esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Experimental Setup  
In our experiments, imaging sites' data are taken from KKI, NI, NYU, OHSU, and Peking of ADHD-200. The time sequence length of the data for each imaging site was different, among which the time sequence length of OHSU was the shortest, and its value was 73. The deep learning network requires a fixed length of an input signal. Therefore, all experiments only took the front part sequence of each imaging site's data, and the time sequence length \(s\) was set to 73.  
An end-to-end fMRI sequence representation learning framework was constructed to classify ADHD, which is shown in Figure 1. Specifically, the preprocessed fMRI data, a sequence of multiple ROIs, \(X_{\text{TS}}\), was firstly passed to the data conversion module, where \(X_{\text{TS}}\) was converted into \(X_{\text{IMG}}\) by a reshaping function. Then, the three-dimensional image, \(X_{\text{IMG}}\), was fed into the channel sequence representation learning module, which was a convolutional layer with three \(1\times 1\times s\) kernels. Next, it was connected by the pre-trained CNN, which produced the image feature \(f\) as output and was followed by a rectified linear unit (ReLU). Finally, the feature \(f\) was sent to the MLP classifier to classify HC and ADHD. The MLP classifier was composed of a fully connected layer and an activation layer. The training of the representation learning neural network needs to set up a loss function, an optimizer, and so on. ResNet34 is used as the pre-trained CNN in our model. The nn.crossentropyloss() function of the torch library was used as a loss function, where this cross-entropy is a negative log-likelihood loss function based on the log transformation of the SoftMax() activation function, and weight \(\alpha\) equals 2 or 3. During the neural network training, the batch size of the samples was set to 32, the learning rate \(lr\) was \(3\times 10^{-5}\), the number of epochs was 14, and the Adam optimization method was adopted. In the experiments, the following two schemes commonly used in ADHD classification were taken: (1) training and test on individual imaging site data and (2) training on combining site data and test on individual benchmark test data.
# Fusion Information Multi-View Classification Method for Remote Sensing Cloud Detection
## 2 Related Work
### Coupling Privileged Kernel Method for Multi-View Learning  
[PERSON] et al. [28] proposed a simple and effective multi-view learning coupling privileged kernel method (MCPK). MCPK integrates consensus and complementarity principles into a unified framework. In particular, consistency is captured by coupling terms between two views. Because the multi-view data collected from different domains can complement each other, a different feature view can receive explicit privilege information from its view. MCPK can be built as Equation (1).  
\[\underset{w_{A}w_{B},\phi_{A}\
eq\mathcal{B}}{min} \frac{1}{2}(\|w_{A}\|^{2}+\gamma\|w_{B}\|^{2})+C_{A}\sum_{i=1}^{l }\xi_{i}^{A}+C_{B}\sum_{i=1}^{l}\xi_{i}^{B}+C\sum_{i=1}^{l}\xi_{i}^{A}\xi_{i} ^{B},\] \[s.t. y_{i}(w_{A}\cdot\phi_{A}(x_{i}^{A}))\geq 1-\xi_{i}^{A},\] \[y_{i}(w_{B}\cdot\phi_{B}(x_{i}^{B}))\geq 1-\xi_{i}^{B},\] \[\xi_{i}^{A}\geq y_{i}(w_{B}\cdot\phi_{B}(x_{i}^{B})),\] \[\xi_{i}^{B}\geq y_{i}(w_{A}\cdot\phi_{A}(x_{i}^{A})),\] \[\xi_{i}^{A}\geq 0,\xi_{i}^{B}\geq 0,i=1,\ldots,l. \tag{1}\]  
where \(w_{A}\) and \(w_{B}\) are the weight vectors of view A and view B, respectively, and the two views are weighed by the non-negative trade-off parameter \(\gamma\). As slack variables, \(\xi_{i}^{A}\) and \(\xi_{i}^{A}\) are constrained by the correction functions determined by the two views. The coupling term \(C\sum_{i=1}^{l}\xi_{i}^{A}\xi_{i}^{B}\) makes the product of error variables of the two views as small as possible. When classifiers constructed from different views are more consistent, errors from both views are small, resulting in smaller couplings. Therefore, its consistency can be fully ensured. \(C\) is a non-negative coupling parameter that controls the influence of the coupling term. \(C_{A}\) and \(C_{B}\) are non-negative penalty parameters.
