esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Sound Reduction Improvement
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Symbol** & **Wall 1 \({}_{\text{combi}}\)** & **Wall 2 \({}_{\text{combi}}\)** & **Wall 1 \({}_{\text{ISO}}\)** & **Wall 2 \({}_{\text{ISO}}\)** & \(\Delta R_{w,\text{combi}}\) & \(\Delta R_{w,\text{ISO}}\) & \(\Delta R_{w,\text{Weber}}\)[13] \\ \hline \(R_{w}\) (dB) & \(58\) & \(58\) & \(59\) & \(58\) & \(0\) & \(-1\) & \(8\) (6) \\ \(C\) (dB) & \(-2\) & \(-5\) & \(-2\) & \(-3\) & \(-3\) & \(-1\) & \(5\) (3) \\ \(C_{tr}\) (dB) & \(-7\) & \(-10\) & \(-6\) & \(-9\) & \(-3\) & \(-3\) & \(2\) (0) \\ \(C_{0.3130}\) (dB) & \(-3\) & \(-6\) & \(-3\) & \(-4\) & \(-3\) & \(-1\) & - \\ \(C_{05000}\) (dB) & \(-2\) & \(-5\) & \(-2\) & \(-3\) & \(-3\) & \(-1\) & - \\ \(C_{1000-3000}\) (dB) & \(-2\) & \(-4\) & \(-1\) & \(-3\) & \(-2\) & \(-2\) & - \\ \(C_{0.3130}\) (dB) & \(-11\) & \(-14\) & \(-10\) & \(-12\) & \(-3\) & \(-2\) & - \\ \(C_{tr,\text{0.5000}}\) (dB) & \(-11\) & \(-14\) & \(-10\) & \(-12\) & \(-3\) & \(-2\) & - \\ \(C_{tr,\text{1000-500}}\) (dB) & \(-7\) & \(-10\) & \(-6\) & \(-9\) & \(-3\) & \(-3\) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Airborne sound insulation values of Wall 1 and Wall 2 and their differences, expressed in terms of single number ratings extracted from microphone-based data and from combined microphone-vibrometry data. Values in brackets give results with taking to account the recommended standard deviation 2 dB of method. Figure 7: Spectra of the sound reduction index of Walls 1 and 2. (**a**) Comparison between the two measurement approaches in 1/3 rd octave bands. Green and black lines: sound insulation curves obtained by vibrometry; blue and red lines: sound insulation curves obtained by means of ISO 10140-2. (**b**) Spectrum in 1/48 th octave bands of the sound reduction index determined by applying a Rayleigh calculation on vibrometry data (green: Wall 1; black: Wall 2); combined spectrum (combi) of the two walls in 1/3 rd octave band (blue dashed: Wall 1; red dashed: Wall 2). Figure 8 also shows a prediction of the \(\Delta R\)-spectrum based on Equation (8), which was proposed by [PERSON] et al. The corresponding \(\Delta R_{\rm W}\) value overestimates the measured value by about 7-9 dB (Table 2), due to the \(\Delta R_{\rm W}\) spectrum overestimating the experimental data, especially above the coincidence frequency. The values in parentheses denote the average standard deviation (2 dB) of results presented by ISO standard [42]. However, the Cremer expression in Equation (13) [59] fits quite well for spectrum above the resonance of the ETICS system and up to the coincidence frequency of ETICS plaster finishing layer (theoretical coincidence frequency of 25 mm lime plaster layer: \(f_{c}\) = 1080 Hz):  
\[\Delta R_{f_{0}<\,f<f_{c}}\ =\ 20\log\left(m.\frac{f}{\left(\frac{\rho_{0}.c}{ \pi}\right)}\right)+10\log\left(\frac{f}{f_{c}}-1\right)+10\log(\eta)-2 \tag{15}\]  
with \(\eta\) the structural loss factor of the wall and \(\rho_{0}\) the density of air.
## 4 Performance Tests
### SELECT Operation
#### 4.3.1 Simple SELECT  
A simple select, that returns a country with id 222 is considered; for CouchDB, using both the first and second structures, the search is made as presented in Table 4.  
