esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring ISAC: Information-Theoretic Insights
## 5 Network ISAC with Sensing Distortion
### Multi-Access ISAC: Collaborative Sensing and Suboptimality of Symbolwise Estimators
#### 5.2.1 The Memoryless Model  
The model is similar to before, however, we now have two Txs and a single Rx, see Figure 10. Each Tx \(k\in\{1,2\}\) wishes to send a rate-\(R_{k}\) message to the Rx and estimate a memoryless state sequence \(\{S_{k,i}\}\). The sequence of pairs \(\{(S_{1,i},S_{2,i})\}_{i\geq 1}\) are i.i.d. according to a given joint pmf \(P_{S_{1}S_{2}}\). The channel input-output relation is specified by the memoryless and stationary channel transition law \(P_{YZ_{1}Z_{2}|S_{1}S_{2}X_{1}X_{2}}\). Based on the two messages \(W_{1}\) and \(W_{2}\) and the past generalized feedback signals \(Z_{k,1},\ldots,Z_{k,i-1}\), each Tx \(k\in\{1,2\}\) generates its time-\(i\) channel input as \(X_{k,i}=\phi_{k,i}(W_{k},Z_{k,1},\ldots,Z_{k,i-1})\) and at the end of the communication, it estimates the state sequence as \(\hat{S}_{k}^{n}=h_{k}(X_{k}^{n},Z_{k}^{n})\). The estimated sequence \(\hat{S}_{k}^{n}\) should match the state sequence \(S_{k}^{n}\) up to distortion level \(D_{k}\) when measured by a given per-symbol distortion function \(d_{k}(\cdot,\cdot)\). The receiver decodes both messages \(W_{1}\) and \(W_{2}\) based on its observed channel outputs as \((\hat{W}_{1},\hat{W}_{2})=g(Y^{n})\).  
Figure 10: State-dependent discrete memoryless multi-access channel with sensing at the transmitters.  
**Definition 4**.: _A rate-distortion tuple \((R_{1},R_{2},D_{1},D_{2})\) is called achievable in the setup above if there exists a sequence (in \(n\)) of encoding, decoding, and estimation functions such that_  
\[rCl\lim_{n\to\infty}\Pr\Big{(}\hat{W}_{1}\
eq W_{1}\quad\text{or} \quad\hat{W}_{2}\
eq W_{2}\Big{)} = 0 \tag{40a}\] \[\lim_{n\to\infty}\frac{1}{n}\sum_{i=1}^{n}\mathbb{E}[d_{k}(S_{k,i },\hat{S}_{k,i})] \leq \text{D}_{k},\quad\text{for}\;k\in\{1,2\}. \tag{40b}\]  
_In this multi-access ISAC setup, the closure of the set of all achievable tuples \((R_{1},R_{2},D_{1},D_{2})\) is called the ISAC-MAC capacity-distortion region \(\mathcal{CD}\)._  
Like the previous point-to-point and broadcast ISAC models, the above MAC model also includes scenarios with (perfect or imperfect) Rx channel state information as special cases. (The state information can simply be added as part of the output.) Note further that the above model also includes scenarios where the channel is governed by an internal i.i.d. state sequence \(S^{n}\) of pmf \(P_{S}\) and the states \(S_{1}^{n},S_{2}^{n}\) are obtained from \(S^{n}\) over an independent memoryless channel \(P_{S_{1}S_{2}|S}\).
## 4 Evaluation Model Based on AH-Entropy and Normal Cloud Model
### Determination of Combination Weights
#### 4.2.1 Deriving Subjective Weights with AHP  
The calculation procedure of AHP [37] is outlined as follows:  
Step 1: Invite experts to conduct pairwise comparisons of the performance evaluation indicators of the current asphalt pavement in highway renovation and expansion projects, assigning scores based on their relative importance. The specific scores and corresponding scale meanings are detailed in Table 1.  
Figure 3: Positive cloud generator.  
