esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Bearing Characteristics of Composite Foundations with Permeable Concrete Piles
###### Abstract  
Permeable concrete piles, which combine the advantages of rigid piles and drainage consolidation techniques, have been widely applied in soil foundation treatment. In this study, the optimal mix proportion of the permeable concrete pile material was first determined through laboratory experiments; subsequently, based on the experimental results, numerical simulations were employed to investigate the load-bearing characteristics of composite foundations reinforced with permeable concrete piles under applied loads. The experimental results indicate that when the designed porosity is set between 20% and 35%, and the water-to-cement ratio is 0.3, the actual porosity closely approximates the design value, achieving a favorable balance between compressive strength and permeability. Numerical simulation results reveal that as the axial force in the permeable concrete piles attenuates with depth, the side friction of piles exhibits an overall increasing trend. Compared with impermeable piles, the pile-soil stress ratio and the load-sharing ratio of permeable piles gradually decrease under high loads; furthermore, the settlement and pile-soil stress ratio of the composite foundation is significantly influenced by factors such as pile length, pile diameter, cushion modulus, inter-pile soil modulus, and the modulus of the pile material.  
soft foundation; permeable concrete piles; bearing capacity; consolidation Article
## 4 Simulation Results
### Noise and Occlusion Attacks  
Images are susceptible to noise pollution during transmission. It is necessary to ensure that the encryption has a certain anti-noise attack capability. In this section, the multiplicative noise is added to the encrypted image to test the effects of noise. The multiplicative noise model is given by  
\[P^{\prime}\ =\ P(1+kG), \tag{22}\]  
where \(P\) and \(P^{\prime}\) denotes the encrypted image and the encrypted image with noise, respectively. G represents the white Gaussian noise with zero-mean and 0.01 standard deviation, \(k\) represents the noise strength.  
Figure 8 shows the decrypted results of the encrypted image with different strength noise. Figure 8a-c shows the results of the intensity \(k\) = 0.05, 0.07, 0.10, respectively, and the corresponding PSNR values are 30.96, 27.07, 24.36 dB, respectively.  
Figure 9a-c shows the decryption results of the ciphertext center being occluded by \(4\times 4\), \(6\times 6\), \(8\times 8\) pixels, respectively, and the corresponding PSNR values are 31.24, 25.51, 24.00 dB, respectively.
# A Compact Circular Rectenna for RF-Energy Harvesting at ISM Band
## 3 Rectenna Simulation Results and Discussion
### Monopole Antenna Simulation Results and Discussion
Furthermore, the calibrated S\({}_{11}\) of the fabricated antenna covers the ISM band and bandwidth of 3 GHz from 2.0 GHz to 5.0 GHz with \(-\)21.0 dB and \(-\)27.6 dB at 2.5 GHz and 4.73 GHz, respectively.
# Random forest Algorithm for the Classification of Spectral Data of Astronomical Objects
## 3 Results and Discussion
### Results and Comparative Analysis for the SDSS DR14 Dataset  
As previously specified in Section 2.1, the Sloan digital sky survey DR 14 dataset contains 4998 observations of the galaxy class (GALAXY), 850 of the quasar class (QSO), and 4152 of the star class (STAR). The imbalance ratio for this dataset is \(IR=5.88\).  
When running the proposed random forest algorithm on the SDSS DR14 dataset, the following confusion matrix is obtained (Figure 4):  
If we consider the case GALAXY as positive, the values of the three performance measures: sensitivity, specificity, and balanced accuracy, are calculated based on the values in the confusion matrix. For comparative purposes, the values of these four performance measures are included in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Algorithm** & **Conceptual Basis** \\ \hline [PERSON] & Calculation of probabilities using [PERSON] theorem \\ IB1 & Dissimilarity: HEOM and Euclidean distance \\ IB3 & Dissimilarity: HEOM and Euclidean distance \\ SVM (SMO) & Finding a kernel-based hyperplane \\ MLP & Artificial Neural Networks \\ \hline \end{tabular}
\end{table}
Table 1: Algorithms against which the random forest algorithm will be compared.  
The results obtained by the proposed random forest algorithm on the SDSS DR14 dataset are good in all four performance measures.  
The comparative table shows that the proposed random forest algorithm outperforms the other five state-of-the-art classifiers, considering GALAXY as the positive class.  
