esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Approach to Identifying Crash Hotspot Intersections (CHIs) Using Spatial Weights Matrices
## 4 Results and Discussion
### Results
#### 4.1.2 The Results of Intersection Hotspot Analysis (Getis-Ord Gi*) of Spencer City
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Gid** & **Name** & **Num\_Crash** & **Nneighbors** & **Gi*ZScore** & \(P\)**Value** \\ \hline
940 & S Grand Ave\& S Grand Ave\&11 th St & & & \\  & SW\&11 th St SE & 32 & 45 & 11.275 & 0.000 \\
1095 & W 44 th St\& W 44 th St\& Highway Blvd & 32 & 11 & 11.218 & 0.000 \\
113 & W 18 th St\& Highway Blvd \& W 18 th St\& & & 27 & 97 & 9.363 & 0.000 \\
377 & W 4 th St\& Grand Ave\& Grand Ave\& & & & 26 & 98 & 9.315 & 0.000 \\
143 & Grand Ave\& W 8 th St\& E 8 th St\& & & 22 & 112 & 7.806 & 0.000 \\ Grand Ave & 1620 & 11 th St SW\&/2 th Ave SW\&/8\&11 th St SW & 22 & 89 & 7.594 & 0.000 \\
263 & Grand Ave\& E 7 th St\& Grand Ave\& & & & 20 & 112 & 7.161 & 0.000 \\ W 7 th St & & & & & & 20 & 112 & 7.161 & 0.000 \\
391 & 4 th Ave SW\&11 th St SW\&/4 th Ave SW\& & & & & 119 & 92 & 6.629 & 0.000 \\
11 th St SW & & & & & & 19 & 108 & 6.729 & 0.000 \\
728 & Grand Ave\& W 1 st St\& Grand Ave\& & & 17 & 83 & 6.002 & 0.000 \\
540 & Grand Ave\& W 1 st St\& Grand Ave\& & & 17 & 83 & 6.002 & 0.000 \\
11 th Ave SW\&13 th St SW\&11 th Ave & & & & & 11 th Ave SW\&11 th Ave & & 16 & 51 & 5.447 & 0.000 \\
1218 & W 11 th St\& Grand Ave\& Grand Ave\& Grand Ave\& & & & 15 & 120 & 5.230 & 0.000 \\
358 & E 11 th St & & & & & & 11 th St & 5. Grand Ave\& & 11 th St \\
925 & Grand Ave\&4 th St SE\& S Grand Ave\& & & & & 15 & 39 & 5.155 & 0.000 \\
1640 & Grand Ave\& E 3 rd St\& W 3 rd St\& & & 14 & 92 & 5.022 & 0.000 \\ Grand Ave & W 5 th St\& E 5 th St\& & & 12 & 103 & 4.308 & 0.000 \\
31 & Grand Ave & & & & & & 12 & 103 & 4.308 & 0.000 \\ Grand Ave & & & & & & 10 & 127 & 3.432 & 0.001 \\
882 & & E & & & & 10 & 89 & 3.558 & 0.000 \\ Grand Ave & & & & & 110 & 89 & 3.558 & 0.000 \\
995 & & & & & & 110 & 89 & 3.508 & 0.000 \\
1100 & 1 st Ave \& E 3 rd St\& E 3 rd St\&1 st Ave E & 10 & 89 & 3.508 & 0.000 \\
918 & & & & & & 111 & 2.642 & 0.008 \\
1153 & E 4 th St\&4 th Ave \& E 4 th St\&4 th Ave E & 8 & 79 & 2.670 & 0.008 \\
1515 & 4 th Ave \& E \& E 8 th St\&4 th Ave E \& E 8 th St & 8 & 114 & 2.680 & 0.007 \\
1538 & 3 rd Ave \& E 6 th St\&3 rd Ave \& E 5 th St\& & 111 & 2.696 & 0.007 \\
1561 & 2 nd Ave & & & & & 111 & 2.696 & 0.007 \\
1561 & 2 nd Ave & & & & & 111 & 2.697 & 0.008 \\
1570 & & & & & & & 111 & 2.248 & 0.025 \\
1585 & 3 rd Ave \& E 11 th St\&3 rd Ave \& E 10 th St\& & & 111 & 2.697 & 0.038 \\
1595 & 3 rd Ave \& E 11 th St\&3 rd Ave \& E 10 th St\& & & 114 & 2.081 & 0.037 \\
1602 & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Gi*ZScores and \(p\)-values of intersections in CHIs. Figure 11: Distribution of crash hotspot intersections in Spencer city. Figure 10: Linear regression between the number of crashes and the Gi*ZScore.
