esa-sceva/satcom-chunk-collection huggingface dataset:
# Short-Term Deformability of Three-Dimensional Printable EVA-Modified Cementitious Mortars
## 5 Conclusions  
EVA powder is easier to handle onsite than liquid polymer (i.e., latex or emulsion types) with regards to pre-mixing. Hence, EVA was selected as the admixture used to produce the 3D-printable EVA-modified cementitious mortars examined here. This research is a follow-up study to previous research published by the author, entitled Fresh Properties of EVA-Modified Cemertitious Mixtures for use in Additive Construction by Extrusion. The present study experimentally examined  
Figure 14: Relationship between the EVA/cement ratio and coefficient of thermal expansion.  
Figure 13: Thermal strains for different EVA/cement ratios.  
the short-term deformability of EVA-modified cementitious mortar for extrusion-based additive construction, especially with regards to the elastic modulus, drying shrinkage, and thermal expansion. These three factors are critical for determining the properties of hardened materials used in additive construction. The major findings of this study are as follows.  
As the EVA/cement ratio was increased, the compressive strength and elastic modulus tended to decrease but the maximum compressive strain increased. Drying shrinkage began one hour after the mortar was placed, was most significant in the 4-11 h range, and continued to increase until 28 days of aging. At 28 days, the drying shrinkage tended to increase as the EVA/cement ratio was increased. The thermal expansion coefficient also tended to increase as the EVA/cement ratio was increased. A correlation analysis indicated that each of the three factors (i.e., elastic modulus, drying shrinkage, and thermal expansion) had a high determination coefficient (r\({}^{2}\)) with regards to the EVA/cement ratio.  
Of the three factors described above, drying shrinkage and thermal expansion increased as the EVA/cement ratio was increased, indicating that the materials concerned were disadvantageous in terms of dimensional stability. At the same time, the elastic modulus decreased; thus, the level of stress caused by the other two factors may also have been reduced, thereby improving the degree of extensibility and offsetting any adverse impact. That being said, in order to increase the elastic modulus while reducing the degree of drying shrinkage and thermal expansion, it is still desirable to reduce the water/cement ratio by reducing the EVA/cement ratio.  
The author is very grateful to former collaborators [PERSON] and [PERSON] from Korea Conformity Laboratories and Kangwon National University for the assistance in conducting the experiment.  
The author declares no conflicts interest.
## 3 Methods
### Probability estimator for ranking of correlated positions  
Considering correlated mutations of pairs of residues at different positions involves a straightforward generalization of the scheme described above. It is in fact sufficient to replace the event \"amino acid \(\alpha\) occurs at position \(p\)\" with \"amino acid \(\alpha\) occurs at position \(p\) and amino acid \(\beta\) occurs at position \(q\)\". Eq. (2) then becomes  
\[P_{pq}(\alpha,\beta,i)\approx\tilde{P}_{pq}(\alpha,\beta,i)\triangleq\sum_{ \gamma,\delta}f_{pq}(\gamma,\delta,i)m^{(2)}[(\gamma,\delta)\rightarrow( \alpha,\beta)] \tag{6}\]  
and the mutual information related to the pair position \(\{p,q\}\) can be calculated as  
\[I_{pq}^{(2)}=\sum_{i=1}^{N}\sum_{\alpha=1}^{20,24}\sum_{\beta=1}^{20,24}\tilde {P}_{pq}(\alpha,\beta,i)\text{ln}\frac{\tilde{P}_{pq}(\alpha,\beta,i)}{\tilde {P}_{pq}(\alpha,\beta)f(i)} \tag{7}\]  
To reduce the impact of marginal mutual information contributions to the pair position score, we maximize the following expression:  
\[I_{pq}^{(2)}-I_{p}-I_{q}. \tag{8}\]  
This definition is very closely related to the one of the \"triple mutual information\" [16] or \"mutual interaction\" [17].  
