esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Design of Steel Moment-Resisting Frames with Damping Systems in Accordance with KBC 2016
## 4 Design Example
### Six-Story Steel Moment Frames with Viscoelastic Dampers (6F-SMRF-VED)  
#### 4.3.1 Initial Design of Seismic-Force-Resisting System  
A six-story steel moment-resisting frame is designed in this example. Velocity-dependent damping devices are added for seismic response reduction. The steel moment frame has five spans in the longitudinal direction, and three spans in the transverse direction. The building is assumed to be located in Seismic zone I of KBC 2016 and belong to seismic risk category 'Special' of which importance factor is 1.5. Site class was assumed to be \(S_{D}\). The overall design was performed under conditions similar to the example building with displacement-dependent damping systems. However, a special moment-resisting frame was adopted for the seismic-force-resisting system. The numerical model of the example building is represented in Figure 12.  
In the transverse direction, only two special moment-resisting frames placed at the outermost part of the building plan play a role of seismic-force-resisting system. Considering geometrical symmetry, only one moment-resisting frame is modeled in the example for simplicity. In addition, the \(P\)-\(\Delta\) effect due to gravity loads at the center of the plan was taken into account using the leaning column as shown in Figure 12c.  
The initial design of the special moment-resisting frame was performed for the minimum base shear with \(\eta=1.0\). The DCR of the initial design result is 0.88. Table 3 summarizes sections of members used in the designed frame. Since \(\eta\) assumed in the design equals 1.0, it is unnecessary to confirm whether a target base shear reduction factor is achieved. The damping devices are installed at the center span of the planar frame with braces and illustrated in Figure 12c. Thus, the frame in the central bay and the damping devices shown in Figure 12c comprises a damping system.  
Figure 12: Six-story steel moment-resisting frames with viscoelastic damping devices: (**a**) Isometric view, (**b**) plan, (**c**) elevation of internal frame for design example.  
From linear dynamic analysis based on response spectrum method with response modification factor and deflection amplification factor defined in KBC 2016, it was observed that the highest peak story drift was 1.75% and observed in the third and fourth stories. It is necessary to reduce the story drift by the damping device because it does not satisfy the allowable story drift of 1.0%.
## 4 Results
### Statistics and Analysis
In order to analyze the interrelation between the independent variables _html_score, og_score, _twitter_score and _schema_score, a [PERSON]'s r criterion was applied [35]. The results are shown in Table 9 where the correlations between the variables are depicted. All the correlations have significance level at 0.000 confirming the statistically significant correlation. The [PERSON]'s r coefficients range from 0.316 (the weaker positive correlation between html_score and twitter score) to 0.482 (the stronger positive correlation between og_score and twitter score). In an effort to examine the interrelationship between the collected Semantic Web metrics and a websites popularity the various websites were ranked according to their measured SWTI rating (variable _swti_rank) and the [PERSON]'s rank correlation coefficient was calculated. The results are presented in Table 10.  
Results of the Spearman correlation indicated that there is a significant very small positive relationship between _swti_rank and _alexa_rank and Y, (r(3630) = 0.0683, \(p<0.001\)). This correlation despite being very small prompted the researchers to further investigate the interrelationship between SW integration and popularity using a gradient boosting analysis which included every metric collected by the crawling algorithm. Figure 6: The histogram (**a**) and boxplot (**b**) of the _swti_green variable showing a non-normal distribution. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **html\_score** & **og\_score** & **twitter\_score** & **schema\_score** \\ \hline \multirow{3}{*}{**html\_score**} & **Pearson correlation** & \(1\) & 0.393 ** & 0.316 ** & 0.349 ** \\  & **sig.** & & 0.000 & 0.000 & 0.000 \\  & **N** & 3632 & 3632 & 3632 & 3632 \\ \hline \multirow{3}{*}{**og\_score**} & **Pearson correlation** & 0.393 ** & \(1\) & 0.482 ** & 0.422 ** \\  & **sig.** & 0.000 & & 0.000 & 0.000 \\  & **N** & 3632 & 3632 & 3632 & 3632 \\ \hline \multirow{3}{*}{**twitter\_score**} & **Pearson correlation** & 0.316 ** & 0.482 ** & 1 & 0.099 ** \\  & **sig.** & 0.000 & 0.000 & & 0.000 \\  & **N** & 3632 & 3632 & 3632 & 3632 \\ \hline \multirow{3}{*}{**schema\_score**} & **Pearson correlation** & 0.349 ** & 0.422 ** & 0.099 ** & 1 \\  & **sig.** & 0.000 & 0.000 & 0.000 & & \\ \cline{1-1}  & **N** & 3632 & 3632 & 3632 & 3632 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Pearson correlations between html_score, og_score, twitter_score and schema_score.