## 5 Conclusions  
From an ecological and geographical perspective, this study combined the NPP value of seismic areas obtained by the CASA model with the spatial distribution data of geobhazards collected by field surveys to explore the effect of seismic intensity on vegetation destruction. The conclusions are as follows:  
1. The NPP value of more than 70% area was 90-150 gC/m\({}^{2}\) in July. In August, the value showed an overall decrease, and the areas with lower values became larger; the NPP value of most areas was 60-150 gC/m\({}^{2}\).
2. \(\mathrm{NPP_{max}}\) and \(\mathrm{NPP_{mean}}\) were 151.5-261.9 gC/m\({}^{2}\) and 54.6-116.3 gC/m\({}^{2}\), respectively, in July, August and September from 2015 to 2019. The vegetation gradually recovered after the earthquake. The NPP value showed an uptrend in the corresponding periods in 2018 and 2019. The integral decline amplitude decreased by years in comparison to that in 2015 and 2016. The decrease slowed down after the earthquake.
3. During the earthquake, compared with the NPP value in the same month in 2016, the value in areas affected by geohazards sharply declined by 27.2% (landslide), 22.4% (debris flow) and 15.7% (collapse). The vegetation in debris flow zones showed a stronger recovery, with a maximum increase of about 23.0% in September 2017.
4. In the aspect of the assessment system, the resilience index corresponding to the seismic intensity ranges one month after the earthquake was 0.642-0.693, and that of the whole study region was 0.671. The vulnerability index corresponding to the seismic intensity ranges was 0.470-0.669, and that of the whole study region was 0.473. The impact coefficient defined to represent the impact of the earthquake on NPP was 0.146-0.213.  
C.W.: Writing, Methodology, Software, Validation, Data Curation, Visualization; X.H.: Conceptualization, Methodology, Investigation, Data Curation, Funding acquisition; K.H.: Data Curation, Funding acquisition, Resources; S.L.: Data Curation, Funding acquisition, Investigation; W.Z.: Data Curation, Funding acquisition, Investigation. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the CRSRI Open Research Program (Grant No. SN CKWV2021888/KY), the National Natural Science Foundation of China (Grant No. 42001002), the Key Laboratory of Mountain Hazards and Earth Surface Processes, Chinese Academy of Sciences (Grant No. KLMHESP-20-02), the Key Programs of Science and Technology Research Plan, Hubei Provincial Department of Education (Grant No. D20201205), and the National Key R&D Program of China (Grant No. 2018 YFC1505205).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Frequency-Limited Model Reduction for Linear Positive Systems: A Successive Optimization Method
## 1 Introduction
For this purpose, we establish conditions for the existence of such a reduced-order system in the form of BMIs. To address this problem, we propose a successive convex optimization (SCO) algorithm that iteratively solves a series of convex optimization sub-problems, each of which is an inner convex approximation of the original non-convex constraint. At each iteration, the solution to the previous step is used as a starting point for the next iteration until the convergence is reached. To summarize, this paper presents three contributions:  