We then define the \"pair substitution probability\" as:  
\[m^{(2)}[(\gamma,\delta)\rightarrow(\alpha,\beta)]=m(\gamma\rightarrow\alpha)m (\delta\rightarrow\beta) \tag{9}\]  
This choice, beside being the simplest, can be justified as follows: the joint probability of finding residue \(\alpha\) at position \(p\) and residue \(\beta\) at position \(q\) in group \(i\) can be written as  
\[P_{pq}(\alpha,\beta,i)=P_{p}(\alpha)P_{q}((\beta,i)|\alpha_{p}) \tag{10}\]where \(P_{q}((\beta,i)|\alpha_{p})\) is the conditional probability of finding, in group \(i\), \(\beta\) at \(q\) given that \(\alpha\) was in \(p\). Both probabilities on the right hand side of the equation above can be written in analogy with the definitions introduced previously:  
\[P_{p}(\alpha)\approx\tilde{P}_{p}(\alpha) \triangleq \sum_{\delta}f_{p}(\delta)m(\delta\rightarrow\alpha) \tag{11}\] \[P_{q}((\beta,i)|\alpha_{p})\approx\tilde{P}_{q}((\beta,i)|\alpha _{p}) \triangleq \sum_{\gamma}f_{q}((\gamma,i)|\delta_{p})m(\gamma\rightarrow\beta)\]  
(The use of Henikoff-like matrix element in the definitions above is justified since each one of them refers to single amino acid counts.) Finally, noting that the sample estimate of eq. (10) also leads to  
\[f_{pq}(\delta,\gamma,i)\approx f_{p}(\delta)f_{q}((\gamma,i)|\delta_{p}) \tag{12}\]  
we found the choice of eq. (9) consistent and reasonable.
# An Enhanced System Architecture for Optimized Demand Side Management in Smart Grid
## 3 Proposed CHEMA and its Implementation
### Load Categorization  
Home appliances are categorized with respect to different aspects in literature [21]. Generally, there are three major categories of home appliances: essential load or baseline load, regular or continuously running load and burst or schedulable loads. In CHEMA, load has been divided into the following categories and subcategories.  
Figure 2: CHEMA flow chart.  
The first category is of essential load which includes: lighting, fans and communication equipment only. Lighting can be divided into sub categories of off-peak lighting in such a way that peak lighting is at least up to half of the off-peak lighting.  
\[\chi=\begin{cases}\frac{1}{2}\chi_{OP}&for\ Peak\ Hours,\\ \chi_{OP}&Otherwise,\end{cases} \tag{1}\]  
where \(\chi\) shows the total lighting load and \(\chi_{OP}\) denotes off-peak lighting load. This load is partially added in the scheduling problem. Inclusion of essential loads on a half-basis as indicated in Equation (1), such as lighting load, is the enhancement in the optimization problem presented in [19]. It will minimize the cost more effectively with the control of the lighting in peak hours. Therefore, base line load is partially added into scheduling problem. In addition, in off-peak hours, the lighting is controlled on the basis of no. of persons present in the house. If the set of time slots is presented by \(T=\{T_{1},T_{2},...,T_{N}\}\) and \(T_{N}\) is the total no. of time slots and the set of persons is presented as \(P=\{P_{1},P_{2},...,P_{N}\}\), and \(P_{N}\) shows the maximum no. of persons in the house, then  
\[P=\begin{cases}P_{1}&\text{for}\ \forall\ \ T_{1}\leq t\leq T_{2}\\ P_{2}&\text{for}\ \forall\ \ T_{2}\leq t\leq T_{3}\\ P_{3}&\text{for}\ \forall\ \ T_{3}\leq t\leq T_{4}\end{cases}, \tag{2}\]  
where, \(T_{1},T_{2}\), _etc._ belong to \(T\). The second major category is of regular or continuous running loads which include: refrigerator, central heating/cooling, water heater _etc._ The third major category is of schedulable loads which include: water pump, electric iron, washing machine and dishwasher, _etc._ We are introducing flexible load categorization, which is explained with the help of the following scenarios:  
* HVAC temperatures are given a tolerance band yet maintain reasonable comfort of the user,
* Hot water consumption is controlled with PPC, _i.e._, on the basis of persons present at home.