The same applies when changing the positive class. Although these two additional results were not included, in both cases, the superiority of the proposed random forest algorithm persists.  
It is relevant to note a significant fact: despite the high-class imbalance in the SDSS DR14 dataset, which deviates from the _IR_ 1.5 value specified for balanced datasets, the proposed random forest algorithm outperforms all others in the performance measure: balanced accuracy.  
These results are shown graphically in the bar chart of Figure 5 for balanced accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Naïve Bayes** & **IB1** & **IB3** & **SVM (SMO)** & **MLP** & **Random Forest** \\ \hline sensitivity & 0.9409 & 0.8581 & 0.8466 & 0.9080 & 0.9710 & **0.9792** \\ specificity & 0.9685 & 0.9251 & 0.9250 & 0.9433 & 0.9845 & **0.9940** \\ balanced accuracy & 0.9547 & 0.8916 & 0.8858 & 0.9256 & 0.9777 & **0.9866** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance measures (in %) for the SDSS DR14 dataset (best values in bold).  
Figure 4: Confusion matrix resulting from applying the proposed random forest algorithm on the SDSS DR14 dataset.  
Figure 5: Comparative bar chart of balanced accuracy values resulting from applying the six classification algorithms on the SDSS DR14 dataset. The proposed random forest algorithm is represented in pink.
## 2 Research Strategy
### Study Selection  
The study selection process was carried out in several stages to ensure rigor and precision. Initially, a selection was made based on the titles and abstracts of the articles identified in the search. Studies that met the inclusion criteria were selected for full-text review. To avoid the inclusion of duplicates, reference management tools, specifically the Mendeley reference manager, were used to identify and eliminate duplicate records. Study eligibility was assessed independently by two reviewers, who reviewed the full articles. Any discrepancies between reviewers were resolved through discussion and consensus, thus ensuring the inclusion of the most relevant studies.  
Searches of the PubMed, ScienceDirect, Scopus and Web of Science registers yielded 57, 631, 98, and 163 results, respectively. Following the removal of duplicates, 418 studies were screened by title and abstract, which yielded 106 studies for retrieval for full-text analysis. Among the excluded reports were those that only report the antioxidant activity of the CEO (e.g., [31]); these were excluded since the EO was not tested in meat or meat products. Reports that publish the use of EOs as food supplements for slaughtered animals (e.g., [32]) were also excluded. All these reports were retrieved, and 43 were included in the final review following full-text assessment. A PRISMA flowchart [33] depicting this process is displayed in Figure 1. The 43 journal articles included in this review are available in Supplementary Information Table S1 [21; 22; 23; 25; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72].  
\begin{table}
\begin{tabular}{c c c} \hline
**Criterion** & **Eligibility** & **Exclusion** \\ \hline Literature type & Journal (research articles) & Book, book series, chapter in a book, systematic review article or conference proceeding \\ Language & English & Non-English \\ Timeline & Between 1999 and 2024 & 1998 and earlier \\ Country/territory & World & \\ \hline \end{tabular}
\end{table}
Table 1: Keywords used for the process of finding relevant literature.
## 3 Design and Implementation Details
### Functionality and Fine-Tuning Explanation
Figure 7a,b illustrate such experiments, including exposure to direct sunlight (close to 60 \({}^{\circ}\)C) and to temperatures close to 0 \({}^{\circ}\)C. During these experiments, the maximum measured voltage difference, i.e., for the 2048 mV analog input fed by the Adafruit chip, did not exceed 2-3 mV, which is absolutely acceptable. Battery manufacturers recommend batteries not to be charged or even used at high temperatures because their performance and life will be shortened significantly [35]. Therefore, it was decided to modify node #A2, intended for ground installation, to place its battery inside an insulated enclosure underground. This arrangement is shown in Figure 7c. Figure 6: Indicative code parts implementing the soil instrument calibration function; exemplification for TEROS 12 instruments. The biggest challenge envisaged with the microcontroller programming was to implement an adequate set of features that fit inside the limited memory resources of the 32u4 module (especially its 2 MB RAM). Furthermore, hardware serial port utilization adjustments for DDI communication were preferable against the software serial port implementation counterpart, as the latter spent more power (especially its transmitting part during the sleep mode period) and as it occupied valuable additional RAM space. The analog multiplexer/demultiplexer 74 HC4052 chip can provide up to 20 mA and has an internal resistance equivalent of 80 \(\Omega\). In order to alleviate these limitations, two identical chips were soldered in parallel. As can be seen in the sensor node diagrams, the need to serve a fifth instrument, namely, an ML2 sensor, along with its increased power consumption (which is shown in detail in Section 4), resulted in adding a second charge pump unit dedicated to this instrument, apart from the main multiplexer/demultiplexer subsystem. Furthermore, the existence of an I2C interface on the 32u4 board favored the installation of auxiliary sensors (e.g., on the outside surface of the node #A2 box enclosure) like the Sensirion SHTC3 air temperature and humidity sensor, which is of low cost and is accurate, fast, and energy-efficient in its operation. Considering the practical arrangements related to the sensor node's flawless operation, one can go further by adopting more severe anti-lightning measures, like shielded metallic tubes hosting any wires above ground level, fast-melting fuses connecting sensitive elec  
Figure 7: Preparing against temperature-related imperfections: (**a,b**) node exposure to temperature extremes; (**c**) preparing the sensor node battery for underground installation. tronics with the exposed part of the equipment, and more powerful battery and transceiver settings. In any case, the cost and the installation difficulty factors should be respected.
