esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Solution  
### Distributed Part of Media (DPoM) Management  
Content placement and caching at the network edge are carried out intelligently using a dynamic and distributive technique. The first part of the solution to the edge caching problem is presented in Algorithm 1, which depicts the steps for file splitting and placement. Firstly, DPoM performs content popularity comparison for any two randomly drawn files for all cacheable contents, then checks the size suitability for caching. For a file in \(SF\) range, there is no need for file splitting, so the entire file is placed in the main cache of a MAN server. For a file of size in the \(MF\) range, the splitter outputs two parts with the first part placed in the main cache. Similarly, a file in the \(LF\) range is split into multiple parts and the first part is cached. All remaining parts of split files are stored on the ROC for faster retrieval to the transient caches when users continuously engage their requested contents from the MAN servers. The MAN servers are represented as a set of knapsacks \(M=\{1,\ldots,m\}\) with capacities \(S_{M}=\{s_{m_{1}},\ldots,s_{m_{n}},\ldots s_{m_{N}}\}\), and the video contents as the set of \(n\) items, each having both profit of caching which is the request probability and weight (cost) which is the size of the item. Each item has a size which is divisible. The problem is to find the number of items to be put in each knapsack such that: (a) the total value of the assigned items is maximum; (b) the total size of items assigned does not exceed the capacity of the knapsack; and (c) the total number of the assigned items does not exceed the upper bound. Hence, the complexity of the proposed algorithm is \(O\big{(}n^{2}+nm\big{)}\).  
```
Input : New videos \(F=\{f_{1},f_{2},\ldots,f_{V}\}\) Output: Optimal content placement on clustered MAN servers
1: Initialize caches on MAN servers \(S_{M}=\{s_{m_{1}},\ldots,s_{m_{s}},\ldots s_{m_{N}}\}\)
2: while\(s_{m_{s}}^{u_{k}}\leq s_{m_{s}}\) draw files for popularity comparator
3: for\(f_{v}\)-\(popularity()==f_{v+1}\)-\(popularity()\)
4: if\(f_{v}\)-\(popularity()>f_{v+1}\)-\(popularity()\) do
5: switch(expression)
6: begin
7: case \(s_{f_{v}}\leq SF\) then
8: No file splitting required : Cache \(f_{v}\) on MAN server \(n\)
9: break
10: case \(SF<s_{f_{v}}\leq MF\) then
11: Split \(f_{v}\) into \(f_{v}^{1}\) and \(f_{v}^{2}\). Cache only \(f_{v}^{1}\) on MAN server \(n\)
12: break
13: case \(MF<s_{f_{v}}\leq LF\) then
14: Split \(f_{v}\) into h parts for each \(h\geq X\) GB. Cache only \(f_{v}^{1}\) in quality \(q\) on MAN server \(n\)
15: break
16: end if
17: end for
18: end while
```  
**Algorithm 1** DPoM File Splitting and Placement Algorithm
# Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
## 3 IoT ML Models  
Big data has a significant impact on productivity by using ML applications. Effective results can be obtained in various fields by using the Internet of Things (IoT) and machine learning (ML) in different applications within ML models. The IoT allows ML algorithms to collect data from physical devices and sensors and share them over the internet, learn patterns from these data, and make predictions on desired topics. By using IoT and ML together, smarter and more automatic decision-making mechanisms can be obtained in agriculture. This section highlights the importance of IoT data collection in agriculture. It demonstrates how these data can be leveraged through cutting-edge machine learning models, including neural networks and deep learning.
## 4 Simulation Results and Analysis
### Sinr  
In this evaluation, the number of D2D pairs and RBs are assumed to be \(N=8\), \(M=10\) and \(N=16\), \(M=20\). For different uplink states, we inspect the performance of terminals (D2D pairs and CUs) and the differences between two scale of the communication systems. The SINR of CUs and active D2D pairs are illustrated in Figures 4 and 5, respectively.  