Figure 2: The cloud image (in the figure, \(Ex=50\), \(En=10\), \(He=1\)).  
Step 2: Construct a judgment matrix based on expert scoring. Assuming there are \(n\) evaluation indicators, the judgment matrix \(A=(a_{\text{ij}})_{n\times n}\) is as follows:  
\[A=\begin{bmatrix}a_{11}&a_{12}&\dots&a_{n1}\\ a_{11}&a_{22}&\dots&a_{n2}\\ \dots&\dots&\dots&\dots\\ a_{n1}&a_{n2}&\dots&a_{nn}\end{bmatrix} \tag{6}\]  
where the element \(a_{\text{ij}}\) of the judgment matrix is determined from Table 1 based on expert opinions.  
Step 3: Compute the maximum eigenvalue \(\lambda_{\text{max}}\) of matrix \(A\) according to Equation (7) and derive the weight matrix \(W\) accordingly:  
\[\lambda_{\text{max}}=\sum_{i=1}^{n}\frac{(AW)}{nW_{i}}=\frac{1}{n}\sum_{i=1}^{ n}\frac{(AW)_{i}}{W_{i}} \tag{7}\]  
where:  
\[AW=\begin{bmatrix}(AW)_{1}\\ (AW)_{2}\\ \dots\\ (AW)_{n}\end{bmatrix}=\begin{bmatrix}a_{11}&a_{12}&\dots&a_{1n}\\ a_{21}&a_{22}&\dots&a_{2n}\\ \dots&\dots&\dots&\dots\\ a_{n1}&a_{n2}&\dots&a_{nn}\end{bmatrix}\times\begin{bmatrix}\omega_{1}\\ \omega_{2}\\ \dots\\ \omega_{4}\end{bmatrix} \tag{8}\]  
Step 4: Consistency verification. This study employed a composite indicator CI for assessing consistency.
# Using a High-Power Fibre Laser to Cut Concrete
###### Abstract  
Concrete cutting at construction sites causes problems such as noise, vibration, and dust. In particular, during the demolition and renovation work on buildings in urban areas, protection against noise, vibration, dust, etc., is needed. Concrete cutting using a CO\({}_{2}\) laser was investigated 20 years ago. However, this method had never used because the equipment is difficult to carry. In this study, we used a portable fibre laser, which is convenient to carry. Two types of concretes with different strengths were prepared for the experiment. High-strength concrete has never been used in similar research before. High-strength concrete is just only used for skyscrapers because of its high quality and costs. Furthermore, it has already been used for skyscrapers in Japan. It is for this reason that we chose to use it in this study. Irradiation measurements were conducted under various conditions using laser powers of 6 and 9 kW. It was confirmed that the cutting effectiveness of CO\({}_{2}\) and fibre lasers was approximately identical for concretes with a thickness of 200 mm. Furthermore, the cutting effectiveness for the two concretes with different densities was almost the same. However, the situation after cutting was different because the vitrification of the cutting and glass formation progressed in low-density concrete and an explosion phenomenon occurred in high-density concrete, simultaneously. This study suggests that laser concrete cutting can be used as a solution when noise and dust are major problems.  
laser; cutting; concrete; laser condition; cutting depth +
Footnote †: journal: Applied Sciences  
Furthermore, the author has been studying the use of lasers in the construction field [8; 14; 15; 16]. We have shown that these results confirmed that, the level of effectiveness differs depending on different heat volumes, even if the materials are the same.  
Thus, a variety of factors, including the laser power, cutting speed, composition, and thickness of the object, are significant. According to this background, we focused on two types of concretes, including high-strength concrete, which has been used for skyscrapers. These results suggest cutting concrete to reconstruct include skyscrapers.  
Other issues to be considered include using laser oscillators, miniaturising the power supply and other equipment, improving the handling performance of beam delivery, establishing safety measures, and cost. Therefore, although lasers can cut concrete, many issues need to be addressed to make them practically applicable.  