## 2 Review
As the information management center of the entire system, the message middleware must be a considerableamount of data exchange when facing massive data, so it still has deficiencies in throughput, delay, reliability, etc. With the continuous development of Internet technology, distributed message middleware came into being to solve the shortcomings of traditional message middleware in these scenarios. Different from traditional message middleware, distributed message middleware has significant advantages in data bandwidth, processing performance, reliability, fault tolerance, etc. due to its distributed architecture, enabling it to efficiently and reliably handle massive message data transmission. Its main advantages are as follows:  
1. Entity decoupling: Producers and consumers do not need to know each other; they only need to communicate and interact with the message middleware. 2. Time decoupling: Producers and consumers do not need to be online simultaneously to participate in the communication. 3. Synchronous decoupling: The producer or consumer publish/subscribe infrastructure does not need to synchronously block the creator or consumer execution thread, allowing the maximum utilization of the processor resources of the creator and consumer. Apache Kafka is a popular open-source distributed stream processing platform widely used to build network data flow platforms [20]. As a publish/subscribe system for network data flow, Apache Kafka provides a highly scalable and fault-tolerant solution with the potential to easily process large amounts of data [21]. [PERSON] [22] proposed a data collection and stable exchange system based on Apache Kafka to address the problems of production bases scattered around the world and information exchange difficulties. [PERSON] [23] and [PERSON] [24] conducted a comprehensive performance analysis of the Apache Kafka platform. The experimental results show that it has scalability, distribution, and reliability with high throughput.
## 4 Mobile Localization Algorithms
### Centralized Localization Algorithms
#### 4.1.2 Energy-Efficient Ranging Scheme (EERS)  
In stationary UWSNs, unknown nodes communicate with anchor nodes to estimate the distances between them, from which their positions can be deduced. However, in mobile UWSNs, because of sensor nodes' uncontrollable mobility, it is impractical to assume that unknown nodes are always in the communication range of fixed anchor nodes. In order to localize a swarm of sensor-equipped drifters that float freely with ocean currents, an Energy-Efficient Ranging Scheme (EERS) is proposed in [39].  
First, range can be deduced from the one-way time of exchange message arrival measurement. This step is called Sufficient Distance Map Estimation (SDME). SDME consists of a synchronization step (SDME-S), followed by a distance estimation step (SDME-D). SDME-S is a synchronization-data collection algorithm to achieve time synchronization. SDME-D is a two step distance estimation process. During distance estimation, not all the nodes need to broadcast localization message. Therefore, SDME-D first selects the subset nodes that need to broadcast localization message. The localization based on EERS is an anchor-free self-localization scheme, which can be extended for large scale UWSNs. Although time synchronization is required to handle the one-way ToA, SDME-S can tackle the time synchronization problem efficiently. SDME does not need to be \"a full localization\" scheme. While submerged, the drifters only need to collect distance estimates. The positions can be calculated after the mission is over. Therefore, energy consumption for position calculation can be saved and  
Figure 7: The Absolute Positioning Scheme.  
complex computation is avoided. However, the subset nodes selection requires a single broadcast per node, which consumes huge energy and prolongs localization time.
