esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of High-Power Radar Propagation Environments Around the Test Site
## 4 Verification of Analysis Results Through Comparison of Measurement and Simulation
In order to verify the free-space loss obtained from simulation, the measured received power is applied into the Friis transmission equation, which includes not only free space propagation loss but also various system level losses, as expressed in the following equation:  
\[P_{r}(dB)=P_{t}+G_{t}+G_{r}-L_{tc}-L_{rc}-L_{s}-L_{p} \tag{3}\]  
where \(P_{r}\) is the received power of Rx. antenna (dBm), \(P_{t}\) is the transmitted power of Tx. antenna (dBm), \(G_{t}\) and \(G_{r}\) are the antenna gains at the Tx. and Rx. antenna (dBi), and \(L_{tc}\) and \(L_{rc}\) represent the cable losses (dB), which are integrated with the Tx. and Rx. antenna systems, respectively. \(L_{s}\) also represents other system losses (such as connectors and circuit loss), and \(L_{p}\) is the Free Space Path Loss (FSPL), which depends on the distance between Tx. and Rx. systems, as well as the operation frequency. Since the conventional Friis model  
Figure 8: Fabrication and reflection coefficient of the Tx. and Rx. antenna: (**a**) fabrication of \(\lambda\)/4 monopole antenna; (**b**) reflection coefficients. assumes ideal transmission conditions, it is necessary to consider the measured cable loss into the equation to reflect the actual signal attenuation in the measurement setup. Radiated power was measured at nine points arranged in a 3 \(\times\) 3 matrix with 1 m increments. The measured values are listed in Table 4. The averaged measured power is then compared with the simulated result. Herein, the measured data is obtained at nine points (\(p_{1}\) to \(p_{9}\)) at 1 m intervals, as shown in Figure 10. The averaged measured power is then compared with the simulated result. The comparison of the simulation results, conducted under the same conditions as the theoretical and measurement results, is presented in Table 5. The theoretical received power is calculated by accounting for the measured monopole antenna gain, cable loss, and system loss, while the measured received power is averaged over the nine measurement points. The theoretical and average measured received powers of \(-61.9\) dBm and \(-62.03\) dBm, respectively, show good agreement with the simulated result of \(-64.64\) dBm. Therefore, we validate our measurement method and support the reliability of the X-band and Ku-band analyses in Chapter 3. This procedure indicates that the proposed method provides comparable results in theory, simulation, and measurement. Figure 9: Radiation patterns of the Tx. and Rx. antenna: (**a**) measurement setup in a full anechoic chamber; (**b**) radiation patterns (\(\theta\) = 0); (**c**) radiation patterns (\(\varphi\) = 0).
## 4 Analysis of Test Results
### Spatial Distribution Characteristics of Goaf Pressure
#### 4.2.1 Critical Layer Deformation
These mechanical parameters yield a theoretical ultimate strain threshold of 3830 \(\upmu\epsilon\). Figure 8: Strain contour diagram of the critical layer monitored by fiber optics. Figure 7: BOTDA strain diagram. Based on distributed fiber optic sensing data from the physical modeling experiment, the critical stratum maintains strain magnitudes below 4000 \(\mu\varepsilon\) prior to rupture initiation. Quantitative analysis establishes 4000 \(\mu\varepsilon\) as the transitional strain threshold between continuous and discontinuous deformation regimes. When monitored strain values remain below this threshold, the stratum exhibits continuous elastic behavior; exceedance of this value triggers discontinuous deformation mechanisms, including fracture propagation and structural disintegration. Four distinct measurement sequences were acquired at face advances of 124 cm, 164 cm, 192 cm, and 232 cm during the complete deformation evolution of the critical horizon. The vertical displacement coordinates were derived from the distributed sensing data. As evidenced in Figure 10, the computed results demonstrate a strong correlation with the geodetic monitoring data, maintaining deviation margins within 15%. This research reveals critical quantitative parameters, including a threshold strain value of 4000 \(\mu\varepsilon\) marking the transition from continuous to discontinuous deformation, peak strains reaching 10,000 \(\mu\varepsilon\) at 104 cm face advancement, and characteristic bimodal strain distributions of 10,487 \(\mu\varepsilon\) and 12,056 \(\mu\varepsilon\) at 168 cm advancement. The critical horizon, identified as siltstone, demonstrates mechanical properties with ultimate compressive  
Figure 10: Subsidence cloud map of the critical layer monitored by fiber optics. Figure 9: Subsidence cloud map of the critical layer monitored by the total station. strength of 295 KPa and elastic modulus of 77 MPa, yielding a theoretical ultimate strain threshold of 3830 \(\mu\epsilon\). This study establishes a reliable deformation prediction model based on continuous, homogeneous, and isotropic elastic medium assumptions, validated through four measurement sequences at different face advancements (124 cm, 164 cm, 192 cm, and 232 cm), maintaining deviation margins within 15% of geodetic monitoring data. These findings provide invaluable practical guidance for mining safety monitoring, influence range assessment, and support system optimization, while the dual monitoring methodology offers a robust framework for similar engineering applications.