By observing (**a**) and (**b**) in Figure 4, we can note that the SINR of CUs is approximately inversely proportional to the path-loss exponent \(\alpha\). That is, the smaller the \(\alpha\), the better the SINR performance of the CUs. Moreover, as \(\alpha\) is equal to 3 and 4, which means a good or normal uplink state, the cumulative distribution function (CDF) of SINR shows a tiny difference. It is clear that the SINR of \(\alpha=5\) is different from that of \(\alpha=3\) and 4. Nevertheless, when CUs share RBs with D2D pairs, the SINR of CUs are required to be larger than \(-10\)dB, the format requirements of modulation and coding scheme (MCS) would not satisfy the communication system if otherwise. As shown in Figure 4a,b, SINR of CUs less than \(-10\) dB for the proposed algorithm are \(1\%\) to \(4\%\) in different uplink states that satisfies the requirements of communication. For the differences between different scale of the system, the scale has little influences on the performance of CUs when comparing these figures.  
the diverse scale of communication system, the comparison of D2D pairs' performance between the case of (a) and (b) shows little differences as well. Thereafter, we can conclude that the scale of the system has little influences on the proposed algorithm noting the results obtained in Figures 4 and 5. Furthermore, it does not signify that larger \(\alpha\) can contribute to higher system throughput, and the reasons are twofold. The poor uplink state would decrease the SINR of CUs on one side, and some RBs cannot be reused under the constraints of the minimum SINR on the other side. The transmit power of D2D pairs would be limited in this communication environment. Based on this fact, the system throughput descends in the case of \(\alpha=5\) due to the decreased number of active D2D pairs. As depicted in Figure 6 for the scenario of (a), the number of active D2D pairs in different uplink states for the situation of \(\alpha=3\), \(\alpha=4\), and \(\alpha=5\) are about 7.8, 7.7, and 7.0, respectively.
# GRACE/GFO and Swarm Observation Analysis of the 2023-2024 Extreme Drought in the Amazon River Basin
## 3 Data and Methods
### Method
#### 3.2.6. Migration of the Center of Gravity  
The trajectory of the drought center of gravity through the spatiotemporal migration of the geometric center intuitively reflects the diffusion path and contraction trend of the drought range. By tracking the trajectory, researchers can identify the \"hotspot paths\" of drought propagation, and the spatiotemporal changes of the trajectory provide a mathematical basis for predicting the future expansion direction of drought [57]. The drought center of gravity formula is shown below [31]:  
\[X=\frac{\sum\limits_{i=1}^{n}GRACE-DSI_{i};X_{i}}{\sum\limits_{i=1}^{n}GRACE- DSI_{i}},Y=\frac{\sum\limits_{i=1}^{n}GRACE-DSI_{i};Y_{i}}{\sum\limits_{i=1}^{n} GRACE-DSI_{i}} \tag{19}\]  
where \(X\) and \(Y\) are the latitude and longitude of the center of gravity of the drought, respectively, and \(X_{i}\), \(Y_{i}\), and \(GRACE-DSI_{i}\), are the latitude, longitude, and GRACE-DSI value of grid points, respectively.
# Message Action Adapter Framework in Multi-Agent Reinforcement Learning
## 7 Results
### Adaption for Different Message Types  
Message channel types significantly affect cooperation performance. Although we may construct environment-specific message types, such as target locations in a predator-prey environment, manual construction of appropriate messages is non-trivial. This work considers three model-agnostic message types: observation, sampled action from the base policy, and probability distribution over actions. We concatenate messages to construct a unified-length representation for the combination of messages and reduce the dimension by 8. Figure 9 shows box plots of performances for MAAF-RNN. In the spread environment, there is little difference between different action types. This is because the environment does not depend on communication, as shown in Table 1, where IPPO, which has no communication, shows the best performance. In the case of the SMAC environment, simple message types, observations, and sampled actions show superior performance.  
To conduct an ablation study on the complexity of environments, we train MAAF-RNN for tag environments with different numbers of predator agents (named by Tag-\(n\) where \(n\) is the number of agents). The number of prey agents is fixed at 1. Figure 10 shows the performance for each environment for different message types. In the Tag-2 environment, message types of single elements \(\{o,a,p\}\) show superior performance than combined, such as \(\{ap\}\). On the other hand, the performance in the Tag-3 environment  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Method** & **F.C.** & **S.C.** & **V.S.** & **Spread** & **Tag** & **SMAC.3M** & **SMAC.8M** \\ \hline MAPPO & X & X & O & \(-66.65\) & \(116.87\) & \(18.18\) & \(\mathbf{17.95}\) \\ \hline IPPO & X & X & X & \(\mathbf{-62.86}\) & \(106.07\) & \(18.27\) & \(15.77\) \\ \hline Tarmac & O & O & X & \(-80.59\) & \(114.30\) & \(18.67\) & \(14.54\) \\ \hline MAAF-RNN (Ours) & X & O & X & \(-65.37\) & \(\mathbf{117.60}\) & \(\mathbf{18.80}\) & \(15.73\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average test return of four methods over 100 episodes. We highlight the best method in each environment with bold text. The first communication (F.C.) refers to whether messages are communicated during the first 50% of the training process, while the second communication (S.C.) indicates message passing during the last 50% of the training process. Value sharing (V.S.) refers to whether cooperative agents share observations in the value function of PPO. MAAF demonstrates an average improvement of 7.66% compared to the full communication approach used in TarMAC.  
increases as the message types become complex. The results indicate that searching for the best message input is necessary in communication.