The times obtained in the case of searches are presented in Figure 7.  
\begin{table}
\begin{tabular}{c} \hline \hline
**RELATIONAL MYSQL: Simple select operation** \\ \hline @Query(“Select c from Country c where c.id =countryId”) \\ Country getById (@Param(“countryId”)long countryId); \\ Country country = countryRepository.getById(222); \\ \hline
**DOCOUNT-BASED MYSQL: Simple select operation** \\ \hline DocResult execute = collection.find(”_id = 222”).execute(); \\ \hline
**COUCHDB FIRST STRUCTURE: Simple select operation** \\ \hline Map \textless{} String, String \textgreater{} map = db.find(Map.class, “222”); \\ \hline
**COUCHDB SECOND STRUCTURE: Simple select operation** \\ \hline Map\textless{}String, String\textgreater{} map = db.find(Map.class, “222”); \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simple select operations.  
Figure 6: Performance time (ms) for the multiple update operation.  
In the case of this simple selection, the search in the CouchDB database is much faster than the relational MySQL but almost 50% slower than the document-based MySQL. In the case of using relational MySQL, response times are larger because it scans the entire table until it finds the item.
## 3 Results and Validation
### Parameterization Results  
After having validated the model of the curved beam with theoretical schemes and experimental measurements, attention was paid to the impact of the parameters described in Section 2 on the transmission capability.
## 3 Methodology
### EFAN Neck Network  
The YOLOv11 neck architecture integrates FPN and PANet for multi-scale feature fusion. As shown in Figure 4a, FPN propagates semantic information in a top-down manner to enrich multi-scale representations, while PANet reinforces localization capabilities by bottom-up aggregation. Although this structure improves detection accuracy, it suffers  
Figure 3: The framework diagram of ConvNeXtBlock module.  
from shallow information loss and feature blurring due to repeated sampling. Shallow features are crucial for small-target detection, as their high-resolution edge and texture details compensate for the positional inaccuracies of deep but low-resolution semantics. To address this, an enhanced feature fusion network termed as EFAN is proposed. As depicted in Figure 4, EFAN incorporates lightweight SCDown [36] convolutions and three novel feature fusion pathways to optimize the integration of shallow-deep features.  
To address the loss of feature map details during repeated sampling and fusion, three novel feature fusion pathways are designed. These pathways downsample features from the P2, P3, and P4 layers, then, respectively integrate them with the final outputs of P3, P4, and P5. By directly injecting low-level edge and texture details into high-level features, this design preserves fine-grained information critical for small-target detection. However, the added pathways carry the risk of excessive parameter expansion, which may dilute subtle but essential signals--particularly detrimental for PCB defect detection, which relies on high-resolution features. To mitigate this, SCDown, a lightweight downsampling module, is introduced to replace standard convolutions. SCDown employs point-wise convolution for channel adjustment followed by depthwise separable convolution for spatial reduction, significantly reducing computational costs while retaining downsampling fidelity. The calculation of the improved feature fusion is as follows.  
\[P_{3}^{out}=F_{1}^{Out}\oplus F_{2}^{Out}\oplus SCDown(P2), \tag{5}\]  
\[P_{4}^{Out}=F_{2}^{Out}\oplus P_{3}^{out}\oplus SCDown(P3), \tag{6}\]  
\[P_{5}^{Out}=F_{3}^{Out}\oplus P_{4}^{out}\oplus SCDown(P4), \tag{7}\]  
where \(F_{1}^{Out}\) denotes the output of the backbone network \(P3\) layer; \(F_{2}^{Out}\) and \(F_{3}^{Out}\) respectively, represent the output of feature fusions along the top-down pathway in EFAN; \(SCDown(P2)\), \(SCDown(P3)\) and \(SCDown(P4)\) respectively, denote the results of downsampling output of layers \(P2\), \(P3\), and \(P4\) using SCDown; \(P_{3}^{Out}\), \(P_{4}^{Out}\) and \(P_{5}^{Out}\) signify the output of the feature fusion network; and \(\oplus\) represents the feature fusion operation.  