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 7 Influence of Different Structural Systems
### Shear Walls
#### 7.4.2 Reinforced Concrete Shear Walls  
The performance of reinforced concrete (RC) shear walls under post-earthquake fire (PEF) conditions is severely compromised by the degradation of both concrete and steel reinforcement. High temperatures drastically reduce the compressive strength of concrete, a particularly concerning issue in the context of PEF scenarios. Research involving a six-story RC building subjected to shaking table tests starkly demonstrated this effect [95], where the short columns on the ground floor--already heavily damaged during the earthquake simulation--experienced a reduction in concrete compressive strength to merely 20% of their original capacity. While steel reinforcement also experiences a reduction in yielding strength when exposed to fire, this effect is less pronounced than the degradation of the surrounding concrete, making the weakened concrete the predominant factor in the overall reduction of the shear wall's capacity under PEF conditions. Earthquake-induced cracks and spalling in the concrete create pathways for more rapid and deeper heat penetration into structural elements, exacerbating the degradation of both concrete and steel. This accelerated heat transfer initiates a dangerous cycle of weakening, further compromising the structural integrity of RC shear walls under fire conditions [95]. Residual drift and P-delta effects--additional forces and moments imposed on a structure due to its deformed shape--are critical considerations in the design of RC shear walls for PEF scenarios. Although a particular study on a six-story RC building found negligible residual lateral deformation after an earthquake simulation, the research emphasizes that these effects must be carefully evaluated to avoid underestimating potential risks [95]. A recurring theme across the findings is the necessity for a comprehensive approach to the analysis and design of structures for PEF events. Such an approach must consider the combined effects of both earthquake and fire hazards, as the interaction between these events can significantly impact structural performance. Ignoring this interplay could lead to dangerous design underestimations. The current scarcity of research focused specifically on PEF behavior is a significant concern. The findings consistently underscore the need for more experimental and analytical investigations to deepen our understanding of the complex interactions between earthquake damage and fire response in both steel and reinforced concrete structures. Advancing this knowledge is essential for developing accurate predictive models, improving design guidelines, and ultimately ensuring the safety and resilience of buildings in earthquake-prone regions.
# Hybrid Encoding Scheme for AMBTC Compressed Images Using Ternary Representation Technique
## 3 Proposed Method
### Encoding Procedures
The AMBTC compressed code of \(I_{0}\) is \(\{a_{0},\quad b_{0},\quad B_{0}\}=\{28,\quad 40,\quad 1110\quad 1110\quad 1100 \quad 1100_{2}\}\), and \(B_{0}\) is depicted in Figure 2b. Because \(\tau_{0}<b_{0}-a_{0}<\tau_{1}\), \(I_{0}\) is a smooth block. Assume \(\alpha_{0}^{*}=43\) and \(C_{43}=1010\ 0110\ 0101\ 0100_{2}\) (see Figure 2c). By comparing \(B_{0}\) and \(C_{43}\), we have \(\rho_{00}=5\), \(\rho_{01}=1\), \(\rho_{10}=4\), and \(\rho_{11}=6\). Using Equations (7) and (8), we have \(\hat{a}_{0}=33\) and \(\hat{b}_{0}=38\). Because \(\hat{b}_{0}-\hat{a}_{0}=5<\gamma\), we have \(Y=0\). Because \((\hat{a}_{0})_{2}=00100001_{2}\), \((\hat{b}_{0}-\hat{a}_{0})_{2}=0\Big{|}\Big{|}000101_{2}\), and \((\alpha_{0}^{*})_{2}=0101011_{2}\), the code stream of \(I_{0}\) should be \(CS_{0}=10\ ||\ 00100001\ ||\ 0|0010101\ ||\ 0101011_{2}\). Figure 1: Illustration of image block encoding. Figure 2d shows another block \(I_{1}\) to be encoded. For this block, quantized values \(a_{1}=25\) and \(b_{1}=103\) of the AMBTC code are calculated. Because \(b_{1}-a_{1}\geq\tau_{1}\), \(I_{1}\) is regarded as a complex block. Suppose after applying the \(k\)-means clustering algorithm to \(I_{1}\), we obtain three quantized values \(\{q_{1}^{0},\quad q_{1}^{1},\quad q_{1}^{2}\}=\{19,\,85,\,133\}\) and the ternary cluster indices of pixels \(T_{1}\)= \(\{1111\,2121\,0210\,\,0000\}\), as shown in Figure 2e. The difference between the first two quantized values is \(q_{1}^{1}-q_{1}^{0}=\,\,66>\gamma\). Therefore, indicator \(Y_{1,0}=1\) should be placed in front of the \(\log_{2}(\sigma)=7-\)bit binary representation of \(66\) (i.e., \(1||1000010_{2}\)). Similarly, because \(q_{1}^{2}-q_{1}^{1}=48<\gamma\), indicator \(Y_{1,1}=0\) should be placed in front of the \(\log_{2}(\gamma)=6-\)bit binary representation of \(48\) (i.e., \(0||110000_{2}\)). Finally, the ternary cluster indices \(T_{1}\) are encoded by \(10101010\,11101110\,011100\,0000_{2}\), which is illustrated in Figure 2f. Therefore, according to Step 3 of Case 3 in Section 3.4, the code stream of block \(I_{1}\) should be \(CS_{1}\)= \(11\,||\,00010011\,\,||\,1||1000010\,||\,0||110000\,||\,101010101110\,011100\,0 000_{2}\).