In this study, the cutting effectiveness of a fibre laser is examined. Specifically, the effects of cutting and additional issues that arise under two different concrete strengths and two different laser conditions for a cutting depth for 200 mm thick concrete are discussed.
## Appendix A Conceptual Evaluation on Projectile Motion  
**Instructions:**  
Mark the correct answer for each question.  
* When calculating the maximum height reached by a projectile, which of the following variables is zero at that instant?
* A. Time.
* B. Horizontal velocity.
* C. Vertical velocity.
* D. Horizontal displacement.
* When determining the position of a projectile, which variables must be calculated?
* A. The values of \"x\" and \"y.\"
* B. Horizontal velocity and vertical velocity.
* C. Total velocity and angle.
* D. Time of ascent and maximum height.
* Based on the equations for projectile motion, which variable must be replaced with zero to obtain the equations used in horizontal projectE motion?
* A. Time.
* B. Vertical velocity.
* C. Vertical displacement.
* D. Initial vertical velocity.
* According to the studied material, what is the sign of the total velocity of an object undergoing parabolic or horizontal projectile motion?
* A. It is positive if ascending and negative if descending.
* B. It has no sign.
* C. It is always positive.
* D. It is always considered negative.
* An object is launched from an elevated position relative to the ground with a specific velocity. Based on the approach presented in this section, where is it most convenient to place the origin of the x, y reference system?
* A. It does not matter where it is placed.
* B. On the ground.
* C. At the launch point.
* D. At the maximum height point.
# Adaptive Optimization Method for Piled Raft Foundations Based on Variable Pile Spacing
## 2 The Theoretical Basis of AOM  
Before introducing AOM, it is necessary to introduce a two-dimensional plane problem first. Figure 1 shows a raft that suffers a non-uniform line load \(y=f(x)\). The problem is locating the pile to avoid the imbalanced settlement of the raft when there are N piles under the raft. The area between \(y=f(x)\) and x-axis is assumed as \(A_{\rm f}\), and the raft will keep its balance when the pile is located at the co-ordinate of the centroid of \(A_{\rm f}\) according to the mechanical principles (in Figure 1a). In a similar way, we divide \(A_{\rm f}\) into \(A_{1}\) and \(A_{2}\), in which \(A_{1}=A_{2}\), and locate two piles at the co-ordinate of the centroid of \(A_{1}\) and \(A_{2}\), which can also keep the raft in balance (in Figure 1b). The same can be done when the number of piles is \(N\) (in Figure 1c).  
The way to solve the two-dimensional plane problem is also suitable for three-dimensional (3D) space problems. For a 3D problem, the line load changes to the surface load \(F=f(x,y)\), and \(A_{\text{f}}\) changes to \(V_{T}\), which is the volume between \(f(x,y)\) and co-ordinate axes. We divide \(V_{T}\) into \(N\) (\(N\) is the number of piles) equal parts, and then the co-ordinate of the centroid of \(V_{i}\) is the location of pile \(i\). Considering that the settlement of the raft is the comprehensive result of the upper load and pile-soil interaction, the settlement distribution of the raft (\(z(x,y)\)) can be used to replace the distribution of the upper load (\(F=f(x,y)\)) of the raft in three-dimensional problems. Therefore, \(V_{T}\) can be expressed as  
\[V_{T}=\iint\limits_{A_{n\
eq t}}z(x,y)dxdy \tag{1}\]  
where \(A_{n\
eq t}\) is the area of the raft. We divide \(V_{T}\) into \(N\) equal parts, and each part is:  
\[V_{1}=\dots V_{i}=\iint\limits_{A_{i}}z(x,y)dxdy\dots=V_{N} \tag{2}\]  
where \(A_{i}\) is the projection area of \(V_{i}\) on the co-ordinate axis. The co-ordinate of the centroid of \(V_{i}\) is the location of pile \(i\).