In short, EFAN enhances shallow detail retention through supplementary fusion routes and counters parameter inflation via SCDown, thereby boosting model efficacy.  
Figure 4: Comparison of feature fusion structure before and after improvement. (**a**) Original network (FPN + PANet); (**b**) improved network (EFAN).
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
## 2 The Energy-Efficient Internet-of-Things Model
### The SAC Algorithm
#### 2.4.3 Step 3: Node Roles' Confirmation  
Finally, the base station informs the nodes about their roles in the network by using the signaling tree built during node discovery to broadcast roles and association messages. Using this process, each node is assigned its role in the network (CH or CM) and its next hop to the base station. The message content will be of the form \([ID,RL,NH,ST]\), where \(ID\), \(RL\), \(NH\) and \(ST\) represent, respectively, a node's ID, its role in the network (CM or CH), its next hop to the base station and its time slot for transmission. For each message received, a node is made aware of its role and next hop to the base station if the message ID of a node matches the ID field of the signaling message sent by the base station and, thus, adjusts its forwarding table to reflect the information received from the base station.
# Evaluation of Parallel Computing on MPI Version PHITS Code
## 3 Results
### MPI Performance Evaluation
#### 3.2.2 Speedup Factor and K-Factor  
When the number of processors was increased from 3 to 6, the maximum increase was 64.0% for 30 bch. From 6 to 9 and from 9 to 12, it increased by 17.6% each. For 300 bch, there were increases of 60.0%, 19.3%, and 19.8% as the number of processors increased, and for 3000 bch, the same trend was observed. However, for 30,000 bch, it decreased by 2.5% from 6 to 9. In general, the speedup factor showed a tendency to saturate along with the increased number of processors (Table 7, Figure 8).  
K-factor was calculated by the least squares method through the speedup factor and Equation (3) in order to evaluate atypical parts such as data processing and transmission/reception among the total operation time. The derived values were 0.402, 0.313, 0.259, and 0.258 for 30,000 bch, 3000 bch, 300 bch, and 30 bch, respectively. At the same time, there was a tendency to plateau as bch decreased (Table 8).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**No. of Processor**} & \multicolumn{2}{c}{**30 bch**} & \multicolumn{2}{c}{**300 bch**} & \multicolumn{2}{c}{**3000 bch**} & \multicolumn{2}{c}{**30,000 bch**} \\ \cline{2-9}  & **Avg** & **S.D** & **Avg** & **S.D** & **Avg** & **S.D** & **Avg** & **S.D** \\ \hline
3 & 1 & 0.01 & 1 & 0.01 & 1 & 0.01 & 1 & 0.01 \\
6 & 1.64 & 0.01 & 1.6 & 0.01 & 1.59 & 0.01 & 1.55 & 0.02 \\
9 & 1.93 & 0.02 & 1.91 & 0.01 & 1.8 & 0.02 & 1.51 & 0.01 \\
12 & 2.27 & 0.07 & 2.29 & 0.02 & 2.07 & 0.07 & 1.81 & 0.05 \\ \hline \end{tabular}
\end{table}
Table 7: Speedup factor. Based on three processors, as the number of processors increased to 6, 9, and 12, the comparison was made. In order to exclude the effect of the difference in performance between nodes, the entire processor was evaluated by adding one from each node. The results of five iterations were averaged. Avg means the average value at this time, and SD means the standard deviation of the measured values.  
Figure 7: Individual node performance was evaluated using only three processors each. The average value of five average CPU times required to repeat 3 \(\times\) 10\({}^{8}\) computational simulations for the PDD in a 10 cm \(\times\) 10 cm field size was analyzed.