# Optimizing Electric Vehicle Routing Efficiency Using K-Means Clustering and Genetic Algorithms
## 3 Methodology and Problem Definition
### Genetic Algorithm
#### 3.3.6 Dynamic Process  
Our algorithm dynamically updates the route for an EV by trimming the traversed portion of the route up to the EV's current position. This ensures each iteration considers only the remaining segments and relevant charging stations. the following is a detailed description of each route adjustment:  
* Cluster assignment: Route points are assigned to clusters by proximity, so the algorithm evaluates queueing times only for charging stations within the same cluster.
* Charging station removal: The algorithm removes traversed route points and their unreachable charging stations, retaining only those relevant to the remaining route.
* Updating queuing times: The algorithm updates queuing times for charging stations in the cluster corresponding to the EV's current location, reflecting real-time conditions.
* Adjusting EV capacity: The algorithm adjusts the EV's capacity based on the distance traveled since the last charging station, ensuring accurate planning for the next charging stop.
* Passing the best route to the next segment: After each recalculation, the best route for the current segment is carried forward. Optimal charging stations are updated, and unavailable stations are removed, ensuring continuity and feasibility in the evolving route.  
After each adjustment, the genetic algorithm recalculates the optimal route for the newly shortened segment, incorporating updated queuing times at charging stations. The algorithm restarts for each segment until the EV reaches its destination. A charging station is selected for recharge only if it is at the EV's current position, i.e., the starting point of the shortened route. This selection is based on updated queuing times, ensuring the algorithm dynamically adapts to the EV's real-time conditions and optimizes the route with every update.
## 1 Introduction
For the latter, we set out to answer the following research questions categorized into two different categories:  
* Time factors
* Is the time it takes to fix a bug affected by the time of the day in which it was written? * Author factors
* Is the time it takes to fix a bug different if the buggy code is authored and committed by different users? * Is the time it takes to fix a bug different if the same user authored both the initial buggy code and the fix code? * Is the time it takes to fix a bug affected by the amount of active users in the project? To answer these questions about time differences accurately, we introduce the _Commit Cycle_ concept. Because open-source projects are of varying size and activity, it would be improper to compare the time it took to fix bugs in smaller, more inactive projects to larger, more active projects. Therefore, instead of using the raw time difference between the initial buggy code and the fixed code, we present time differences in _Commit Cycles_. This metric takes into account a project's activity to normalize these time differences across projects. The _Commit Cycle_ is calculated by taking the time difference in minutes between introducing the buggy code and fixing the code and dividing it by the project's _Commit Rate_. The _Commit Rate_ is a measure describing the number of commits a project introduces per minute. It is calculated by dividing the number of commits made in the project's relevant active period by the number of minutes in question. \[Commit\ Cycle=bug\ fix\ time\ in\ minutes*Commit\ Rate \tag{1}\]  
where  
\[Commit\ Rate=\frac{total\ commits\ in\ period}{minutes\ in\ period} \tag{2}\]  
This standardization allows us to proportionately compare fix times without having the project size as a confounding variable. We further discuss the _Commit Cycle_ concept in the following data section.
## 2 Methodology and Materials
### Data Preprocessing
#### 2.2.1 Label Encoding  
The first step is the encoding step of the features consisting of labeled variables. Boriding media and boriding method features consist of labeled data. These labeled values can be coded and converted into a numerical structure and analyzed how they affect the performance of the ML models. The transformation of the labeled value is performed using the OneHotEncoder approach, which is frequently used in the literature [56]. OneHotEncoder is a method for converting categorical data into a numerical form that ML algorithms can process. This technique converts each categorical value into a binary vector. For example, suppose a categorical variable has 3 different classes that are labelled 'Red', 'Blue' and 'Green'. OneHotEncoder creates a separate column for each class. These columns are filled with '1' for observations belonging to the class and '0' for observations not belonging to the class and a new dataset is created. When this method was applied to the boriding media and boriding method features, the number of features increased from 19 to 56 in this study. The performance of the ML algorithms is analyzed separately for the two cases with and without transformation.