## 4 Algorithms
### Tree Estimation  
Once the dissimilarity value \(d_{i,j}\) has been computed for every \((i,j)\), the algorithm analyzes the resulting dissimilarity matrix \(D\) in order to estimate the phylogenetic tree.  
1. Starting from the matrix \(D\), build an undirected graph \(\mathcal{G}=\{\mathcal{V},\mathcal{E}\}\) with \(N\) nodes, where the \(i\)-th node is associated with the audio track \(x_{i}(n)\) and each edge \((i,j)\) exists if and only if \(d_{i,j}<+\infty\) and \(d_{j,i}<+\infty\).
2. Run a maximal clique algorithm on \(\mathcal{G}\), obtaining \(\mathcal{C}_{1},\ldots,\mathcal{C}_{K}\subseteq\mathcal{V}\).
3. Compute the \(K\times K\) clique-dissimilarity matrix \(D_{\mathcal{C}}\) as: \[D_{\mathcal{C}}(p,q)=\frac{1}{|\mathcal{C}_{p}||\mathcal{C}_{q}|}\sum_{i\in \mathcal{C}_{p},j\in\mathcal{C}_{q}}d_{i,j}\] (9) where \(|\cdot|\) denotes the cardinality of a clique.
4. Starting from the matrix \(D_{\mathcal{C}}\), build a complete directed graph \(\mathcal{G}_{\mathcal{C}}=\{\mathcal{V}_{\mathcal{C}},\mathcal{E}_{\mathcal{C }}\}\), with \(K\) nodes, where every node is a clique of the undirected graph \(\mathcal{G}\) and each edge \((p,q)\) has a weight equal to \(D_{\mathcal{C}}\), corresponding to the average dissimilarity between the audio tracks belonging to the \(p\)-th and the \(q\)-th cliques.
5. Compute the phylogenetic tree as the minimum spanning arborescence \(\hat{\mathcal{G}}_{\mathcal{C}}=\{\mathcal{V}_{\mathcal{C}},\hat{\mathcal{E}} _{\mathcal{C}}\}\), i.e., the directed rooted spanning tree with minimum weight. \[\hat{\mathcal{E}}_{\mathcal{C}}=\arg\min_{\mathcal{E}^{s}\subset\mathcal{E}_ {\mathcal{C}}}\sum_{(p,q)\in\mathcal{E}^{s}}D_{\mathcal{C}}(p,q)\] (10) In our implementation, \(\hat{\mathcal{G}}_{\mathcal{C}}\) is found via the Chu-Liu/Edmonds optimum branching algorithm [37; 38].