## 4 Analysis of Numerical Calculation Results
### Influence of Soil-Rock Composite Stratum
#### 4.5.2 Pipe Jacking Structure Stress  
The polar coordinate distribution of peak stress was calculated, as shown in Figure 12. The stress of the upper and lower pipe jacking structures under two site conditions are compared and shown. It can be seen from the figure:  
1. The upper pipe jacking under the two site conditions is located in the soil layer, and the stress distribution and stress value of the pipe jacking are basically the same. The maximum value is located on the 45\({}^{\circ}\) symmetry axis, and the minimum value is located on the horizontal and vertical symmetry lines. The ratio of the maximum and minimum stresses is about 1.6, which also indicates that the circular pipe jacking structure is prone to cracking along the 45\({}^{\circ}\) symmetry axis. 2. The stress of the lower and upper pipe jackings in homogeneous stratum is basically the same distribution form, and the maximum stress is basically located at the 45\({}^{\circ}\) symmetry axis. The stress value of the lower pipe is about 1.5 times that of upper pipe due to the lower pipe being subjected to greater earth pressure. 3. Due to the large difference in the stiffness of soil and rock, the seismic wave at the soil-rock junction transmitted and reflected may form a certain seismic surface wave effect [24; 25]. The stress at the soil-rock interface, with an increase of 104% compared with the homogeneous soil stratum, and the stress distribution pattern of the lower  
Figure 12: Peak stress polar distribution of the pipe tunnel: (**a**) Upper pipe jacking; (**b**) Lower pipe jacking.  
pipe jacking under the two site conditions is quite different too. Therefore, the seismic energy acting on the pipe jacking structure increases at the soil-rock interface, which is more likely to cause the pipe jacking tunnel damage.
## 3 Methodology  
### Datasets  
In the context of our work, we train and evaluate our auto-encoder with two publicly available datasets, the DRIVE [23] and the STARE [24]. The DRIVE dataset is the acronym for the Digital Retinal Images for Vessel Extraction and has been used for comparative studies on the segmentation of retinal blood vessels. The images that the DRIVE dataset consists of have been obtained from a diabetic retinopathy program in Holland. In total, 40 images have been selected; more specifically, 33 of those images do not show any sign of diabetic retinopathy, while 7 show some signs of diabetic retinopathy. Specifically, these images were captured using a Canon CR5 non-mydriatic 3 CCD camera with a 45-degree FOV (Field of View). The plane resolution of DRIVE is 565 \(\times\) 584 pixels and a 24-bit grey scale resolution. The dataset's images have been appropriately cropped around the Field of View and a mask image is also provided that delineates the Field of View of each image. The 40 images we used to create two sets, the training and the test set, and each one of those two sets has 20 images. Also, for images of the training set, there is available a single manual segmentation of the vasculature of each image. So, the testing set has 20 images, some masks, and manually labeled vessel structures. Specifically, for the testing set images, two manual segmentations are given; one is used as golden-standard, and the other one aims to assist in comparing the segmentations of the computer method to those of an independent expert. In addition, a mask image is also available for each one of the retinal images and indicates the same region of interest. An experienced ophthalmologist participated in the study to instruct all human observers to segment the vasculature manually. They were requested to mark the pixels for which they were confident for at least 70% that these pixels were vessels.  
The STARE (Structured Analysis of the Retina) Project was created at the University of California in 1975. The project was supported by the U.S. National Institutes of Health [24]. Around thirty individuals from various backgrounds contributed to the project, including medicine, science, and engineering. The Shiley Eye Center at the University of California, San Diego, and the Veterans Administration Medical Center in San Diego provided the clinical data and images. The STARE dataset includes 20 colour fundus images with a resolution of 700 \(\times\) 605 pixels, captured using a TopCon TRV-50 fundus camera. The dataset also contains the manually labeled vessel structure for each image, with two sets of annotations provided by two experts in the field. The first set of annotations is considered to be the ground truth. Half of the images in the STARE dataset depict healthy retinas, while the other half depict retinas with various diseases..
# Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
## 2 Related Work
### Disaster Response
[31] take a similar approach and extract transition patterns from land use and land cover maps, generated from high resolution WorldView-2 images. Some works have explored the alternative strategy to directly analyse a single post-disaster image. [PERSON] and [PERSON] [40] forecast the direction of a cyclone's track at a given instant using satellite images; and [PERSON] and [PERSON] [28] predict hurricane-induced damage by detecting urban debris zones in high-resolution images.