## 4 Discussion
### Limitations and Future Works  
This study suffers from several limitations, including the use of a specific robot (i.e., Sota) and concentrating on a specific conversational topic (i.e., Japan's state of emergency). Our future works will investigate the influence of recency effects as well as opinion changes with different conversational topics, such as more general or personalized conversations.  
In addition, using different entities (e.g., different robots or human speakers) would make for interesting future works. We did not conduct the experiment with human speaker cases in this study to avoid their various characteristics and effects, such as appearances, age, perceived authority, and so on. If we conducted an experiment with human speakers, the power of opinion changes would be stronger than with robot speakers, but the trends of conditions would be the same because people regard social robots as \"social others\", with similar social influences to human being [30; 31; 32].  
From another perspective, the trust relationships would have influenced opinion changes. In human-robot interaction studies, building trust relationships between robots and people is one of the active research topics [33; 34; 35; 36]. Moreover, past studies reported how trusted robots are effective in the context of persuasion [37; 38; 39]. Therefore, people may change their opinions to a greater extent when they observe that their trusted robots' opinions change.  
Since we only conducted our experiment with Japanese participants, generality and cultural differences must also be considered. Although the primacy/recency effects have been investigated in-depth worldwide, cultural differences have received less focus. A few studies reported that Americans showed a stronger primacy effect than Asians [40; 41]; therefore, conducting our study with different countries might illuminate cultural differences. The relationships between showing opinion changes and perceived trust remain unknown.
# A Low Energy IoT Application Using Beacon for Indoor Localization
An indoor location system can be defined as a system for identifying the position of objects or people in a delimited and closed space. Subsequently, guidelines are drawn for the approximate calculation of the coordinates of a two-dimensional space of the position of a specific object, or user, in an indoor environment. The various techniques used today are based on such technologies as ultrasound, Bluetooth, Wi-Fi, etc. The techniques that can be used are divided into two distinct types: recognition techniques based on tracing (triangulation, proximity, dead reckoning) [14], and techniques defined as RSS techniques, based on signals wireless. What happens is that often the localization systems, based exclusively on a single technology, suffer from errors due to the limitations of the technology itself, often resulting in lower accuracy or even, at times, an exaggerated increase in costs due to the attempt to correct errors. The current trend is to combine multiple sensing technologies in order to increase accuracy and reduce costs, making the most of the characteristics of each technology.
# Expert System Based on Integrated Fuzzy AHP for Automatic Cutting Tool Selection
## 2 Proposed Methodology
### Tool Type Selection Model  
There are a lot of milling tool types, and the characteristics of machining operations are also different. In the AHP or fuzzy AHP methods, the number of alternatives is normally smaller or equal to ten [8]. The higher the alternative number is, the more complex the calculation and judgments are. Thus, it is convenient for the evaluation to fabricate each fuzzy AHP model for each specific milling operation. The number of alternatives is reduced, and the criteria are more suitable for each operation. The selection model can be presented as shown in Equation (9). In this model, the cutting tool type selection is a function (\(F_{ToolType}\)) of the initial selection shape characteristics (\(SC\)), work stability (\(WS\)), feature quality (\(FQ\)), or specific type (\(TE\)) to evaluate four criteria, including the material removal capacity (\(MRC\)), power requirement (\(PR\)), tool cost (\(TC\)), and flexibility (\(FE\)):  
\[F_{ToolType}=f(SC,\,WS(LI,\,NO,\,HE),\,FQ(RO,\,SF,\,FI)),\,TE)\rightarrow\left\{ \begin{array}{l}MRC\\ PW\\ TC\\ FE\end{array} \right.. \tag{9}\]  