## 2 Perfect-Reconstruction Cosine-Modulated Filter Banks  
A \(M\)-channel maximally decimated filter bank as illustrated in Figure 1 is composed of \(M\) finite impulse response duration analysis filters with impulse responses \(h_{k}(n)\), the down- and up- sampling by factor \(M\) of sub-band signals, and synthesis FIR filters \(g_{k}(n)\). The impulse response of filter bank is obtained by cosine modulation with a low pass filter:  
\[h_{k}(n) = 2h(n)\cos[\frac{\pi}{M}(k+\frac{1}{2})(n-\frac{V}{2})+(-1)^{k} \frac{\pi}{M}], \tag{1}\] \[g_{k}(n) = 2h(n)\cos[\frac{\pi}{M}(k+\frac{1}{2})(n-\frac{V}{2})-(-1)^{k} \frac{\pi}{M}] \tag{2}\]  
with \(0\leq k\leq M-1\) and \(0\leq n\leq N-1\). \(h(n)\) is the impulse response of the FIR prototype filter, \(V\) denotes the system delay and \(N\) is the order of the prototype filter. The input-output relation of the system in the \(z\) domain is given by  
\[\hat{X}(z) = A_{0}(z)X(z)+\sum_{l=1}^{M-1}A_{l}(z)X(ze^{-j2\pi l/M}) \tag{3}\]  
where  
\[A_{0}(z) = \frac{1}{M}\sum_{k=0}^{M-1}H_{k}(z)G_{k}(z) \tag{4}\]  
is the distortion transfer function which determining the distortion of non-aliased component \(X(z)\) caused by the system, and  
\[A_{l}(z) = \frac{1}{M}\sum_{k=0}^{M-1}H_{k}(ze^{-j2\pi l/M})G_{k}(z) \tag{5}\]  
are the aliasing transfer functions which determine the attenuation of aliased components \(X(ze^{-j2\pi l/M})\) for \(l=1,\ldots,M-1\). It follows that the filter bank holds the perfect-reconstruction property if and only if \[A_{0}(z) = z^{-V}, \tag{6}\] \[A_{l}(z) = 0,\quad l=1,\ldots,M-1. \tag{7}\]
## 2 Materials and Methods
### Data Processing
#### 2.2.4 Centred Log-Ratio Transformation  
The vector space in which the n-component data resides becomes a monomorphic space, and because monomorphic spaces are subject to fixed sum constraints, traditional statistical analysis methods for ordinary data are no longer applicable to component data. A review of the literature shows that analysis of monomorphic spaces often has the following three problems [31]:  
1. The intuitive form of the data differs between monomorphic and euclidean space and cannot be interpreted across space;  
Figure 2: Process Results.  
2. The covariance matrices of the component data calculated on the monomorphic space are significantly biased negative, with very different connotations from those on the Euclidean space;
3. The lack of parametric distribution of the component data on the monomorphic space makes it difficult to model the variational patterns of the data for analysis.  
Based on the problems mentioned above, this study performs a centred log-ratio transformation (\(clr\)) on the data, which can more fully reflect the characteristics of the components after the central logarithmic ratio transformation and make the component data more interpretable. \(clr\) is calculated by the formula shown in Equations (2) and (3),  
\[clr(x)=[\ln\frac{x_{{}_{1}}}{g(x)},\ldots,\ln\frac{X_{D}}{g(x)}] \tag{2}\]  
\[g(x)=\sqrt[]{x_{{}_{1}}\cdot\ldots\cdot x_{D}} \tag{3}\]
## 2 Zinc Binding Groups in Chelation Therapy
### Inhibition of Matrix Metalloproteinases
[53]. that the N-hydroxyurea, in contrast with the analogous hydroxamate, coordinates the catalytic zinc ion in a monodentate fashion, rather than a bidentate one, and with high out-of-plane distortion of the amide bonds. Therefore, N-hydroxyurea showed a decrease in binding affinity compared to hydroxamate analogs. This profile was explained through conformational differences: N-hydroxyurea prefer the trans-conformation of the N\({}_{1}\)-CO amide bond, which is unfavorable for zinc chelation, while hydroxamate moiety generally adopts a more favorable cis-conformation of the N\({}_{1}\)-CO amide bond. This led researchers to introduce some modifications, such as N\({}_{3}\)-methylation, in order to prevent the trans-conformation and obtain, finally, zinc chelation [55]. Finally, [PERSON] et al. evaluated the inhibition activity of 40 phenolic fragment compounds against MMP-2, -8, -9, and -14, which could be new ZBG with enhanced selectivity and affinity for Zn\({}^{2+}\). Phenols containing hydroxy and amino moieties in ortho- or para-position are effective MMP inhibitors, such as catechol (Figure 16) which showed a very interesting activity on all tested MMPs with a good selectivity towards MMP-2 [56]. Unfortunately, subsequent studies of the same group demonstrate the instability of catechol as well as a probable monodentate interaction of this moiety with Zn\({}^{2+}\) in MMPs pocket [57].