## 2 Results and Discussion  
### Morphologies of the Particles  
The transmission electron microscope (TEM) image of Fe\({}_{3}\)O\({}_{4}\) nanoparticles was shown in Figure 1a. It can be revealed that the average diameter of Fe\({}_{3}\)O\({}_{4}\) was about 10 nm with excellent dispersion property. According to the SEM pictures in Figure 1b,e, we can see that the diameter of microspheres has a narrow size distribution, and the size ranges from 200 to 300 \(\upmu\)m. It is clear that ethylenediamine-functionalized magnetic microspheres (EDA-MPMs) are good spherical shape from Figure 1b,c. In Figure 1d, it is obviously shown that the surface of the EDA-MPMs is primarily rough. Additionally, the surface area, pore volume and pore diameter of EDA-MPMs are 286 m\({}^{2}\)/g, 0.512 cm\({}^{3}\)/g and 6.8 nm by Brunauer-Emmett-Teller (BET) analyses, which have higher surface area than other adsorbents reported by other literature [28].
## 3 Problem Formulation
### Model Analysis
#### 3.3.1. Integrality Necessity of Binary and Integer Variables  
To improve the solving efficiency of the MIP model, we first investigate whether the binary variables \(x_{r}\) and integer variables \(n_{sr}\) can be relaxed. Through meticulous analysis, we establish Theorems 1 and 2.  
**Theorem 1**.: _The binary variables \(x_{r}\) cannot be relaxed to continuous ones as non-integer solutions will emerge and violate practical feasibility._  
**Theorem 2**.: _The integer variables \(n_{sr}\) cannot be relaxed to continuous ones, as this would lead to impractical fractional ship assignment._  
The proofs of Theorems 1 and 2 are presented in Appendix A.1.  
3.2. Totally Unimodular Property of the Coefficient Matrix for \(n_{s}^{\text{in}}\) and \(n_{s}^{\text{out}}\)  
A key strategy for efficiently solving large-scale optimization problems involves relaxing binary or integer variables into continuous ones without compromising the integrity of the solution. Within our proposed framework, the structure of constraints related to vessel chartering is of particular interest. By examining the mathematical properties of the corresponding coefficient matrix, we can identify conditions that permit such a relaxation, thereby enhancing the model's tractability while preserving solution validity.  
The TU property holds considerable significance in Integer Linear Programming (ILP). It enables the dropping of integer constraints without compromising optimality, thereby simplifying the solution process [51]. In practical scenarios, an ILP problem, which is generally NP-hard, can be solved efficiently through simpler LP techniques that work in polynomial time. This makes the TU property highly valuable in applications like network flows, scheduling, and resource allocation, where the underlying matrices often naturally possess this characteristic.  
We introduce the following theorem to formalize this property for the variables \(n_{s}^{\text{in}}\) and \(n_{s}^{\text{out}},\forall s\in S\).  