# IRS, LIS, and Radio Stripes-Aided Wireless Communications: A Tutorial
## 3 System and Signal Characterization
#### System Model and Receiver Design of Receivers  
Figure 5 shows a reflector that assists communications. It looks at a communications system in which \(N\) reflecting components are added to a reflecting device to improve communication from a BS/AP with \(M\) antennas to \(K\) users with only one antenna.  
Figure 4: (**A**) lock diagram of OFDM/SC-FDE iterative FDE receiver. (**B**) IB-DFE receiver structure.  
Let \(G\in C^{M\times N}\), \(h_{r}\in C^{N\times 1}\) and \(h_{d}\in C^{M\times 1}\) denote the equivalent channels of the (AP/BS <<-> Reflector), (Reflector <<-> user) with \(k\) link.  
Then, the received signal of the \(k\)th user can be expressed as  
\[Y_{k}=\underbrace{h_{d}^{H}S_{k}}_{Direct\cdot Link}+\underbrace{h_{r}^{H} \theta^{H}GS_{k}}_{Reflected\cdot Link}+N_{k} \tag{1}\]  
where \(\theta\) indicates a matrix of \(N\times N\) diagonal reflection coefficient, which can be written as \(\theta=diag(\varphi_{1}.\varphi_{2},\ldots,\varphi_{n})\)), \(\varphi_{n}=a_{n}\exp(j\varphi_{n})\), \(a_{n}\in[0,1],\varphi_{n}\in(0,2\pi)\) and \(N_{k}\) denotes the additive white Gaussian noise (AWGN) at the receiver of \(k\)th user, with zero mean and variance \(\sigma_{k}^{2}\).  
The \(k\)th user's received signal-to-interference plus noise ratio (SINR) can be stated as  
\[SINR_{K}=\frac{\left|h_{d}^{H}+h_{r}^{H}\theta^{H}G\right|^{2}}{\left|h_{d}^{ H}+h_{r}^{H}\theta^{H}G\right|^{2}+\sigma_{K}^{2}} \tag{2}\]  
and the equivalent attainable rate, expressed in bits per second per Hz (bits/s/Hz),  
\[R_{k}=\log_{2}(1+SINR_{k}) \tag{3}\]  
with  
\[h_{d}^{H}+h_{r}^{H}\theta^{H}G=H_{k} \tag{4}\]  
\[Y_{k}^{(l)}=S_{k}H_{k}^{(l)}+N_{k}^{(l)} \tag{5}\]  
For evaluation of two different receivers, we need information about the feedforward and feedback matrices, where \(F_{k}\) denotes feedforward and \(B_{k}\) stands for the feedback matrices.  
Using the ZF algorithm, \(F_{k}\) becomes [42]  
\[F_{k}=\left(H_{k}^{H}H_{k}\right)^{-1}H_{k}^{H} \tag{6}\]  
For a linear MMSE-based receiver  
\[F_{k}=\left(H_{k}H_{k}^{H}+\beta I\right)^{-1}H_{k}^{H} \tag{7}\]  
\[\beta=\frac{\sigma_{N}^{2}}{\sigma_{S}^{2}}=\frac{E\left[\left|N_{k}\right|^{ 2}\right]}{2}\bigg{/}\frac{E\left[\left|\mathcal{S}_{k}\right|^{2}\right]}{2} \tag{8}\]  
where \(I\) denotes an appropriate identity matrix and \(\beta\) denotes the assumed identical values for all values.  
Figure 5: Block diagram of an IRS-assisted multi-antenna communication system.  