# Evaluation of AI-Assisted Telemedicine Service Using a Mobile Pet Application
## 4 Results
### Additional Analyses (Moderating Effect and Robustness Check)  
We tried to analyze the moderating effect on H2, H3, and H4b for in-depth consideration. We sought to conduct additional analyses through a robustness test to verify the outcomes of the basic hypotheses. Thus, we set up additional hypotheses as follows. Since H3a and H3b showed significant results, we set the reinforcement hypotheses H5a and H5b in order to verify them again. In addition, since H2 showed a significant result, we determined that it was necessary to further subdivide and examine the health status of companion animals; thus, H6 was set and tested. We also set H7 to examine the specific effects of the degree of ease of use of mobile app services on the negative effects of telemedicine further, as H4b appeared to be a significant result.  
**Hypothesis 5a (H5a).**_Negative perceptions about pet telemedicine services differ depending on the degree of ease of use of mobile app services._  
**Hypothesis 5b (H5b).**_Negative perceptions about telemedicine services for people differ depending on the degree of ease of use of mobile app services._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hypothesis** & **Path** & **Path Coefficient** & **t-Value** & **Test Result** \\ \hline \multirow{3}{*}{H1} & Adoption period \(\rightarrow\) positive & & & \\  & perception of pet & 0.108 & 0.027 & Reject \\  & telemedicine service & & & \\ \hline \multirow{3}{*}{H2} & Health condition \(\rightarrow\) positive & & & \\  & perception of pet & \(-\)0.527 & 6.507 & *** & Accept \\  & telemedicine service & & & \\ \hline \multirow{3}{*}{H3a} & Ease of use of mobile application & & & \\  & service \(\rightarrow\) positive perception of & 0.484 & 5.148 & *** & Accept \\  & pet telemedicine service & & & \\ \hline \multirow{3}{*}{H3b} & Ease of use of mobile application & & & \\  & service \(\rightarrow\) positive perception of & 0.371 & 2.986 ** & Accept \\  & telemedicine service for people & & & \\ \hline \multirow{3}{*}{H4a} & Positive perception of pet & & & \\  & telemedicine service \(\rightarrow\) positive & 0.065 & 0.211 & Reject \\  & perception of telemedicine service & & & \\ \hline \multirow{3}{*}{H4b} & Negative perception of pet & & & \\  & telemedicine service \(\rightarrow\) negative & & & \\ \cline{1-1}  & perception of telemedicine service & & & \\ \cline{1-1}  & for people & & & \\ \hline \hline \end{tabular} (Significant Level: \({}^{**}p<0.05\), \({}^{***}p<0.01\)).  
\end{table}
Table 7: Result of hypotheses test with path coefficient.  
**Hypothesis 6 (H6).**_The severity of a companion animal's disease has different effects on the companion animal's health status and positive perceptions about pet telemedicine services._  
**Hypothesis 7 (H7).**_The degree of ease of use of mobile app services has different effects on the negative perceptions about pet and human telemedicine services._
## 3 Results & Discussions
### Effect of Initial Heat Treatment  
A linear shrinkage of 7.7% and mass loss of 12.0% occurred after heating cured ASH-G composites to 900 \({}^{\circ}\)C for five hours using heating/cooling rates of 2.5 \({}^{\circ}\)C/min. Additionally, a uniform glaze of approximately 200 \(\upmu\)m thickness formed on the 2.5 \({}^{\circ}\)C/min heat-treated polished samples as seen in Figure 3. The densities before and after initial heating to 900 \({}^{\circ}\)C were 1.93 g/cc and 2.32 g/cc, respectively. Open porosities before and after initial heating to 900 \({}^{\circ}\)C were 19.95% and 0.73%, respectively. The results are shown in Table 3.  
Figure 1: Class frit powder as received (**A**). Alumina platelets (50 \(\upmu\)m) as received (**B**).  
Figure 2: ASH-G composites before (**A**) and after (**B**) heating to 900 \({}^{\circ}\)C for five hours using 2.5 \({}^{\circ}\)C/min (**left**) and 10 \({}^{\circ}\)C/min (**right**) heating and cooling rates.  