**Theorem 3**.: _For all \(sinS\), the coefficient matrices of \(n_{s}^{\text{in}}\) and \(n_{s}^{\text{out}}\) are TU._  
The proof of Theorem 3 is presented in Appendix A.2. Combining Theorem 3 with the fact that the right-hand-side terms of constraints (1-4) and (1-5) are always integral allows  
Figure 2: Illustration of Pareto frontier segments and gaps.  
for the relaxation of the integer variables \(n_{s}^{\text{in}}\) and \(n_{s}^{\text{out}}\) to be continuous. The integrality of the right-hand side is assured, as these terms are constructed solely from the integer variables \(n_{sr},\forall s\in S,\ \forall r\in R\) and the integer parameters \(A_{s},\ \forall s\in S\). The direct consequence of this relaxation is a significant reduction in the model's computational burden, achieved without compromising the optimality of the solution.
# CRISPR-Cas Systems: Prospects for Use in Medicine
## 4 CRISPR-Cas Implementation to Control Human Infectious Diseases
### Application of CRISPR-Cas in Diagnostics  
CRISPR-Cas can also be used to diagnose infectious diseases. Cas13 nuclease, which can cleave not only the target sequence, but also nearby off-target RNAs, was used for this purpose. The SHERLOCK (specific high sensitivity enzymatic reporter unlocking) platform has been developed for in vitro nucleic acid detection, based on this property of Cas13. It includes the Cas13a nuclease, sgRNA targeting specific RNA sequences, and fluorescent RNA reporters. After Cas13a cleaves the target RNA, it cuts off the reporter RNA, releasing a detectable fluorescent label [100; 101]. Viruses, pathogens and some specific mutations characteristic of tumor cells can be identified by this system.  
The DETECTR (DNA endonuclease-targeted CRISPR trans reporter) platform was created for the detection of nucleic acids, based on Cas12a nuclease, which has similar activity. DETECTR can be used to diagnose the HPV16 and HPV18 subtypes of human papillomavirus associated with cervical cancer [102].  
These detection methods look very promising, however commercial diagnostic kits are currently not available.
## 3 Material and Methodology
### Methodology  
This study utilizes the UrbanSound8K dataset to conduct urban sound classification, simulating a real-world urban sound recognition framework in the absence of real-time data. The proposed system is structured into three primary components: the End Device, the Fog Layer, and the Cloud Server (Figure 3). Initially, audio samples are processed using pre-recorded segments from the UrbanSound8K dataset, which are stored in WAV format to maintain consistency across the dataset. Several preprocessing techniques are applied on the End Device, including spectral gating for noise reduction, min-max normalization to standardize input data, and silence removal to isolate relevant sound patterns. Furthermore, MFCCs (mel-frequency cepstral coefficients) are extracted as key features to capture the spectral properties of the audio signals.  
Figure 3: Smart city sound detection architecture (adapted from [35]).  
Subsequent to the preprocessing stage, the audio data are locally classified on the End Device using three distinct deep learning models: CNN, LSTM, and Dense networks. Each model operates independently to produce classification results. The Fog Layer functions as an intermediary component, facilitating data transmission and reducing latency by ensuring efficient communication between the End Device and the Cloud Server. In this architecture, the Fog Layer supports a low-latency communication environment by minimizing the time required for data exchange, thereby simulating real-time conditions.  
The Cloud Server, deployed on AWS, is tasked with receiving and securely storing the classification results transmitted through the Fog Layer. This multi-layered architecture, comprising local data processing at the End Device, fog computing for optimized data transmission, and cloud computing for scalable storage, serves as a foundational framework for future real-time urban sound recognition systems.
## 2 Materials and Methods
### Model Development
#### 2.3.3 Support Vector Regression (SVR)  
The support vector machine (SVM)/support vector regression (SVR) has been lately utilized in multi-various fields for the prediction of quality characteristics. While support vector machines (SVMs) are a well-known method for classification, SVR is a lesser known, but effective, method for solving regression problems and estimating function with a supervised learning approach. Unlike SVM, where the output is a separate set, in this method, by estimating a multivariate function, the output is obtained with a continuous value [48]. Support vector machine is considered a non-parametric technique because it is based on nuclear functions. SVR estimates a continuous-valued function that encodes the fundamental interrelation between a given input and its corresponding output in the training data. This function then can be used to predict outputs for given inputs that were not included in the training set [49]. This algorithm has been used in cases such as face recognition, text classification, and bioinformatics. Learning algorithms include the following types: linear, quadratic, cubic, and gaussian [50]. One of the main advantages of using SVR is that it has very few adjustable parameters which in turn provides less effort and diminishes the need for experimentation for the purpose of finding appropriate architecture [51]. In this study, all data were used to train SVR algorithms. The architecture of the SVR model is presented in Figure 6.