## 4 Example Verification
### Experimental Data Collection  
This study used a hybrid data collection methodology combining online web scraping and offline field acquisition. For online sourcing, image data were gathered using search keywords such as \"safety helmet\", \"reflective vest\", and \"water conservancy project construction\". A program was written to collect images through web scraping techniques. Offline data collection involved on-site photographic documentation at a shuice gate reconstruction project, yielding 3621 high-resolution images captured under diverse lighting/occlusion conditions. In total, 22,141 datasets were collected, comprising 18,520 online images and 3621 on-site images. The collected data were then divided into a training set, a validation set, and a test set in an 8:1:1 ratio. The training set is shown in Figure 5, while the validation and test sets followed a similar structure.
## 2 Analysis of Antenna Pattern Errors
### Backlobe Antenna Pattern  
When FPASR is observing the Earth in orbit, the main lobes of the antenna points toward the Earth. The antennas capture not only the BT of the Earth directly in front of the array but also that of the cold sky entering through their backlobe. Due to the fact that the gain of the backlobe antenna pattern is lower than that of the main lobe, and the BT of the cold sky is significantly lower than that of the Earth, the contribution of the backlobe is very small in normal mode. However, since the cold sky provides a well-known and stable reference, FPASR can achieve in-orbit external calibration by observing the cold sky. In cold sky mode, the attitude of FPASR is different from the nominal mode. The main lobes of the antennas point towards the cold sky, while Earth is located within its backlobe [12; 24]. At this time, the BT contribution of the backlobe cannot be ignored [25]. When the backlobe antenna pattern is considered, the visibility function expression is as follows:  
\[\begin{split}& V_{kj}^{pq}(u,v)=\iint\limits_{\xi^{2}+\eta^{2}\leq 1 }\frac{TF_{front}(\xi,\eta)+TF_{back}(\xi,\eta)}{\sqrt{\Omega_{i}\Omega_{k}} \sqrt{1-\xi^{2}-\eta^{2}}}e^{-j2\pi(u_{k}^{2}+v\eta)}d\xi d\eta\\ & TF_{front}(\xi,\eta)=T_{B}^{pq}(\xi,\eta)F_{nk}^{p}(\xi,\eta)F_ {nj}^{q*}(\xi,\eta)\\ & TF_{back}(\xi,\eta)=T_{B\_back}^{pq}(\xi,\eta)F_{back\_k}^{p}( \xi,\eta)F_{back\_j}^{q*}(\xi,\eta)\end{split} \tag{12}\]  
where \(T_{B\_back}^{pq}(\xi,\eta)\) is the BT entering the backlobe. \(F_{back\_k}^{p}(\xi,\eta)\) represents the backlobe antenna pattern at p polarization. \(F_{nj}^{q*}(\xi,\eta)\) is the conjugate of the backlobe antenna pattern at q polarization. The contribution of the backlobe is equivalent to adding an additive error to the visibility function.
## 5 Conclusions  
This paper proposes a temporal knowledge graph completion (TKGC) model that combines high-order connectivity between entities and attribute information. The model utilizes LSTM networks to extract rich semantic information from attribute values, providing additional supervision and addressing the problem of indistinguishable structural information for entities and relations with different meanings. Additionally, it employs graph attention networks to model high-order connectivity between entities, enhancing the model's ability to learn complex relationships between entities, implicit dependencies between events, and constraints. Experimental results on the GDELT and ICEWS demonstrate that the proposed HCAE model performs well in terms of mean reciprocal rank (MRR) and Hits@n metrics, outperforming other baseline models. It is particularly suitable for addressing complex temporal evolution tasks involving unknown entities or relations. The model also holds significant value for practical engineering tasks. However, compared with other models, HCAE has a larger number of parameters and computational efficiency limitations. Future work will focus on designing a more lightweight yet powerful model and improving its applicability in real-world engineering tasks.  