## 3 Description of the Test Specimens and Test Setup
### Test Set-Up and Loading Scenarios  
Each cage was tested under different lifting and tilting configurations, as shown in Figure 6. Two overhead cranes were used for these experiments. At each pick-up point, the two top pick-up bars were lifted by a lifting strap connected to the crane hook. The lifting process was slow and gradual to avoid dynamic movements in the cage.  
In test scenario 1, pick-up points were located 2.64 m (104.0 in) from each cage end, which is equal to one-fifth of the cage length. This test scenario represents the most common practice for rebar cage lifting and handling. Test scenario 2 was similar to scenario 1 but with a concentrated load applied to the center of the cage. The concentrated load was applied with a winch connected to the ground and monitored using an axial load cell placed between the specimen and the winch. The load was applied to simulate an accidental loading scenario during transportation or the handling of the cage at a construction site and was used to further investigate the response of the cage. An example of such an accidental load is the weight of an ironworker who may step onto a rebar cage. According to the ASCE standard \"Design Loads on Structures during Construction\", the concentrated load applies to the center of the cage with a load factor of \(C_{p}\) = 1.6. Therefore, for an ironworker weighing approximately 100 kg, the concentrated load should not be less than \(100\times 9.81\times 1.6=1570\) N [8]. In scenario 3, the rebar cage specimen was picked up by the far ends of the cage. Although this is not a common practice for picking up rebar cages, this loading scenario provides valuable data for numerical model validations. Test scenario 4 was similar to scenario 3, with a concentrated load added at the mid-span. In scenario 5, the cage was picked up by two points in the middle of the span, 2.44 m (8 ft) apart from each other. In scenario 6, the cage was picked up by the center. Test scenarios 5 and 6 do not represent the most common practice in rebar cage lifting and were exclusively performed to collect additional data for numerical model validation. In scenario 7, the cage was picked up from one end and one-fifth of the cage length from the other end and tilted up to about 40\({}^{\circ}\) to evaluate its behavior during site tilting. The test matrix is presented in Table 3.  
Figure 5: (**a**) One of the cage specimens under construction; (**b**) bending in the U-bolt connector plates in a tight-fit connection.
## 3 Beam Layout and Frequency Allocation Optimization Method Based on User Distribution
### Number of Users in the Coverage Area for a Satellite  
The user distribution under the satellite beam, as shown in Figure 3, can be equivalently expressed as independent user densities \(p_{i}\) and \(q_{i}\) for the center and edge regions, where \(p_{i}\) corresponds to the user density within a circular area with radius \(r_{i}\). The number of users \(U_{i}\) in the \(i\)th beam can then be expressed as  
\[U_{i}=U_{c}^{i}+U_{s}^{i}=p_{i}\pi r_{i}^{2}+q_{i}\pi(R_{0}^{2}-r_{i}^{2}) \tag{1}\]  
The total number of users wait for access to the satellite \(U_{total}\) is  
\[U_{total}=\sum\limits_{i=1}^{K}U_{i}=\sum\limits_{i=1}^{K}p_{i}\pi r_{i}^{2}+q _{i}\pi(R_{0}^{2}-r_{i}^{2}) \tag{2}\]  
In the \(i\)th beam, when \(0<R_{i}\leq R_{0}\), the relationship between the average distribution density of users in the center of the beam \(\rho_{i}\) and frequency assignment center area radius \(R_{i}\) is  
\[\rho_{i}=\left\{\begin{array}{l}p_{i},R_{i}\leq r_{i}\\ \left(p_{i}\pi r_{i}^{2}+q_{i}\pi(R_{i}^{2}-r_{i}^{2})\right)/\pi R_{i}^{2},R _{i}>r_{i}\end{array}\right. \tag{3}\]