# A Proposed Soft Computing Model for Ultimate Strength Estimation of FRP-Confined Concrete Cylinders
###### Abstract  
In this paper, the feed-forward backpropagation neural network (FFBPNN) is used to propose a new formulation for predicting the compressive strength of fiber-reinforced polymer (FRP)-confined concrete cylinders. A set of experimental data has been considered in the analysis. The data include information about the dimensions of the concrete cylinders (diameter, length) and the total thickness of FRP layers, unconfined ultimate concrete strength, ultimate confinement pressure, ultimate tensile strength of the FRP laminates and the ultimate concrete strength of the concrete cylinders. The confined ultimate concrete strength is considered as the output data, while other parameters are considered as the input data. These parameters are mostly used in existing FRP-confined concrete models. Soft computing techniques are used to estimate the compressive strength of FRP-confined concrete cylinders. Finally, a new formulation is proposed. The results of the proposed formula are compared to the existing methods. To verify the proposed method, results are compared with other methods. The results show that the described method can forecast the compressive strength of FRP-confined concrete cylinders with high precision in comparison with the existing formulas. Moreover, the mean percentage of error for the proposed method is very low (3.49%). Furthermore, the proposed formula can estimate the ultimate compressive capacity of FRP-confined concrete cylinders with a different type of FRP and arbitrary thickness in the initial design of practical projects.  
Keywords:FRP; soft computing; compressive strength; confined concrete; artificial neural network +
Footnote †: journal: Applied Mechanics and Physics Letters
# Environmental Behavior and Remediation Methods of Roxarsone
## 6 Remediation Methods of ROX Contamination
### Combined Degradation and Adsorption  
Removing ROX from a contaminated medium by adsorption often requires several hours for adsorption equilibrium to be reached. Organoarsenical is generally more difficult than inorganic arsenic to remove by adsorption [61]. Adsorbents, such as aluminum and iron, more strongly adsorb inorganic arsenic than organoarsenic [85], so first oxidizing organoarsenical to As(V) could improve the total arsenic removal efficiency achieved by adsorption. ROX is, therefore, usually removed by combining adsorption with degradation or oxidation. First, inorganic arsenic is produced through degradation or oxidation using light, then the degradation products are adsorbed to an adsorbent to effectively remove the arsenic completely. Combinations, such as photochemical oxidation + adsorption, chemical oxidation + adsorption, and heat treatment + adsorption, have been used, as shown in Table 2. Iron ore, Fe(II), and Fe(III) have been used in most combinations. Combinations of photodegradation and adsorption have been used to remove ROX. Examples include UV/Fe(III) [86; 87], Fe(II) + UV/chlorine [53], hematite + UV/oxalate [88], and UV/\(\alpha\)-FeOOH@GCA activated persulfate [89]. Oxalate can chelate to surface Fe(III) in hematite to give plenty of \(\bullet\)OH radicals through an oxygen activation process mediated by a surface Fe(III)/Fe(II) cycle under simulated solar light, and this can transform ROX into inorganic arsenic species that will adsorb to the hematite surfaces [88]. Almost 100% of ROX was transformed by \(\alpha\)-FeOOH@GCA activated persulfate in an UV irradiation system using light at a wavelength of 365 nm, and the As(V) released immediately adsorbed to the \(\alpha\)-FeOOH@GCA surfaces [89]. Bifunctional Co\({}_{3}\)O\({}_{4}\)-Y\({}_{2}\)O\({}_{3}\) with excellent catalytic and adsorption performances has been used to activate peroxymonosulfate to eliminate ROX and simultaneously adsorb secondary inorganic arsenic, with Co\({}_{3}\)O\({}_{4}\) acting as the primary catalyst and Y\({}_{2}\)O\({}_{3}\) as the main adsorbent [90]. ROX can be effectively oxidized by ferrite, and the arsenic can then be removed by the ferric nanoparticles that form through the oxidation reaction [61; 91; 92]. A thermal treatment combined with a chemical stabilization technique can degrade ROX efficiently and completely and also stabilize the products and effectively and safely remediate soil contaminated with ROX [93].