Conceptualization, M.W.; methodology, M.W.; software, H.M.; validation, M.W., H.M., W.W. and X.Z.; formal analysis, X.Z.; investigation, M.W.; resources, M.W.; data curation, M.W.; writing--original draft preparation, M.W.; writing--review and editing, H.M.; visualization, M.W.; supervision, W.W.; project administration, M.W.; funding acquisition, H.M. 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 (grant number No. 62273170), the Liaoning Education Department Scientific Research Project (grant number No. JZL202015404, grant number No. LJKZ0625), the General project of Liaoning Provincial Department of Education (grant number No. LJKZ0618).  
Not applicable.  
Figure 8: Effects of different \(\lambda_{3}\) on HCAE performance.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Machine Learning Models for Predicting Mortality in Hemodialysis Patients: A Systematic Review
## 3 Results
### Outcomes Measure Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Study** & **Follow-Up** & **ML. Model** & **True Positive** & **False Positive** & **True Negative** & **False Negative** & **Total** & **Patients Died** \\ \hline \multirow{8}{*}{Sheng 2020 [30]} & \multirow{8}{*}{1 year} & Adaptive Boosting & S25 & 350 & 4720 & 223 & 5828 & 764 \\ \cline{3-8}  & & \multicolumn{2}{c}{Decision Tree} & 466 & 117 & 4954 & 291 & 5628 & 764 \\ \cline{3-8}  & & \multicolumn{2}{c}{General Boosting} & S25 & 116 & 4954 & 223 & 5628 & 764 \\ \cline{3-8}  & & \multicolumn{2}{c}{K-Noured Neighbor} & 408 & 58 & 5012 & 350 & 5628 & 764 \\ \cline{3-8}  & & \multicolumn{2}{c}{Light Gradient Boosting} & 583 & 175 & 4895 & 175 & 5628 & 764 \\ \cline{3-8}  & & \multicolumn{2}{c}{Logistic Regression} & 641 & 3147 & 1923 & 117 & 5628 & 764 \\ \cline{3-8}  & & \multicolumn{2}{c}{Random Forest} & 525 & 233 & 4837 & 223 & 5628 & 764 \\ \cline{3-8}  & & \multicolumn{2}{c}{XColoset} & 583 & 175 & 4895 & 175 & 5628 & 764 \\ \hline \multirow{8}{*}{Lee 2023 [32]} & \multirow{8}{*}{1 year} & Logistic Regression & 0 & 23 & 691 & 46 & 760 & 42 \\ \cline{3-8}  & & \multicolumn{2}{c}{Decision Tree} & 8 & 15 & 699 & 38 & 760 & 42 \\ \cline{3-8}  & & \multicolumn{2}{c}{Random Forest} & 0 & 0 & 714 & 46 & 760 & 42 \\ \cline{3-8}  & & \multicolumn{2}{c}{Gradient Boosting} & 0 & 0 & 714 & 46 & 760 & 42 \\ \cline{3-8}  & & \multicolumn{2}{c}{XColoset} & 0 & 23 & 692 & 45 & 760 & 42 \\ \cline{3-8}  & & \multicolumn{2}{c}{Logistic Regression} & 33 & 47 & 473 & 113 & 666 & 147 \\ \cline{3-8}  & & \multicolumn{2}{c}{Random Forest} & 13 & 7 & 513 & 133 & 666 & 147 \\ \cline{3-8}  & & \multicolumn{2}{c}{Gradient Boosting} & 27 & 13 & 506 & 120 & 666 & 147 \\ \hline \multirow{8}{*}{[PERSON] 2012 [54]} & \multirow{8}{*}{7-12 months} & Logistic Regression & 93 & 535 & 1651 & 47 & 2326 & 133 \\ \cline{3-8}  & & \multicolumn{2}{c}{13-18 months} & Logistic Regression & 75 & 448 & 37 & 1306 & 1866 & 121 \\ \cline{3-8}  & & \multicolumn{2}{c}{19-24 months} & Logistic Regression & 46 & 335 & 30 & 1113 & 1534 & 80 \\ \hline Rankin 2022 [55] & 3 months & XGBout & 6024 & 22,134 & 84,124 & 2541 & 114,623 & 86,083 \\ \hline \multirow{8}{*}{[PERSON] 2021 [77]} & \multirow{8}{*}{2.29 years} & Decision Tree & 307 & 145 & 300 & 145 & 857 & 408 \\ \cline{3-8}  & & \multicolumn{2}{c}{Neural Network} & 241 & 128 & 317 & 171 & 887 & 408 \\ \cline{3-8}  & & \multicolumn{2}{c}{Support Vector Machine} & 274 & 137 & 309 & 137 & 887 & 408 \\ \cline{3-8}  & & \multicolumn{2}{c}{Logistic Regression} & 283 & 129 & 317 & 128 & 857 & 408 \\ \hline \end{tabular}
\end{table} 
