Flexible wall permeameter system.  
value of 1, 2, and 3 for planar, cylindrical, and spherical pores, respectively [25]. According [PERSON] et al. [26], pores in soils can be considered spherical.  
\[\frac{1}{T_{2}}=\rho_{2}\frac{3}{r} \tag{4}\]  
The Schlumberger-Doll research equation was adopted to calculate the surface relaxation coefficient (\(\rho_{2}\)), as expressed in Equation (5) [27].  
\[\rho_{2}=\left(\frac{K_{S}}{\phi^{4}T_{2 LM}^{2}}\right)^{1/2} \tag{5}\]  
where \(K_{\text{S}}\) (\(\upmu\)m\({}^{2}\)) is the soil permeability, \(\phi\) is the soil porosity, and \(T_{2\text{LM}}\) (\(ms\)) is the weighted geometric mean value of the \(T_{2}\) spectrum. Thus, the radius of the spherical pore \(r\) (\(\upmu\)m) in thawed soils can be calculated as expressed in Equation (6).  
\[r=3\rho_{2}T_{2} \tag{6}\]  
Then, the pore volume \(V_{\text{i}}\) with a certain radius is given as [28]  
\[V_{i}=\frac{A_{i}}{\Sigma A_{i}}\cdot\frac{m_{w}}{\rho_{w}}=\frac{A_{i}}{ \Sigma A_{i}}\cdot\frac{m_{s}-m_{d}}{\rho_{w}} \tag{7}\]  
where \(A_{\text{i}}\) is the amplitude of the corresponding signal in a \(T_{2}\) spectrum; \(m_{w}\) is the mass of pore water in the tested sample; \(\rho_{w}\) is the density of water, which is 1.0 g/cm\({}^{3}\) approximately; \(m_{s}\) is the total mass of saturated soil sample; and \(m_{d}\) is the mass of thoroughly dried soil sample after a dry-out treatment in an air oven.
## 4 Conclusions  
Accurate target segmentation and robust tracking are of great significance in substation intelligent inspection. A segmentation tracking algorithm based on pixel-level equalized memory matching network (PEMMN) is proposed. Experimental results show that the method exhibits excellent performance on both real substation inspection scenarios and three benchmark datasets. This work can not only help to ensure effective detection and localization of power equipment and improve the accuracy and effectiveness of inspection, but also help to reduce false alarms and missed inspections and alleviate manual intervention, thus improving work efficiency. Applying the target segmentation and tracking algorithm proposed in this paper to UAV inspection can realize accurate monitoring and tracking of power equipment from an aerial perspective and improve the coverage and efficiency of inspection. It can also be combined with artificial intelligence technology and big data analysis to realize intelligent fault diagnosis and prediction capability and improve the intelligent level of power inspection.  
**Author Contributions:** Conceptualization, H.Z. and B.Z.; methodology, B.Z.; software, B.Z. and Y.T.; validation, B.Z. and Y.T.; formal analysis, Z.L.; investigation, H.Z. and B.Z.; resources, H.Z. and B.Z.; data curation, Y.T. and Z.L.; writing--original draft preparation, B.Z.; writing--review and editing, H.Z.; visualization, B.Z. and Y.T.; supervision, H.Z. and Z.L.; project administration, H.Z.  
Figure 9: Tracking results of different tracking methods.  
and B.Z.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China under Grant (62272423, 62072416, 62006213, 62102373) and the Excellent Youth Science Foundation of Henan Province (2300421055).  
Data are contained within the article.  
The authors declare no conflicts of interest.