# Analysis of the Channel Influence to Power Line Communications Based on ITU-T G.9904 (PRIME)
###### Abstract  
ITU-T G.9904 standard, also known as PoweRline Intelligent Metering Evolution (PRIME), is a Power Line Communications standard for advanced metering, grid control and asset monitoring defined by the International Telecommunication Union (ITU). In this paper, an analysis about how different characteristics of the communication channel and types of noise might affect the system performance is carried out. This study is based on simulations of the PRIME physical layer using different channel characteristics and transmission parameters. The conclusions obtained are very valuable for better understanding the behavior of the ITU-T G.9904 (PRIME) standard in the field, allowing future improvements in deployment strategies and equipment design.  
electromagnetic noise; ITU-T G.9904; power line communications; PRIME; smart grid; smart metering +
Footnote †: journal: _Article_
## 2 IoTMADLab and Methodology
### IoT Network Reference Architecture  
The objective of the IoTMADLab IoT network reference architecture is to establish the requirements in terms of connectivity and semantics for achieving direct interoperability between IoT devices, which may be from different manufacturers, and even from different municipal services. The IoTMADLab focuses on the lower layers of the IoT ecosystem, i.e., at the network level, including the connectivity layer and the data model (see Figure 1).  
In IoT applications for Smart City, one of the necessary requirements is a reduced overhead in the information encoding and the use of lightweight communication protocols to reduce the amount of information transmitted and processing needs so that simple, low-cost, low-power devices can be used. It is also important that the data model and protocols used should be based on open, mature standards that are widely implemented, allowing the development of an ecosystem of products and projects that favors competitiveness and innovation.
## 3 Experimental Results and Analysis  
### Experimental Environment Configuration and Evaluation Metrics  
The experimental environment configurations are listed in Table 2.  
Indices used to evaluate the detection effect of multiple object categories include the mean average precision (mAP) and the average precision (AP) for each category. The AP is determined by the Recall rate (R) and the Precision rate (P), and it is an intuitive standard  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parametric** & **Configure** \\ \hline Operation System & Ubuntu18.04 \\ CPU & Intel(R) Xeon(R) CPU E5-2678 v3 @ 2.50 GHz \\ GPU & NVIDIA GeForce RTX 2080 Ti \\ Graphics Card Memory & 11 GB \\ Programming Language & Python3.8 \\ Graphics Acceleration Environment & CUDA 11.3, cuDNN 8 \\ Deep Learning Framework & Pytorch 1.11.0, torchvision 0.12.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental environment configuration.  
Figure 6: MFF structure diagram.  
for the model performance results of a single category. The F1 score takes into account both the precision and recall of the model.  
Recall is calculated using the equation:  
\[\text{R}=\frac{\text{T}_{\text{P}}}{\text{T}_{\text{P}}+\text{F}_{\text{N}}} \times 100\% \tag{6}\]  
Precision is calculated using the equation:  
\[\text{P}=\frac{\text{T}_{\text{P}}}{\text{T}_{\text{P}}+\text{F}_{\text{P}}} \times 100\% \tag{7}\]  
where T\({}_{\text{P}}\) is the number of correctly detected object objects, F\({}_{\text{N}}\) is the number of correctly detected object objects, and F\({}_{\text{P}}\) is the number of incorrectly detected object objects.  
The higher the AP and mAP scores, and the higher the FPS, the better the detection performance and speed of the model. The F1 score is also a measure of model performance, taking into account both precision and recall.
# Weak Signal Processing Methods Based on Improved HHT and Filtering Techniques for Steel Wire Rope
## 3 Principles and Methods
### The Proposed Method  
The flow diagram of the proposed method is schematically shown in Figure 4, staring with the data acquisition and pre-amplification for the wire rope testing signals, and the standard Hilbert transform algorithm is applied to obtain a phase changed signal. Then, the signals combining with the original signal are calculated by the differentiating technique. On account of the mixed frequency features of the processed signal, further signal denoising method based on the Fourier transform and lowpass filtering method is applied, aiming at the low frequency and weak defect signal. The lowpass cutoff frequency is mainly determined by the Fast Fourier Transform (FFT) analysis results. Additionally, to obtain a higher SNR of the detection signals, the moving average technique is applied. Generally, many factors would affect the improving of the weak detection signals, such as the averaging method, the moving span, as well as the lowpass cut off frequency. Obviously, different from the traditional single signal denoising method, the proposed weak signal processing methods for steel wire rope is a combination technique which integrates the phase transformation techniques of Hilbert transform and signal filtering methods. Consequently, the weak signals and defect are well distinguished.  