Table 3: Prediction of positive and negative cases.
# SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks
## 2 Related Work
### CC Algorithm Based on the End-to-End Mode
#### 2.1.3 Hybrid CC Algorithms  
Hybrid congestion control algorithms refer to algorithms based on packet loss and delay. The most representative hybrid CC algorithms are the compound algorithm and BBR algorithm. Compound TCP [17] is a TCP congestion control algorithm implemented by Microsoft. By maintaining two congestion windows at the same time, compound TCP can achieve better performance in a long fat network without loss of fairness. CTCP maintains two congestion windows: a regular additive increase/multiplicative decrease (AIMD) window and a delay-based window, and the actual sliding window size used is the sum of these two windows.  
BBR is a congestion control algorithm proposed by Google in 2016 and is based on congestion. The BBR performs congestion control by measuring the bottleneck bandwidth (BtlBw) and round-trip transmission delay (RTprop), which are constantly changing over the life of a connection and must be continually estimated.  
At present, the most widely used end-to-end mode CC algorithms are CUBIC and BBR, of which the CUBIC algorithm is the default built-in algorithm of the current mainstream Linux system. The main problem of the CUBIC algorithm is congestion control based on packet loss signals. Once packet loss occurs in the link, the CUBIC algorithm will inevitably reduce the data transmission speed. In addition, packet loss usually occurs after router queue congestion, and the CUBIC algorithm often causes an increase in router queue delay. The BBR algorithm is currently widely used in YouTube's and Google's data center servers. The main problems of the BBR algorithm are unfairness in competition with other CC algorithm streams and unfairness in competition with BBR streams (large RTT streams occupy more bandwidth), as well as a slow bandwidth convergence speed [18; 19; 20].
## 2 Problem Description and Modeling
### Task Model  
A total of \(v\)_Applications_ is submitted by IoT devices, denoted as \(\mathbf{W}\). Each application \(w\in\mathbf{W}\) can be decomposed into several _tasks_ with data dependencies, represented by a directed acyclic graph (DAG) \(\mathcal{G}=(\mathcal{V},\,\mathcal{E})\), where node set \(\mathcal{V}\) represents tasks, and \(\mid\!\mathcal{V}\!\mid\) is the number of tasks in application \(w\). The set of directed arcs \(\mathcal{E}\) denotes data dependencies between tasks. Each arc \((m_{p},\,m_{j})\) in \(\mathcal{E}\), \(m_{p}\), \(m_{j}\in\mathcal{V}\), is weighted by \(d_{p,j}\), denoting that task \(m_{j}\) needs an intermediate result with amount \(d_{p,j}\) from \(m_{p}\). For task \(m_{p}\in\mathcal{V}\), let \(pre_{p}\) and \(succ_{p}\) be its predecessor and successor tasks, respectively. Task \(m_{p}\) can be executed only if all results of tasks in \(pre_{p}\) have been received. For completeness, we let \(d_{p}\) be the input data for entry task who has no predecessors, and each node \(m_{p}\in\mathcal{V}\) is labeled with its workload \(e_{p}\) (Kcycles/Byte). Furthermore, we assume that tasks can be executed with any satellites in an SEC network.  