## 5 Discussion
### Summary and Analysis of Overall Results  
The findings of this study indicate that the majority of students achieved a final accuracy rate close to 99%, demonstrating that the difficulty level of the system's questions was appropriately set to effectively differentiate student proficiencies without causing excessive frustration. Furthermore, students were able to achieve a stable and satisfactory performances regardless of their background in computer science, underscoring the strong instructional versatility of the system.  
Overall, students experienced a smooth learning curve and were able to sequentially master key concepts. The platform's fully online operation, which requires no additional hardware or software resources, enables both instructional experiments and learning assessments to be completed with ease, offering flexibility in implementation and significant potential for broader adoption.
# Shear Wave Velocity Estimation Based on Deep-Q Network
## 3 Theories of DRL and DQN  
DRL is used to solve a type of task that controls an agent to iteratively interact with the environment, and maximize future rewards. This task can be formulated as a finite Markov decision process [25] and be achieved by the DQN algorithm [13]. DQN is derived from Q-learning and can learn an optimal strategy by estimating the Q-value, which expresses the quality of executing an action \(a\) given a certain environment state \(s\). The Q-value can be iteratively updated by the following formula and converge to the optimum.  
\[Q(s,a)=Q(s,a)+\alpha(r+\gamma\text{max}_{a^{\prime}}Q(s+1,a^{\prime})-Q(s,a)) \tag{3}\]  
where \(Q(s,a)\) expresses the Q-value at the current environment state, \(\alpha\) is the learning rate, \(r\) is the current reward, \(\gamma\) is the discount factor, and \(\text{max}_{a^{\prime}}\,Q(s+1,a^{\prime})\) represents the maximum Q-value in the next environment state \(s+1\).  
The conventional Q-learning needs to create a Q-table for saving and updating the Q-value at each environment state, which can be intractable to build a table when the environment state number is huge. To mitigate this problem, the DQN algorithm utilizes a neural network instead of a Q-table for estimating the Q-value. The DQN algorithm introduces a replay memory \(D=\{ex_{1},\ldots,ex_{t}\}\) to save the agent's experience at different iterations, where \(ex_{t}=[s_{t},a_{t},r_{t},s_{t+1}]\) is the experience at \(t\) iteration. During the training stage, the DQN randomly selects a mini-batch from the replay memory for minimizing a loss function \(L\):  
\[L=(Q(s,a)-(r+\gamma\text{max}_{a^{\prime}}Q(s+1,a^{\prime})))^{2} \tag{4}\]  
where the meanings of symbols are the same as in Equation (3).  
Given a specific configuration of the environment and agent, the training process of DQN is expressed in Algorithm 1[13].  
```
1:Initializing the parameters of DQN and the replay memory \(D\).
2:for Epoch from 1 to M do
3:repeat
4: Collecting the initial environment state \(s_{1}\).
5: With a preset probability selecting a random action \(a_{t}\) otherwise selecting the \(a_{t}=\text{argmax}_{a}Q(s_{t},a)\).
6: Executing action \(a_{t}\) and receiving feedback from the environment. The feedback includes the reward \(r_{t}\) and the new environment state \(s_{t+1}\).
7: Saving the experience \(ex_{t}=[s_{t},a_{t},r_{t},s_{t+1}]\) in the replay memory \(D\).
8: Randomly sampling a mini-batch of experience \([s_{i},a_{i},r_{i},s_{i+1}]_{i=1}^{N_{mini}}\) from \(D\) where \(N_{mini}\) is the size of the mini-batch.