1. A finite-frequency specification is employed to characterize the approximation error, improving the model reduction capability within limited frequency intervals. 2. It is guaranteed that the reduced-order model maintains positivity, which retains the positive nature of the original system. 3. An inner convexity strategy and associated SCO algorithm are proposed to obtain a desired reduced-order model without any parametrization techniques. This paper is organized as follows: Section 2 introduces the problem statement and preliminary lemmas. Section 3 provides the conditions for the existence of a reduced-order model and a sequential algorithm for optimizing it. Section 4 gives an academic example to show the efficacy of the proposed method. Finally, Section 5 summarizes the contributions and discusses the limitations of the study and future work. **Notations:** The symbols \(\mathbb{R}\), \(\mathbb{R}^{n}\), and \(\mathbb{R}^{m\times n}\) represent the set of real numbers, \(n\)-dimensional column vectors, and \(m\times n\)-dimensional matrices, respectively. The notation \(\mathbb{R}^{n}_{+}\) refers to a set of column vectors with element-wise positive entries. The symbols \(\mathbf{I}\) and \(\mathbf{0}\) denote the identical and zero matrices, respectively. For \(M\in\mathbb{R}^{m\times m}\), \(M>\mathbf{0}\) (\(M<\mathbf{0}\)) means that \(M\) is positive-definite (negative-definite). \(\mathrm{He}\{M\}\triangleq M+M^{T}\), where \(M^{T}\) is the transpose of \(M\). Given a matrix \(S\in\mathbb{R}^{m\times n}\), we use \(S\succeq 0(S\succ 0)\) to signify that all the elements are non-negative (positive).
## 3 Results and Discussion
### Chemical Composition and Extractable Protein of Porcine Organs
#### 3.1.2 Extractable Protein  
The porcine organs contained significantly different quantities of extractable protein (Figure 1a). The highest amount was identified in the heart (262.7 mg/g dry matter), whereas the lung contained the lowest amount of extractable protein (155.1 mg/g dry matter) (\(p<0.05\)). Generally, the solubility and extractability of food proteins are related to intrinsic (protein content, amino acid composition/sequence, and modifications) and extrinsic factors (pH, ionic strength, temperature, and solvents), which influence the protein-protein and protein-water interactions [19]. Collagen, which is commonly found in the connective tissues of organs, is a highly stable macromolecule under neutral-pH and low-temperature conditions [20]. Thus, although there were no statistically significant differences in collagen content (Table 1), the relationship between the collagen content and the extractable protein indicates that collagen content could be one of the critical factors influencing the amount of protein that can be extracted from porcine organs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Trait** & **Heart** & **Kidney** & **Spleen** & **Liver** & **Lung** & **_p_ Value** \\ \hline Moisture (g/100 g) & 74.08 \(\pm\) 0.95 b & 72.91 \(\pm\) 0.34 b & 69.23 \(\pm\) 0.82 c & 70.37 \(\pm\) 0.45 c & 75.75 \(\pm\) 0.74 a & \(<\)0.001 \\ Protein (g/100 g) & 16.13 \(\pm\) 0.20 c & 15.90 \(\pm\) 0.40 c & 20.30 \(\pm\) 0.99 a & 19.72 \(\pm\) 0.42 a & 17.48 \(\pm\) 0.17 b & \(<\)0.001 \\ Fat (g/100 g) & 4.68 \(\pm\) 0.12 a & 4.19 \(\pm\) 0.04 b & 0.46 \(\pm\) 0.19 d & 1.15 \(\pm\) 0.11 c & 4.15 \(\pm\) 0.12 b & \(<\)0.001 \\ Ash (g/100 g) & 3.65 \(\pm\) 0.02 c & 3.77 \(\pm\) 0.01 b & 3.79 \(\pm\) 0.00 b & 4.41 \(\pm\) 0.06 a & 3.78 \(\pm\) 0.00 b & \(<\)0.001 \\ Collagen (g/100 g) & 0.88 \(\pm\) 0.21 & 1.09 \(\pm\) 0.14 & 1.07 \(\pm\) 0.11 & 1.06 \(\pm\) 0.01 & 1.57 \(\pm\) 0.41 & NS 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of porcine organ.
# PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring--A Comprehensive Review
## 4 Biological Signals for Wearable-Based HR
### PPG Signal derivatives  
The first and second derivatives of the PPG signal are shown in Figure 8. Time domain features related to peak amplitudes are marked. These features have been used in machine learning algorithms for blood pressure estimation in [51]. Analysis of the second derivative of the PPG signal allows one to identify characteristic features, such as peak latency, notch latency, notch relative amplitude, and peak-to-notch latency of the PPG signal. In [53], the authors have stated that these features are the best ones for the assessment of arterial stiffness.
## 3 Methodology
### System Design Methodology
#### 3.2.2 Multilevel Relational Model  
In multilevel relational databases, attribute values and tuples are generally treated as data objects with assigned security classifications. In the MLS/DBMS model, each attribute in the schema must be linked to a classification attribute (K), and every value within a tuple must be associated with its corresponding security classification. Additionally, certain models include a tuple classification attribute (TK) to assign an overall security level to each tuple.  
Therefore, a multilevel relational system is represented as \(R(A_{1},K_{1},A_{2},K_{2},\ldots,A_{n},K_{n},TK)\), where each A\({}_{i}\) is a data attribute, each \(K_{i}\) categorizes \(A_{i}\), and \(TK\) classifies the tuple. Relation instances consist of distinct tuples in the form \((a_{1},\ k_{1},\ a_{2},\ k_{2},\ \ldots,\ a_{n},\ k_{n},\ tk)\). The TK attribute indicates the highest classification within a tuple, while each \(K_{i}\) ensures the security classification of its respective attribute.  
The primary key in an MLS relationship is derived from attributes that form the primary key in single-level relations. Depending on their authorization levels, users  
Figure 3: The MLS data model for the illegal information exchange.  
(subjects) view different data in a multilevel relation. With a filtering method, a single tuple can be kept at a higher classification level, with corresponding tuples generated at lower levels when required. This relationship in the MLS/DBMS design assists developers in maintaining consistent categories within relations and selecting the most appropriate authentication restriction type.  
The model emphasizes authentication, so the process begins with creating an account, as illustrated in Figure 4. To clarify how the MLS/DBMS model operates, a flowchart can be created. The process starts by prompting the user to confirm their username and password. The flowchart then includes a decision point with the question: \"has an account?\", which leads to two possible paths:  
* No: If the user does not have an account yet, the process continues to the next step.
* Yes: If the user already has an account, the process skips to the step labeled \"Login\".  
After a successful login, the user proceeds to the step labeled \"Check the databases for existing accounts based on user accessibility\". However, the subsequent step appears redundant, as the system has already determined that the user has not created an account (as indicated in decision step 2, Figure 4). This redundancy may be a leftover from a previous version of the flowchart, potentially representing additional internal verification. The final box, labeled \"User Management\", indicates that the account was successfully created and that the user now has access to the user management system.
## 3 Results and Discussion  
### Preliminary Batch Mode Results  
Detailed analysis on the selected HMIs of this study was conducted by [PERSON] and [PERSON] [32; 33] in batch-mode configuration, consisting of a synthetic nitrate salt solution at 10 meq/L total concentration, acidified to a pH of 2 by concentration HNO\({}_{3}\) acid, with a zeolite dosage of 4 g per 100-mL HMI solution. The aqueous solution is agitated within a contact period 180 min by means of a triple-eccentric drive orbital shaker operating at 400 r/min set to 22\({}^{\circ}\)C. The five (5) HMIs were methodically combined in single-, dual-, triple-, and multi-component systems. Elemental analysis by ICP-AES concludes that after 3 contact h, a total HMI uptake of 0.0986 meq/g is achieved the multi-component system.  
Figure 2: Image of automated sorption system prototype design.  