Table 2 shows the selection evaluation criteria and alternatives for the most common milling operations. There are only three selection criteria for the first four milling operations, but one more criterion is added in pocket and profile machining. Figure 5 shows the developed hierarchical structure for the shoulder milling feature as a case study. The goal is to select the most suitable cutting tool type from a collection of five alternatives, which are the available cutting tool types for this kind of machining feature. The first three selection criteria can be chosen by users. Regarding shape characteristics, there are three sub-criteria--the aspect ratio (\(AR\)), width (\(WI\)), and side (\(SI\))--as shown in Equation (10). \(SC_{Shoulder}\) is presented for the relationship among shape characteristics for shoulder milling. The rule-based method is used to classify each sub-criterion into sub-sub-criteria. AR is the ratio between the depth (\(DE\)) and width, which has three levels, named low (\(LO\)), medium (\(ME\)), and high (\(HI\)), corresponding to \(DE\)/\(WI\) ratios in the range of (0-1), (1-2), and more than 2. There are also three levels of width criteria, namely small (\(SM\)), medium-large (\(ML\)) and large (\(LA\)), which correspond to \(WI\) in the ranges of (0-15 mm), (15-50 mm), and more than 50 mm, respectively. Depending on the tool's access direction, the side has two options: top (\(TO\)) or back (\(BA\)). Similar rules are applied when classifying feature quality into rough (RO), semi-finish (\(SF\)), or finish (\(FI\)). \[SC_{Shoulder}=\varphi(AR(LO,\,ME,\,HI),\,WI(SM,\,ML,\,L),\,SI(TO,\,BA)). \tag{10}\]  
To define the feature stability, three characteristics are used, namely the wall thickness, the ratio between the depth and wall thickness, and the workpiece weight. For example, if the wall
# R&D of an Innovative OFDM Communication Payload for Small-Size AUV
## 2 Systems and Methods  
### Basic Signal Model  
Without loss of generality, one cyclic-prefixed (CP) OFDM block can be formulated as  
\[s(t)=\Re\Bigg{\{}e^{i2\pi f_{c}t}\sum_{k=-L/2}^{L/2-1}s[k]e^{i2\pi\frac{k}{t}t }g(t)\Bigg{\}}, \tag{1}\]  
where \(f_{c}\) is the center frequency, \(s[k]\) denotes the symbol to be transmitted on the \(k\)-th sub-carrier, and \(T_{s}\) is the basic OFDM symbol duration. Let \(L\) represent the number of sub-carriers, which dictates that the \(k\)-th sub-carrier is located at the frequency  
\[f_{k}=\frac{k}{T_{s}},\,k=-\frac{L}{2},-\frac{L}{2}+1,\ldots,\frac{L}{2}-1, \tag{2}\]Let \(T_{cp}\) denote the length of the CP, and \(g(t)\) can be defined as a rectangular window of length \(T_{cp}+T_{s}\)  
\[g(t)=\left\{\begin{array}{ll}1,&t\in[-T_{cp},T_{s}]\\ 0,&\text{otherwise}\end{array}\right., \tag{3}\]  
One can assume that the UWA channel can be modeled as a time-varying linear system, and the Doppler effect on each path is uniform. Letting the \(p\)-th amplitude response maintain constant over one OFDM block, the UWA channel response can be expressed as  
\[h(t,\tau)=\sum_{p=1}^{N_{p}}A_{p}\delta\big{(}\tau-\tau_{p}+\epsilon t\big{)}, \tag{4}\]  
where \(N_{p}\) is the maximum discernible number of multipath, \(\epsilon\) denotes a common Doppler factor, moreover, \(A_{p}\) and \(\tau_{p}\) represent the time-varying amplitude response and delay of the \(p\)-th path, respectively.  
Based on the previous assumptions, letting the CP-OFDM signal pass through the channel as described in (4), the received baseband signal can be formulated as  
\[r(t)=\sum_{k=-L/2}^{L/2-1}\left\{s[k]e^{2\pi\tau_{k}t}e^{2\pi\varepsilon_{k}t }\left[\sum_{p=1}^{N_{p}}A_{p}e^{-2\pi\tau_{k}\tau_{p}}\right]g(t+\epsilon t- \tau_{p})\right\}+n(t), \tag{5}\]  
where \(n(t)\) is the baseband white Gaussian noise. Note that the Doppler effects on received OFDM packets can be expressed as  
\[\xi_{k}=e^{2\pi\lambda\epsilon_{k}t}. \tag{6}\]  
Based on the above derivation, Doppler estimation and compensation are essential to eliminate the interference of (6) for accomplishing robust mobile UWA OFDM communication.
## 3 Results
### Comparison with Actual Data  
#### 3.3.1 Rotor Equivalent Wind Speed Calculation  
In this study, the rotor disk was divided into a total of 5 sectors to calculate the rotor equivalent wind speed, and Figure 9 and Table 6 show the area and height of the detailed sectors. Table 1 mentions that the hub height of the turbine is 81.6 m, and Table 2 explains that the height of the offshore platform where the LiDAR is installed is 25.6 m above sea level. For convenience in calculation, the turbine hub height and LiDAR observation height offset are assumed to be 80 and 25 m, respectively. Although the uncertainty of the analysis result may increase slightly, it was judged that it would not have a significant level of influence on the result.