# Research and Application of an Improved Sparrow Search Algorithm
## 3 Methods  
### Sparrow Search Algorithm  
The sparrow search algorithm (SSA) [30] was proposed in 2020 and was inspired by the predation and scouting warning behavior of sparrows in nature. Sparrow populations are categorized into discoverers, followers, and scouts according to different classes. Discoverers are dominant in the sparrow population as they have a better position in the search space and usually provide information about the location of food for the population, whereas the follower always monitors the finder: when the finder finds food, the follower will immediately follow. And the scout is observing the situation around the food. Once there is a danger, the monitor will immediately issue an alarm. When the alarm signal reaches a certain threshold, the whole population will move under the leadership of the finder. The discoverers update strategy is as in Equation (1):  
\[x_{i,j}^{t+1}=\begin{cases}x_{i,j}^{t}\cdot\exp\left(\frac{-i}{a\cdot\text{ iter}_{\max}}\right)&\text{R}_{2}<ST\\ x_{i,j}^{t}+Q\cdot L&\text{R}_{2}\geq ST\end{cases} \tag{1}\]  
\(x_{i,j}^{t}\) represents the position of the sparrow in the j-dimension at generation \(t\), \(a\) is a random number between \([0,1]\), \(Q\) is a normally distributed random number, \(L\) is a \(1\times d\) unit matrix. \(\text{R}_{2}\) is the warning threshold, \(ST\) is the safety threshold. When \(\text{R}_{2}<ST\), it means that the sparrow did not find the predator, and the individual sparrow carries out a wide range of searching behaviors. When it is larger than \(ST\), the discoverer needs to lead the population to fly to a safe place to forage. The follower update strategy is as in Equation (2):  
\[x_{i,j}^{t+1}=\begin{cases}Q\cdot\exp\left(\frac{x_{\text{worst}}^{t}-X_{i,j} ^{t}}{2}\right)&i>\frac{n}{2}\\ x_{p}^{t+1}+\left|x_{i,j}^{t}-x_{p}^{t+1}\right|A^{+}\cdot L&i\leq\frac{n}{2} \end{cases} \tag{2}\]where \(x_{p}^{t+1}\) is the current discoverer's optimal position, \(x_{\text{worst}}^{t}\) is the globally worst position, and \(A^{+}\) is a matrix where the elements are random values from 1 to \(-1\).  
Scouts are randomly generated from the population, which is about 10 to 20 percent of the total population, and the scout position update formula is as follows:  
\[x_{i,j}^{t+1}=\begin{cases}x_{\text{best}}^{t}+\beta\cdot\left|x_{i,j}^{t}-x_ {\text{best}}^{t}\right|&f_{i}>f_{g}\\ x_{i,j}^{t}+K\cdot\left(\frac{\left|x_{i,j}^{t}-x_{\text{worst}}^{t}\right|}{ \left(f_{i}-f_{\text{w}}\right)+\varepsilon}\right)&f_{i}=f_{g}\end{cases} \tag{3}\]  
where \(\beta\) is a normal random number obeying a mean of 0 and a variance of 1, \(K\) is a random number in the range \([-1,1]\), and \(\varepsilon\) is a normal number preventing the denominator from being zero.
# Property-Based Semantic Similarity Criteria to Evaluate the Overlaps of Schemas
## 5 Computation of Coverage and Flexibility
### Handling of Is-a Relationship
#### 5.2.2 From Subclass to Superclass  
As shown in Figure 3, it is assumed that entity types \(\{A_{1},A_{2}\ldots A_{k}\}\) are all subclasses of entity type A in a schema. If entity types \(\{A_{1},A_{2}\ldots A_{k}\}\) have properties \(p_{i}(i=1,\ldots,k)\) pointing to entity type B, then we create a property \(p\) pointing from entity type A to entity type B. Here, \(p\) is only used to calculate weights, so the specific meaning of \(p\) is not considered. However, in theory, the common parent property of the original k edges can be extracted. In addition, we assume that these k properties are different, otherwise they should be refined into properties of super classes entity type A to entity type B before building a schema.  