Figure 4: Flow diagram of the proposed algorithm and procedure. f and K are threshold values of the frequency and SNR.
# Implementation of a Cryogenic Facility for Space Debris Analysis
## 3 Prototype Design
### Mechanical Design  
A 3D CAD model is presented in Figure 14.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Standard Error** \\ \hline \(T_{sh}^{0}\) & \(-\)236.735 & 0.693 \\ \(A_{1}\) & 99.960 & 1.576 \\ \(\tau_{1}\) & 93.728 & 2.992 \\ \(A_{2}\) & 281.677 & 0.541 \\ \(\tau_{2}\) & 6974.883 & 44.291 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parameters determined for the fit of the dynamic simulation result.  
Figure 13: Cooldown fit.  
In Figure 14, the main parts are identified as follows:  
1. Thermal-mechanical switch Mechanical switches consist of a compressed-air pneumatic system using a flexible bellow as a linear vacuum feedthrough. The cold part is isolated by an epoxy with low thermal conductivity, used in cryogenics and vacuum applications. This system allows one to disconnect the cold source from the hot source mechanically.
2. Baseplate The baseplate is the part of the system where the resistors are attached, and the thermal connection with the cold finger is made on its back. The samples to be tested will be placed on this part.
3. Heater For the heater, we selected Kapton flexible foils [9] because they have a wide operational temperature and we have used them in the past for this kind of application. Kapton foil heaters are positioned between the sample holder and the baseplate.
4. Cold finger (heat pump) A helium cryocooler is used to cool down the baseplate. The cryocooler, at \(-150\)\({}^{\circ}\)C, can transfer 90 W. The section area of the copper strip can be customized to fit the desired power flux.
5. Thermal isolation Besides the isolation provided by the epoxies, an MLI blanket at room temperature is used to shield from the radiation emitted by the chamber walls.
## 2 Methodology
### Objective Function
#### 2.1.1 Spatial Objective Function \(J_{so}\)
An appropriate metric for the quality of the information present in both the East-West \(B_{ew}\) and North-South \(B_{ns}\) information for all spatial cells m is one where both the average as well as the uniformity of such information are optimized. This relation is shown in Equations (6) and (7). \[\mathbf{B}_{ew}= \begin{bmatrix}B_{ew_{1}}\\ \vdots\\ B_{ew_{m}}\end{bmatrix} ,\mathbf{B}_{ns}= \begin{bmatrix}B_{ns_{1}}\\ \vdots\\ B_{ns_{m}}\end{bmatrix}  \tag{6}\]  
\[ \begin{split} J_{ew}&=\frac{\overline{\mathbf{B}_{ew}}}{2}+ \frac{1}{2}\left(\frac{\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{m}\left|B_{ew_{ ij}}-B_{ew_{j}}\right|}{2m\sum\limits_{i=1}^{m}B_{ew_{ij}}}\right)\\ J_{ns}&=\frac{\overline{\mathbf{B}_{ns}}}{2}+\frac{1}{2}\left( \frac{\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{m}\left|B_{ns_{i}}-B_{ns_{j}} \right|}{2m\sum\limits_{i=1}^{m}B_{ns_{i}}}\right)\end{split}  \tag{7}\]
# Analysis of Small-Loop Electromagnetic Signals to Detect Subsurface Anomaly Zones
###### Abstract  
Recently, sinkholes have significantly increased in urban areas as a result of subsurface cavities and ground softening. In this study, a small-loop electromagnetic survey was conducted in a testbed where anomaly zones comprised of cavities, areas of ground softening, and underground facilities were simulated in the ground under road pavement using the CMD Mini Explorer. The equipment was only able to measure the electrical resistivity (ER) at three depths. As, occasionally, the equipment was unable to detect the anomaly zones using the ER measured at those three depths, survey measurements were taken at three heights to collect the ER at seven depths using the superposition method. The result shows that the anomaly zones can be detected qualitatively by visually observing the contour map of the ER. In addition, another method for detecting anomaly zones in the ground statistically using a box plot and the relative standard deviation was employed in this study. Consequently, the position and depth of the cavities, areas of ground softening, and underground facilities in the testbed can also be determined quantitatively.  
Keywords:small-loop electromagnetic exploration; cavities; ground softening; underground facilities; electrical resistivity; electrical conductivity +
Footnote †: journal: Applied Sciences