Previous work indicated no chemical reaction occurred among the three phases according to XRD and EDS results [31]. After heating plain KGP without additives to 900 \({}^{\circ}\)C a series of crystalline peaks appeared in an XRD analysis [31]. Microcracks formed during geopolymer matrix dehydration/densification were filled by molten glassy phase as indicated by little-to-no open porosity in the heated samples as expected [31; 50]. Figure 4 shows how the glass phase filled much larger cracks after heat treatment. This removed the presence of discrete glass particulates and introduced oblong, form-fitting regions of glass and geopolymer matrix [31]. This phenomenon makes this material a good candidate for the containment of high-temperature liquids as the liquid cannot penetrate the glassy layer, leaving the structural material untouched and unaffected.  
Figure 4: Micrograph of polished ASH-G composites after heating to 900 \({}^{\circ}\)C for 5 h using heating/cooling rates of 2.5 \({}^{\circ}\)C/min.  
Figure 3: Micrographs of polished (**A**) and fractured (**B**) ASH-G composites after heating to 900 \({}^{\circ}\)C for 5 h using heating/cooling rates of 2.5 \({}^{\circ}\)C/min.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Linear**} & \multirow{2}{*}{**Mass Loss (\%)**} & \multirow{2}{*}{**Density (g/cc)**} & \multirow{2}{*}{**Open Porosity**} \\  & **Shrinkage (\%)** & & & **(\%)** \\ \hline Room & & & & \\ Temperature & 0 & & & \\ \hline Post 2.5 \({}^{\circ}\)C/min & & & & \\ Heat Treatment & 7.7 & 12.0 & 2.32 & 0.73 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical Changes Due to Initial Heating to 900 \({}^{\circ}\)C.
## 4 Excitation Characteristics of Mistuned Stators and Its Effect in Reducing Excitation Amplitude  
### Mistuned Stator Pattern and Naming Rules  
In this paper, two types of mistuned stator distribution have been defined: asymmetric mistuned stators and sectional non-uniform mistuned stators.  
Asymmetric mistuned means that the whole circle of blades is divided into two sections with the angles between blades in each section being the same, whereas the angles between blades in another half are different. As shown in Figure 11a, the stator cascade is divided in two halves: one where the blade angle is \(\theta_{1}=2\pi/N_{1}\) and the other where the blade angle is \(\theta_{2}=2\pi/N_{2}\). For the asymmetric mistuned stator, we define the name as:\(IGV=N_{s}(N_{1}/N_{2})\). Therefore, the asymmetric mistuned stator in Figure 9 should be named: \(\text{IGV}=12(10/14)\).  
Sectional non-uniform mistuned means the whole circle of blades is divided into \(M\) sections, where there are \(x=N_{1}/M\) blades in each section. One blade in the same phase of each section is chosen, and this is adjusted in the same direction with the same angle \(\Delta\theta\) or displacement \(\Delta d\) (along the rotation axis), as shown in Figure 11b. For the sectional non-uniform mistuned stator, the name can be defined as: \(IGV=N_{s}(M/x,\Delta\theta)\) or \(IGV=N_{s}(M/x,\Delta d)\). Therefore, the asymmetric mistuned stator in Figure 9 should be named: \(IGV=12(4/3,\Delta\theta)\).  
In order to study the excitation characteristics of different mistuned stators, four different mistuned forms of stator were designed, as shown in Table 5.
### Importance of Mechanical Processes in Food Engineering  
Mechanical processes are fundamental in food engineering, transforming raw ingredients into structured, stable, and sensory-acceptable products by influencing their physical form, microstructure, and functional characteristics, thereby impacting production efficiency, nutrition, and organoleptic properties.  
Key mechanical unit operations include:  
* Mixing and Homogenization: Ensuring uniform ingredient distribution, texture control, and product stability (e.g., emulsions, doughs) [6].