On the semantic level, this operation is reasonable. If every subclass of entity type A has a relationship with entity type B, it means that entity type A and entity type B also have a certain relationship. When calculating the weight of an entity type A, the influence of entity type B should be considered together. In the computation, if each subclass of entity type A has a property pointing to entity type B, then \(|L_{A}|+1\)_and_\(|\mathrm{L}|+1\) in Formula (4). The weight of entity type A will be larger.  
In the process of migrating a property from superclass to subclass, there are two special cases that need to be dealt with. As shown in Figures 4 and 5, suppose that in a schema, entity type \(A_{1}\) is a subclass of entity type A, there is a property \(p_{1}\) between entity type A and entity type B (only in Figure 5) is a subclass of entity type B.  
Figure 4: Special case \(1\) of is-a relation processing.  
Figure 3: Convert subclass property to superclass.  
1. As is shown in Figure 4, if there is property \(p_{2}\) between \(A_{1}\) and B and \(p_{2}\) is a subclass of \(p_{1}\), there is no need to create a new property from \(A_{1}\) to B. In other cases, we create a new property \(p\) from \(A_{1}\) to B;
2. As is shown in Figure 5, if there is property \(p_{3}\) between \(A_{1}\) and \(B_{1}\), and \(p_{3}\) is a subclass of \(p_{1}\), there is no need to create a new property \(p\) from \(A\) to \(B\). In other cases, we create a new property from \(A\) to \(B\).  
After preprocessing, if the is-a relationship in the original schema is well handled, then the weight is calculated according to Definition 2.
###### Abstract  
Cross-media communication underpins many vital applications, especially in underwater resource exploration and the biological population monitoring domains. Water surface micro-amplitude wave (WSAW) frequency detection is the key to cross-media communication, where the WSAW frequency can invert the underwater sound source frequency. However, extracting the WSAW frequency information encounters many challenges in a real environment, such as low precision and symbol synchronization, leading to inaccurately estimating the WSAW frequency. Thus, this paper proposed a WSAW frequency estimation method based on an improved RELAX algorithm, incorporating two improvements. First, adding a nonlinear filter to the RELAX kernel function compensates for the filtered gain and enhances the WSAW frequency precision. Second, the improved RELAX kernel function is combined with the generalized inner product method to obtain the time distribution of the non-stationary signals, which is convenient for decoding. Several simulations and experiments applying our method on a Ka-band frequency modulated continuous wave (FMCW) radar demonstrate that our algorithm attains a better performance than traditional methods, e.g., periodogram and the RELAX algorithm. Using the improved algorithm affords to extract the frequency information of the WSAW signal accurately with a short sampling duration, further improving the performance indicators of the communication system, such as communication rate.  
water surface micro-amplitude wave; improved RELAX; FMCW radar; nonlinear filtering; generalized inner product 2022
## 2 Simultaneous Segmentation and Detection Network (SSADNet)
### Input Data Preprocessing
#### 2.1.2 Point Cloud Top View Image  
Generally, the input type of deep learning network for image processing consists of three channels. Each pixel of a normal three-channel image consists of information on the RGB color. However, since the type of data from LiDAR are different from the type of data in an image, we transformed the LiDAR data to image-type data. Each channel is configured using information from the point cloud data as shown in Figure 2.  
Using the point cloud data, we created a new three-channel input for the proposed network. We generated three channel inputs using values of distance, reflection intensity, and height normalized within the range of \([0,255]\) for LiDAR point cloud data, and these are applied as input data for training the network.  