9: Setting

\[\begin{split}& y_{i}=r_{i}\quad\text{, if }s_{i+1}\text{ is the termination state}\\ & y_{i}=r_{i}+\gamma\text{max}_{a}Q(s_{i+1},a)\quad\text{, otherwise}\end{split} \tag{5}\]
10: Minimizing the loss function \(L=\frac{1}{N_{mini}}\sum_{i=1}^{N_{mini}}(y_{i}-Q(s_{i},a_{i}))^{2}\) and updating the parameters of DQN.
11:until the termination criteria are met.
12:endfor
```  
**Algorithm 1** Training procedure of DQN
## 3 Results
### Intra-Session Reliability in Isometric Belt Squat Test
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Mean** & **SD** & **CV\% (95\%CD)** & **ICC (95\%CD)** & **SEM (95\%CD)** & **SWC (0.2 (95\%CD)** \\ \hline Peak force (N) & 514.9 & 1332.8 & 6.1 (4.9-8.2) & 0.95 (0.98-0.98) & 316.4 (2545-422.0) & 258.9 (149.9-333.3) \\ Relative peak force (N/kg) & 65.8 & 21.9 & 6.0 (4.8-8.0) & 0.97 (0.94-0.99) & 3.9 (3.2-5.3) & 4.3 (2.5-5.5) \\ Force at 50 ms (N) & 1539.1 & 432.3 & 9.6 (7.7-12.8) & 0.89 (7.08-0.95) & 148.0 (119.0-197.4) & 81.2 (48.6-105.9) \\ RFD 0-50 ms (N-s) & 6698.3 & 58881.1 & 2.63 (2.12-35.1) & 0.90 (0.80-1.00) & 1763.0 (1418-2351.4) & 1123.6 (661.8-145.3) \\ Impulse 0-50 ms (N-s) & 66,154.6 & 14,580.7 & 9.3 (7.5-12.4) & 0.84 (0.68-0.92) & 6128.0 (4928-61732) & 2646.1 (1635.7-349.4) \\ Force at 100 ms (N) & 2003.8 & 800.6 & 11.4 (9.1-15.1) & 0.93 (0.85-0.97) & 227.5 (182.9-303.4) & 153.5 (900.0-198.6) \\ RFD 0-100 ms (N-s) & 8406.5 & 606.1 & 2.62 (10.3-94.9) & 0.90 (0.80-0.95) & 219.7 (179.2-2933.9) & 2145.8 (742.2-162.3) \\ Impulse 0-100 ms (N-s) & 156,359 & 4820.00 & 86.6 (9.1-91.4) & 0.93 (0.86-0.97) & 1341.47 (90.719.1-187.96) & 9259.0 (5418.3-19.5) \\ Force at 150 ms (N) & 2301.6 & 871.0 & 19.3 (15.5-25.7) & 0.76 (0.55-0.88) & 443.1 (356.4-591.0) & 150.0 (974.2-022.7) \\ RFD 0-150 ms (N-s) & 7290.9 & 4866.3 & 22.2 (7.9-27.9-27.9) & 0.90 (0.80-0.95) & 1621.1 (1303.8-216.1) & 917.5 (668.1-1195.0) \\ Impulse 0-150 ms (N-s) & 257,938 & 758.87 & 14.5 (11.6-19.3) & 0.79 (0.60-0.90) & 373.85 (803.046.9-49.87) & 33.2 (898.0-28.52) \\ Force at 200 ms (N) & 2634.4 & 959.3 & 12.6 (10.1-16.8) & 0.89 (0.78-0.95) & 331.9 (267.0-442.7) & 180.0 (1078.2-324.9) \\ RFD 0-200 ms (N-s) & 7491.9 & 390.44 & 16.5 (13.3-22.0) & 0.91 (0.82-0.96) & 1237.8 (995.5-1650.9) & 746.1 (441.7-69.0) \\ Impulse 0-200 ms (N-s) & 391,072 & 138,111.9 & 9.7 (7.8-13.0) & 0.93 (0.86-0.97) & 302.47 (0.58-27.05.15) & 26.554 (15.529.5-34.316.2) \\ Force at 250 ms (N) & 2908.3 & 969.7 & 13.0 (10.4-17.3) & 0.86 (0.73-0.93) & 376.7 (303.0-502.5) & 178.7 (998.0-234.9) \\ RFD 0-250 ms (N-s) & 6980.6 & 336.7 & 19.6 (16.0-26.5) & 0.84 (0.69-0.92) & 1387.2 (1115.7-158.2) & 66.06 (93.74-801.9) \\ Impulse 0-250 ms (N-s) & 540,461.9 & 173,484.7 & 8.7 (7.0-11.6) & 0.93 (0.86-0.97) & 477.02 (197.8-428.6-722.5) & 3339.97 (915.03-413.84) \\ \hline \end{tabular}
\end{table} 
Table 2: Intra-session reliability of isometric belt squat test.