Figure 3 shows the DAGs for two applications. Task \(m_{1}\), labeled as 1, has a workload 1 Kcycles/Byte. Moreover, since \(d_{1}=13\), it implies that 13 Gcycles (13 Mbyte \(\times\) 1 Kcycles/Byte = 13 Gcycles) are needed to compute task \(m_{1}\). The weight of arc \((m_{1},\,m_{2})\) is 7, indicating that \(m_{2}\) can only be executed after receiving a 7 Mbyte result from \(m_{1}\).  
Figure 3: DAGs for applications.
## 2 Materials and Methods
### Spectral Preprocessing
#### 2.3.2 DWT Denoising  
Discrete Wavelet Transform (DWT) achieves wavelet denoising by decomposing and filtering high-frequency noise layer by layer [21]. Data processed through DWT not only reduces noise interference but also enhances feature expression to a certain extent [39]. The steps of wavelet denoising include discrete wavelet decomposition, threshold processing, and wavelet reconstruction. Threshold processing involves filtering the detail coefficients decomposed at each layer, while wavelet reconstruction involves adding the approximate coefficients of the last layer and the detail coefficients processed by the threshold at each layer to reconstruct a one-dimensional signal. This study uses the Donoho denoising method [40], with the threshold formula shown in Equation (2).  
\[threshold=\sigma\sqrt{2 log_{e}M} \tag{2}\]  
where \(\sigma\) is the predicted standard deviation of the wavelet coefficients, and \(M\) is the total number of sample wavelet coefficients. In this study, the threshold is set to half of the threshold value, and the wavelet function used is db4.
## 4 Discussion
### Discussion on the Negative Aspects of Hand Gesture Recognition and Approaches for Improvement
#### 4.2.2 The Restrained Recognized Area for Hand Gestures  
This study finds that the majority of users who prefer the joystick control complain that the hand gesture recognition must restrain their gestural hands within the relative detected area of the helmet's cameras. If the users' hands move too far away or do not maintain a proper distance, their gestural commands might not be recognized or cause inaccurate commands. Thus, unfamiliar users need to spend time adjusting their hands' relative positions to fit the helmet's cameras' detection. In addition, if users do not maintain a certain distance with others on site, the recognition might detect another person's hand movements. Our idea is for the users to experiment with the hand gesture recognition in a place where no one else is present, but since each person could only use the system once in our study, multiple trainings were not possible. In reality, we believe that with ample training and practice, as well as providing users with a private space specifically for the system's use, they are capable of gradually getting used to the designed, recognized area. Otherwise, the detected zone of hand gesture recognition must be expanded in the future to ensure a better human-computer interaction.
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 6 Evaluation Metrics for Localization Techniques
### Anchor to Node Ratio  
Curtailing the number of anchor nodes is necessary to reduce the deployment cost and increase network lifetime. A large number of anchor nodes in the network use GPS for their location estimate, which is uneconomical and consumes more power. Ultimately, it reduces the lifetime of the network. Therefore, an appropriate proportion of anchor nodes and sensor nodes is crucial in the designing of a localization process. This measure is suitable to compute the trade-off between location precision and the proportion of the localized sensor nodes compared to the deployment cost. The localization algorithm should essentially target a minimal number of anchor nodes to attain the expected precision of an application.
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 5 Experimental Results  
This section outlines the experimental study conducted to evaluate the performance of the proposed 5G mmWave network technique. Furthermore, this section is separated into the following three subsections: research overview, comparative analysis, and simulation setup.