* Extrusion: Combining thermal and mechanical energy to create texturized products like cereals, snacks, pasta, and meat analogues through continuous processing, shaping, and even encapsulation [7].
* Size Reduction and Shaping: Operations like cutting, grinding, and forming that impact product uniformity and subsequent processing [8].
* Mechanical Drying Assistance: Systems (e.g., fluid-bed, vibratory) that use airflow and mechanical agitation to enhance mass and heat transfer, reduce drying times, and prevent agglomeration [9].  
These operations enable process intensification, increasing productivity, reducing energy consumption, and minimizing waste. For example, twin-screw extrusion allows in situ mixing, cooking, and shaping in a single unit. Mechanically induced structuring is crucial for novel food textures, such as fibrous plant-based meat analogues or aerated products like mousses [10]. Furthermore, mechanical processes affect food safety and shelf-life by controlling moisture content, water activity, and structural integrity. Their complex nature, involving interactions of mechanical forces, thermal effects, and material properties, necessitates advanced modeling tools like CFD for design, scale-up, and real-time control [11].
## 3 Mathematical Model
### The Traveling Salesman Problem  
Let us consider \(x_{ij}\) to be the connection between products \(i\) and \(j\); \(x_{ij}\) is an integer, and its value is defined by the status of the path:  
\[x_{ij}=\begin{cases}1&\text{if }loc_{i}\text{ and }loc_{j}\text{ are connected}\\ 0&\text{otherwise}\end{cases} \tag{5}\]  
In a kitting process, \(x_{ij}\) describes the actual reconfiguration process between two kits. Its cost is described by \(c_{ij}\) of Equation (4).  
The TSP finds \(X=[x_{ij}]\) that solves the function as follows:  
\[\min\left(\sum_{i=1}^{n}\sum_{j\
eq i,j=1}^{n}c_{ij}x_{ij}\right) \tag{6}\]While TSP can be used with the full \(C\) matrix, the computational time may greatly increase if a large number of products is to be considered [45]. Therefore, it is important to reduce the computational time to obtain a good solution in a reasonable time.  
To do so, clustering is of great importance: not only is the algorithm that solves the TSP constrained to evaluate a smaller number of solutions, but the intrinsic characteristic of TSP makes it well suited for the clustering of cities [44]. In fact, clustering the cities in the TSP means that the solution \"enters\" the cluster, moves between all the cities of the cluster and then moves to another cluster. This means that for each cluster \(S\), containing \(n_{s}\) positions, the TSP defines a non-closed sub-tour containing only the positions of the cluster. This constraint can be defined as follows [44]:  
\[\sum\limits_{i\
eq j,j\in S,j\in S}x_{ij}=n_{s}-1 \tag{7}\]  
since the last connection that would close the tour has to be removed to connect the cluster to the other positions. The number of connections between the clusters must be equal to the number of clusters to obtain a closed path. The clustering can be performed multiple times: Equation (7) can be used to group different clusters or group different products.  
By applying constraints to the TSP, the final solution can result in a cost which differs from the optimal solution, but can be obtained in lower computational times.
# Comparing Speaker Adaptation Methods for Visual Speech Recognition for Continuous Spanish +
## 3 The LIP-RTVE Database  
One of the main reasons why we chose the LIP-RTVE database [49] was because it offers a suitable support to estimate VSR systems for the Spanish language against realistic scenarios. In addition, it defines a partition for both speaker-dependent and speaker-independent scenarios, each with respective training, development, and test datasets. This challenging database was compiled from TV newscast programmes recorded at 25 fps with a resolution of 480 \(\times\) 270 pixels. No restrictions were considered in data collection in terms of ability to find so-called spontaneous speech phenomena, head movements, or different lighting conditions. The corpus comprises 323 speakers, providing 13 h of data with a vocabulary size of 9308 words.  
It should be noted that due to the nature of our proposed study only the speaker-dependent partition of the LIP-RTVE database was considered in our experiments. More specific details on how the database was processed to carry out our research experiments according to the proposed study are described in Section 6.1.