The percent removal of Pb\({}^{2+}\), Cu\({}^{2+}\), Fe\({}^{3+}\), Ni\({}^{2+}\) and Zn\({}^{2+}\) are 94.0%, 21.9%, 56.2%, 9.10%, and 16.5%, respectively. The zeolite's preference among the HMIs is demonstrated by the selectivity series, which is established as Pb\({}^{2+}>>\) Fe\({}^{3+}>\) Cu\({}^{2+}>\) Zn\({}^{2+}>\) Ni\({}^{2+}\). One of the objectives of this current study is to investigate how these HMIs interact and affect the removal uptake in a continuous flow, dual-column settling.
# A Scalable Deep Network for Graph Clustering
## 4 Experiments
### Scalability Analysis
#### 4.4.1 Complexity Analysis  
In this paper, we assume that the dimension of the input data is \(d\) and the dimensions of each layer of the pre-train auto-encoder are \(d_{1},d_{2},d_{3},\cdots,d_{L}\). Assuming the number of input data is \(N\), the time complexity of the pre-train auto-encoder is \(\mathcal{O}(Nd^{2}d_{1}^{2}d_{2}^{2}\cdots d_{L}^{2})\). For the linear encoder in the Quasi-GNN module, the dimension used in this part must be the same as the pre-train auto-encoder, so the time complexity of the linear encoder is \(\mathcal{O}(Nd^{2}d_{1}^{2}d_{2}^{2}\cdots d_{L}^{2})\). In addition, the linear propagation module in the Quasi-GNN module requires an adjacency matrix to participate in the operation instead of parameters, so the time complexity of this part is related to the output dimension of the linear encoder and the number of nodes. Therefore, the time complexity is \(\mathcal{O}(NL_{p}d_{L}^{2}|\mathcal{V}|^{2})\). Moreover, we suppose that there are \(K\) classes in the clustering task, and the time complexity of Equation (17) is \(\mathcal{O}(NK+NlogN)\) according to the analysis of [PERSON] et al. (2019). In summary, the total time complexity of our proposed model is \(\mathcal{O}(Nd^{2}d_{1}^{2}d_{2}^{2}\cdots d_{L}^{2}+NL_{p}d_{L}^{2}|\mathcal{ V}|^{2}+NK+NlogN)\).  
Next, we analyze the space complexity of our proposed model. For neural networks, the space complexity is represented by the number of neural network layers and the number of parameters. The parameters needed in our model appear in the DNN module and the linear encoder in the Quasi-GNN module. For the encoder of the pre-train auto-encoder and the linear encoder in the Quasi-GNN module, to combine the embeddings of these two components, the dimensions of their weight matrix should correspond to each other. In addition, the decoder and the encoder are completely symmetrical. Therefore, the weight matrix size of these three components should be the same. The space complexity of \(\mathbf{W}_{c}\), \(\mathbf{W}_{d}\), and \(\mathbf{W}_{m}\) is \(\mathcal{O}(dd_{1}+d_{1}+d_{1}d_{2}+d_{2}+\cdots+d_{L-1}d_{L}+d_{L})\). To sum up, the space complexity of SDN should be \(\mathcal{O}(dd_{1}+d_{1}+d_{1}d_{2}+d_{2}+\cdots+d_{L-1}d_{L}+d_{L})\).
## 4 Raman Spectroscopy and Hepatitis Diagnosis
### Strategies Based on the Sample Type
#### 4.1.4 RNA/DNA Extracted from the Blood  
Nucleic-acid-based detection is popular in the clinical diagnosis of different diseases [85]. Nucleic acid is extracted from the blood of patients and compared with the DNA/RNA sequence of the suspected virus [86]. These tests are more specific and sensitive and provide quick results for single organisms and all categories of microbes. Extracted DNA/RNA is also used for hepatitis diagnosis, and some efforts have been made using Raman spectroscopy and extracted nucleic acids as the test samples. [PERSON] et al. presented a confocal Raman spectroscopic study of HBV DNA for the quantitative diagnosis of hepatitis B in 499 HBV-positive samples in 2020 [63]. The spectral features of HBV DNA were identified using a spectral range of 800-1700 cm\({}^{-1}\). A PLSR analysis was applied to quantitative modeling of the data, showing a correlation coefficient of 0.9673 and an RMSE of 0.4314 IU/L for the prediction set. Similarly, in the following year, [PERSON] et al. studied 27 extracted RNA samples of HCV for quantitative hepatitis C diagnosis. A PCA study was performed by classifying the samples into low-, medium-, and high-viral-load groups [26]. The PLSR model for quantitative viral load predictions was claimed to be 99% accurate.