The first channel of the point cloud top view image computes the distance information on the x-y plane of the 3D LiDAR coordinate system as in Equation (1). The second and third channels contain the reflection intensity and the height values (z-coordinate on the 3D LiDAR coordinate system) of the point cloud, respectively.  
\[\text{Distance}=\sqrt{x^{2}+y^{2}} \tag{1}\]  
The size of the point cloud top view image is associated with the ranges of x and y of the point cloud. The resolution of a 3D LiDAR sensor decreases as the distance increases; thus, the area of a point cloud in this study is limited for conversion to a top view image. The \(x\)-axis on the 3D LiDAR coordinate system ranged from 6 m to 46 m, while the \(y\)-axis ranged from \(-10\) m to 10 m. In other words, the \(x\)-axis and the \(y\)-axis have a range of 40 m and 20 m, respectively. Furthermore, considering different heights of various vehicle types, the \(z\)-axis ranged from \(-2.5\) m to 2.5 m for a 5 m range. Therefore, a top view image is generated only using a point cloud that is within the range of \(x\in[6,46]\), \(y\in[-10,10]\), and \(z\in[-2.5,2.5]\). One pixel of the image represents a gap of 0.1 m. The generated point  
Figure 1: The configuration of a point cloud.  
Figure 2: The configuration of point cloud top view image.  
cloud top view image is \(200\times 400\times 3\) (width \(\times\) height \(\times\) channel). Figure 3 shows the top view image of the generated point cloud.
# Extending UWOC System Applications through Photon Transmission Dynamics Study in Harbor Waters
###### Abstract  
Underwater wireless optical communication (UWOC) in harbor waters can facilitate real-time monitoring underwater instruments for environmental monitoring, underwater inspection, and maintenance tasks. This study delves into the complex dynamics of UWOC in four distinct harbor waters. The research employs Monte Carlo method incorporated with Fournier-Forand scattering phase function for simulating photon transmission. Key parameters such as the Transmitted full divergence angle, received aperture, and Field of View (FOV) are meticulously evaluated for their impact on power loss and time delay spread. Notably, the normalized power loss and time delay spread are found to be more significantly affected by communication distance than water quality, and the traditional Beer-Lambert law is ineffective in harbor water. The power loss of Harbor II, III, and IV are found to be 14.00 dB, 31.59 dB, and 41.59 dB lower than that of Harbor I, and the time delay spread of Harbor II, III, and IV is 30.56%, 9.67%, and 0.49% times that of the Harbor I under certain conditions. In addition, increasing the received aperture and FOV, particularly over longer distance, make little contribution to reduce the power loss and mitigate the time delay spread. Based on the fixed transmitted full divergence angle, the most applicable received FOV range is 1-3.2 rad, and the most ideal received aperture is 0.15-0.4 m. Under these conditions, the variation in normalized power loss is less than 2 dB. Additionally, the time delay spread remains within the same order of magnitude with the attenuation length (AL) held constant. These conclusions hold substantial technical relevance for the engineering design of UWOC systems in harbor waters.  
UWOC; harbor water; divergence angle; received aperture; FOV; normalized power loss; time delay spread +
Footnote †: journal: Applied  
Article
# Elements of Hyperstructure Theory in UWSN Design and Data Aggregation
## 2 Mathematical Model
This is because there is no need for the special element \(s\) and we do not distinguish between communication within and between clusters. The operation \"\(\cdot\)\" defined by Equation (2) reduces to \(a\cdot b=c\) (we still suppose that it is commutative) and, consequently, the hyperoperation Equation (1) reduces to \(a*b=\{a,b\}\cup[a\cdot b]_{\leq}\), in both cases for all \(a,b\in H\). **Lemma 5**. : [1] _If we are able to uniquely identify \(CH_{k}\) in Equation (2), then \((H_{CH},\cdot,\leq)\) is a partially ordered semigroup._  
Finally, what is \(x\in[a]_{\leq}\)? This means that \(a\leq x\), i.e., that the data from the element \(a\) reach the element \(x\). Thus, if \(x\) is a sink, than the fact that \(x\in[a]_{\leq}\) means that the data from \(a\) can be successfully collected. What we want is that, if we denote \(S\) the set of all sinks, for all \(a\in H\) there exists at least one \(x\in S\) such that \(x\in[a]_{\leq}\), which means that data from all elements of our network \(H\) can be successfully collected. Of course, in order to achieve this, it is crucial to have an algorithm for unique determination of \(CH_{k}\) in Equation (2). Yet clustering algorithms such as the Distributed Underwater Clustering Scheme (DUCS) [3] or Low Energy Adaptive Clustering Hierarchy (LEACH) protocol can provide this.