Using the MRC algorithm, \(F_{k}\) becomes  
\[F_{k}=H_{k}^{H} \tag{9}\]  
Using the EGC algorithm, \(F_{k}\) is given by  
\[F_{k}=e^{i*Arg(H_{k}^{H})} \tag{10}\]  
In IB-DFE receivers, the ideal feedforward and feedback coefficients are provided by  
\[F_{k}^{(L)}=\frac{H_{k}^{(l)*}}{\beta+(1-(\rho^{(i-1)})\sum\limits_{l^{\prime}=1 }^{L}\left|H_{k}^{(l^{\prime})}\right|^{2}} \tag{11}\]  
\[B_{k}^{(i)}=\rho^{(i-1)}(\sum\limits_{l^{\prime}=1}^{L}F_{k}^{(l^{\prime},l)}H_ {k}^{(l^{\prime})}-1) \tag{12}\]  
with  
\[\rho^{(i)}=\frac{E[s_{n}^{*}s_{n}^{(i)}]}{E[\left|s_{n}\right|^{2}]} \tag{13}\]
# A Systematic Compact Model Parameter Calibration with Adaptive Pattern Search Algorithm
## 5 Results
### Adaptive Pattern Search
#### 5.1.2 Parallel Exploration  
In an exploratory move, conventional HJ method explores (2M\({}_{\text{d}}\) + 1) number of points sequentially to decide searching direction, when M\({}_{\text{d}}\) is the number of dimensionalities, thus the length of parameter vector. It evaluates the ternary displacement \(-1,0,+1\) with the scale of searching step in each dimension independently and chooses the best displacement compared to the exploration basis (0 displacements for all dimension) [10,11]. The number of all possible directions with ternary displacement is the 3 to the power of M\({}_{\text{d}}\). In a high dimensional problem, conventional exploratory move explores only a part of possible directions, so it compromises accuracy to computational cost. However, evaluations in each direction can be fully parallelized with many-core machine, or even utilizes the thousands of cores in graphical processing unit. Along with the parallel implementation of evaluation, parallel exploration is substantially advantageous in sequential exploration since it leads the pattern move to the best direction among all possible choices in the ternary exploration. This is conceptualized in Figure 5.  
Figure 4: The benchmark results of adaptive pattern move and constant pattern move in the HJ method. To be fair in comparison, 10 samples of the target curves are randomly chosen and used on both implementations. The scale factor = 0.1 is configured in this benchmark. (**a**) The number of moves in exploratory move; (**b**) The number of moves in pattern move; (**c**) fitness distribution of the calibrated parameters; w.c. refers to worst case and m.c. refers to median case. A top/bottom edge of the whisker in the plot represents the maximum/minimum datum, respectively.  
Comparisons carried out between sequential exploration and parallel exploration is depicted in Figure 6. In terms of the parallel exploration, the number of moves both in the exploratory and the pattern move has improved. The number of exploration move is reduced by 7% in worst case and 86% in the median case by the parallel exploration, and the number of pattern moves is reduced by 89% in worst case and 97% in the median. The parallel exploration prevents the misjudgment of the direction in the exploratory move and makes the process converges much quicker than sequential one. Also, preventing the misleading to bad local optima, the fitness of the calibration is enhanced by 99% in worst case and 93% in median case. In short, parallel exploration with full searching in the direction enhances the searching time and fitness utilizing the parallelization of evaluation.
## 2 Materials and Methods
### Cardio Data Protection Procedure
#### 2.2.2 Compression  
The next step in the cardio data encryption procedure involves the application of optimized compression based on the Energy Packing Efficiency (EPE) method [27]. The chosen method of compression requires preliminary data processing (normalization of the studied sequence is carried out): first by amplitude and then by period. After this input processing, the EPE-based threshold processing itself takes place. At each subsequent level \(i\) of the wave decomposition of the data, the corresponding signal energy is calculated, using the current values of the detailed coefficients \(D(i,j)\), total \(K\) for the respective level:  
\[E_{Di}=\sum_{j=1}^{K}(D(i,j))^{2}. \tag{4}\]  
Compression based on the Energy Packaging Efficiency processing method reduces any excess information in the signal (but it does not remove the diagnostic properties of the signal) when processing the signal threshold, setting a zero value of insignificant wavelet coefficients (their presence in the studied sequence does not significantly affect the significance of the signal).