## 3 Results and Discussion
### COD, Chromium Removal, and Coulombic Efficiency  
#### 3.4.1 COD Removal and Coulombic Efficiency  
It was observed that the LTA zeolite and bentonite-catalyzed electrode performances were better in terms of COD removal and CE as compared to the unmodified carbon felt. Table 1 shows the COD reduction and CE for batch runs for unmodified carbon felt, LTA zeolite, and bentonite-modified anodes. The COD removal for bentonite and LTA zeolite-modified carbon felt (\(96.3\pm 2.1\) % and \(93.8\pm 1.7\)%, respectively) was significantly higher (\(p<0.001\)) than that obtained with the unmodified carbon felt (\(78.7\pm 1.3\)).  
CEs increased significantly (\(p<0.001\)) when the modified anodes were employed. The CE was \(29.4\pm 1.5\)%with the bentonite-modified anode, \(21.2\pm 2.1\)% with the LTA zeolite-modified anode, and \(14.4\pm 1.9\)% with the unmodified carbon felt, even though the same tannery wastewater was used. The relatively higher CE obtained with the modified anodes might be explained by a greater amount of electro-oxidative bacteria with the ability to oxidize more organic substrates (COD) and transfer more electrons.
## 4 Existing Solutions
Contrary to the basic Batch, which is a collection statically defined by the web server, a Linked Batch is dynamically controlled by a web client. The resources forming the linked batch are referenced using Web Linking [10] and the CoRE Link Format [11]. The draft does not foresee any way to manipulate resources that are located on multiple smart objects with a single client request. An approach somewhat more similar to ours, also using the notion of an entity, has been presented in [22]. The aim here is to annotate real-world objects by using entities that are automatically created based on semantic information, which resides on the constrained devices. One problem of using semantics on constrained devices is that semantics can easily require a lot of memory that might not be available on the constrained devices. Further, in our approach users can create entities as required and we address important aspects related to entity validation and entity behavior. The authors of [23] present an extension to CoAP called SeaHttp, that enables communication with a group of resources. Similar to our work, SeaHttp also uses unicasts to realize group communication. The authors propose to extend CoAP with two additional methods (BRANCH and COMBINE) to allow members to join and leave groups without the need for a separate group manager. This means that members should have the intelligence to know which group they should join/leave. Constraineddevices will not have this intelligence, so again, a \"manager\" will be needed to inform the devices so they can take appropriate actions. Furthermore, BRANCH and COMBINE can maybe reduce the number of messages; however the trade-off is the need to implement a new mechanism. It is better to use an approach that can be plugged in into any existing network without major modifications (or at least not a modification to every node). The article does not discuss if the use of caches will still be possible with SeaHttp resources. However, should this be possible then also the caches should be extended accordingly. Finally this approach does not have the flexibility we target, since group members have to be reprogrammed with the groups they should join each time the requirements of the user changes. To our knowledge, these are the only works that explore communication solutions for interacting with a group of CoAP-enabled constrained devices. Next to these, there exist other solutions to realize or improve multicast communication in Wireless Sensor Networks, such as [18, 24]. These solutions can alleviate some of the problems related to (reliable) multicasting, but their scope is different from the work presented here.