## 5 Conclusions  
The use of far-UVC radiation with a specific wavelength of 222 nm for disinfection can have a significant negative effect on the mechanical and visual properties of the surfaces to be disinfected. The results of the study showed that far-UVC radiation at 222 nm causes significant color degradation in all the polymeric materials tested. The degree of color degradation varies depending on the type of polymeric material and the duration of exposure to far-UVC radiation. An obvious color difference was observed on FRC and PVC materials, where \(\Delta E00\) values of 6.431 and 7.194, respectively, were obtained after 290 J/cm\({}^{2}\) radiant exposure.  
When evaluating the effect of far-UVC radiation on the mechanical properties of the materials, it was observed that the modulus of elasticity increased for the PVC samples tested and decreased for the FRC material samples. It was also observed that the modulus of elasticity decreased faster for FRC 0/90\({}^{\circ}\) than for FRC \(\pm\)45\({}^{\circ}\). The largest decrease in elongation at break and elongation at ULS is seen in the tensile test results of a \(\pm\)45\({}^{\circ}\)-oriented E-glass FRC, which decreased by 26% and 17%, respectively, after 290 J/cm\({}^{2}\) radiant exposure.  
The results of the study show that when designing far-UVC disinfection in public transport, it is very important to pay attention to its effect on materials. Therefore, in future work, it is important to find the optimum time for far-UVC disinfection to minimize negative impact on materials. These results can guide the development of safe and effective disinfection protocols that minimize damage to the materials and ensure the longevity of the disinfected surfaces.  
Conceptualization, D.D., M.K., A.T., Z.L., A.M., R.D., J.G., D.S. and V.J.; data curation, D.D., M.K., A.T. and Z.L.; formal analysis, D.D., M.K., A.T., Z.L. and J.G.; funding acquisition, [PERSON] and R.D.; investigation, [PERSON], [PERSON], A.T. and Z.L.; methodology, [PERSON], M.K., A.T. and Z.L.; project administration, [PERSON] and R.D.; resources, [PERSON], M.K., A.T., Z.L., D.S. and V.J.; software, [PERSON], M.K., A.T., Z.L. and A.M.; validation, [PERSON], M.K., A.T., Z.L., A.M., R.D., J.G., D.S. and V.J.; visualization, [PERSON], M.K., A.T. and Z.L.; writing--original draft, [PERSON], [PERSON], A.T. and Z.L.; writing--review and editing, D.D., M.K., A.T., Z.L., A.M., R.D., J.G., D.S. and V.J. All authors have read and agreed to the published version of the manuscript.  
This research has received funding from European Regional Development Fund (project No 13.1.1-IMT-K-718-05-0002) under grant agreement with the Research Council of Lithuania (LMTLT), funded as the European Union's measure in response to the COVID-19 pandemic.  
Not applicable.  
The data that support the findings of this study are available within the article.  
The authors declare no conflict of interest.
# A Review of Medication Adherence Monitoring Technologies +
## 4 A Review of Medication Adherence Monitoring Systems
### Fusion-Based Systems
#### Proximity-Visual Systems  
A blend of RFID sensors and video camera has been used in [87; 141] to characterize the medication taking activity in an in-home environment. In this work, medication bottles were equipped with RFID tags and stored in a medicine cabinet that embeds an RFID reader. The RFID technology is employed for identification purposes of the medication bottles placed in the cabinet. However, once a bottle is removed from the medication cabinet and it is out of the coverage of the reader's antenna, the identification process using RFID technology can not be achieved anymore. As such, the vision system is used such that it is activated once the medication bottle moves out of the range of the reader. The camera is used for tracking and verifying the occurrence of medication taking based on moving object detection and color model of the bottle.