# Central Heating Cost Optimization for Smart-Homes with Fuzzy Logic and a Multi-Agent Architecture
## 5 Results and Discussion
### Multi-Agent System  
One of the main contributions of this scientific work is the use of a MAS to coordinate different entities to achieve a common goal. The architecture proposed has shown to offer good performance in terms of communication between agents and the performance of every individual agent. The communication behavior between agents allows for the establishment of a good execution of the whole system. Thanks to the messages, the supervisor agent can control the whole system and it knows exactly when to act or send other messages. Furthermore, the MAS has shown that even if an agent fails, it can continue working with the rest of the agents without any problems. This is one of the main characteristics of MASs and the proposed system incorporates it.  
Additionally, the communications between agents are done quickly and no delays have been detected during the simulation. Hence, after observing the behavior of the agents, it can be noted that the proposed multi-agent architecture seems to work perfectly in these kinds of systems and that the idea mentioned in the proposal section is confirmed now, MASs are ideal for a distributed system that aims to achieve a common goal.  
To test the effectiveness of the proposed MAS, a small sensor network has been deployed, which interacts with the MAS. The sensor network has two Xiaomi NUN4019 TY (humidity and temperature sensor, Xiaomi Inc., Beijing, China) and two Garza Power 430041 (presence sensor, Garza, Fuenlabrada, Spain). We established the appropriate connections with them and ran the fuzzy algorithm with the measured values of the sensors. To obtain the number of persons inside the house/building, an algorithm was deployed to interpret the presence's sensors data. The MAS and the strategy defined to obtain the data from the sensors have demonstrated that it works perfectly in a small sensor network's environment; this type of environment is the normal setting in homes and flats.
## 5 Results
### Consensus Gesture Set for HAR Interactions  
We isolated 891 preferred gestures (33 participants \(\times\) 27 tasks) from the original 2673 gestures. 57 unique gestures were used to perform 891 preferred gestures for the 27 tasks. To create a user-preferred gesture set, we picked the gestures which achieved the highest consensus for each task. This led us to have 13 unique gestures performed for 27 tasks, which represented the 599/891 gestures or 67.23% of the user-preferred gestures (see Figure 7).  
We asked our participants to rate their preferred gesture proposal with numbers from 1 (very poor fit) to 7 (very good fit) to denote their confidence in the usability of their preferred gestures in two categories. We compared the subjective rating for social acceptance and comfortable to use between the user-preferred gesture set and the discarded set. We found that the average scores for gestures that are socially acceptable for the given tasks were 6.019 (\(SD=0.992\)) and 4.830 (\(SD=1.603\)), and average scores for being comfortable to use with our proposed design were 6.362 (\(SD=0.366\)) and 5.612 (\(SD=0.844\)), respectively. Independent \(t\)-tests confirmed that the user-preferred set was rated significantly higher than the discarded set in all two factors of social acceptance (\(p=0.023\)) and comfortable to use (\(p=0.005\)). Therefore, gestures in the user-preferred set are more suitable for HAR interactions in dynamic outdoor environments than the discarded one in terms of social acceptance and comfortable to use.  
Figure 7: User-preferred gesture set for HAR interactions. Simple touch gestures: (**a**) tap button one, (**b**) tap button two, (**c**) tap button three, (**d**) hold button two, (**e**) hold button three; Simple wrist gestures: (**f**) downward flexion, (**g**) upward extension, (**h**) leftward flexion, (**i**) rightward extension; Complex gestures: (**j**) hold button one and downward flexion, (**k**) hold button two and downward flexion, (**l**) hold button two and leftward flexion, (**m**) hold button two and rightward extension.