esa-sceva/satcom-chunk-collection huggingface dataset:
# Message Action Adapter Framework in Multi-Agent Reinforcement Learning
## 4 Methods
### Message Action Adapter Framework
#### 4.2.2 Step 2: Communication with Messages  
After training the base policies for all agents, the agents communicate to leverage the observed environment information to construct messages. These messages are further applied to cooperative agents to make final actions. We train a message generator \(g_{i}\), message interpreter \(f_{i}\), and action adapter \(\phi_{i}\) which will be combined to base policy network \(\pi_{i}^{base}\). The message generator inputs any information for the agent to construct a message and outputs a vector \(m\in\mathbb{R}^{d_{\text{msg}}}\). The number of cooperative agents of agent \(i\) is not fixed. To process these messages, a usual choice of aggregation function of messages is a recurrent neural network (RNN), but other pooling methods or attention mechanisms, such as TarMAC, can be applied. We use notation \(\hat{m}_{i}\) to represent the aggregated messages agent \(i\) received.  
The message generated by an agent also feeds into the agent. This process can be viewed as a self-talk. Finally, the action adapter \(\phi_{i}\) inputs the current observation and the aggregated messages to make residual action logits  
\[\log\hat{\phi}_{i}(a_{i}|o_{i},\hat{m}_{i})=\log\pi_{i}^{base}(a_{i}|o_{i})+ \log\phi_{i}(a_{i}|o_{i},\hat{m}_{i}), \tag{4}\]  
where \(\hat{\phi}_{i}\) is a message-adapted policy of \(\pi_{i}^{base}\) (in short adapter policy). In training the adapter policy, the parameters of the base policy could be either updated or not, but the messages do not directly affect the base policy. We provide a pseudo-code of the action computation in Appendix A. Although we formulate a message adapter with a policy, the q-value estimator in an off-policy setting can be formulated similarly by adding a residual term of q-values. The adapted action in MAAF is  
\[a_{i}^{adapt}\sim\hat{\phi}_{i}(a|o_{i},\hat{m}_{i}) \tag{5}\]  
and the contribution of the message can be quantified with a measure \(d(\cdot,\cdot)\) as follows:  
\[d\Big{(}\hat{\phi}_{i}(a_{i}|o_{i},\hat{m}_{i}),\pi_{i}^{base}(a_{i}|o_{i}) \Big{)}. \tag{6}\]  
For example, when we choose KL divergence for the measure, the contribution of a message is the discrepancy between adapted policy \(\hat{\phi}\) and adapter \(\phi\):  
\[D_{KL} \Big{(}\hat{\phi}_{i}(a_{i}|o_{i},\hat{m}_{i})||\pi_{i}^{base}(a_ {i}|o_{i})\Big{)} \tag{7}\] \[=\sum\hat{\phi}_{i}(a_{i}|o_{i},\hat{m}_{i})\log\frac{\hat{\phi} _{i}(a_{i}|o_{i},\hat{m}_{i})}{\pi_{i}^{base}(a_{i}|o_{i})}\] (8) \[=\sum\hat{\phi}_{i}(a_{i}|o_{i},\hat{m}_{i})\log\phi_{i}(a_{i}|o_ {i},\hat{m}_{i})\] (9) \[=\operatorname{H}\Big{(}\hat{\phi}_{i}(a_{i}|o_{i},\hat{m}_{i}), \phi_{i}(a_{i}|o_{i},\hat{m}_{i})\Big{)} \tag{10}\]  
where \(\operatorname{H}(\cdot,\cdot)\) is the cross entropy of two probability distributions. Hence, the difference between the message-adapted and base policies can be written as the difference between the adapted policy and the residual part.
## 2 Review Approach
### Conducting the Review Phase
#### 2.2.1 Research Questions (RQs)  
The choice of objective-driven RQs is paramount to achieving the research's objectives. Hence, we identified, examined, and presented a set of research questions related to our review study. The following are our developed RQs:  
* RQ1: What are the security challenges of IIoT?  
The objective of RQ1 is to identify and broadly classify the security challenges in an IIoT ecosystem, as well as the corresponding diverse security approaches by the research community.
* RQ2: What kinds of learning algorithms are being deployed toward the security of IIoT?  
The objective of RQ2 is to determine the extent to which ML, deep learning (DL), and blockchain techniques have changed the narratives of security challenges in the IIoT ecosystem. This includes a set of evaluating datasets used by the researchers and the bottlenecks in the deployment of the algorithms.
* RQ3: What security enhancement could the quantum mechanism offer the IIoT ecosystem?  
The objective of RQ3 is to advocate for an alternative security measure, such as quantum mechanisms, in the IIoT ecosystem through the state-of-the-art (SOTA).
* RQ4: Is hybrid quantum-classical more efficient against IIoT security challenges than a single technique deployment?  
The objective of RQ4 is to determine a better deployable technique to combat the increasing security menace in the IIoT environment.
## 3 Results and Discussion
### Bioactive Compound
#### 3.2.2 Total Flavonol  
The interaction type of treatment by storage duration significantly impacted the TPC (\(p\leq 0.05\)), as shown in Figure 3. Total flavonol content of fruit samples treated with AIEW showed a higher flavonoid content with an increase from 48.65 mg QE L\({}^{-1}\) (day 0) to 55.71 mg QE L\({}^{-1}\) at (day 9). Fruit samples under the different treatments exhibited a progressive increase in total flavonoid concentration on day 6; however, by the end of day 9 of storage, the control samples had decreased. This resulted from the fruit's entire surface accumulating phenolic compounds due to the mechanical damage subjected to the whole fruit after cutting, as reported by [PERSON] et al. [22] for fresh-cut kiwi slices. Apple slices treated with AEW and AIEW had no significant difference at the end of day 6 of storage. The relative influence was AEW > AIEW > control for total flavonoid content.  
[PERSON] et al. [23] found that treating fresh-cut eggplant t with SAEW increased total flavonoids on day 9, showing 11.31% increase with respect to tap water. Similar observations were confirmed by [PERSON] et al. [24] for juibbe fruit treated with slightly acidic EW. The authors reported an initial increase in total flavonoids for treated juibbe fruit until day 30,  
Figure 3: Effects of AEW and AIEW on total flavonol concentration of fresh-cut ‘Granny Smith’ apples during storage at 2 \({}^{\circ}\)C and 95% RH for 9 days. Error bars represent standard deviation (SD) of mean values of treatments (n = 3) tested using Duncan multi-range test at 95% confident interval (\(p\leq 0.05\)) and different lower-case letter indicate significant difference in means (\(p<0.05\)). Similar lower-case letters are not significantly different. Continuous dashed line indicates baseline measurement. The descriptions of treatments: AEW = acidic electrolyzed water (200 mg L\({}^{-1}\)); AIEW = alkaline electrolyzed water (200 mg L\({}^{-1}\)).  
and SAEW treatment helped maintain relatively high total flavonoids compared to other treatments at the end of storage. [PERSON] et al. [25] reported that AEW treatment inhibited the degradation of flavonoids in the pericarp of longan fruit during storage.  
Increased ACC is known to further modify the flavonoid concentration under EW treatments by regulating essential enzymes and phenylalanine ammonia-lyase (PAL), which are involved in phenylpropanol metabolism [12; 13]. Flavonoids are important indices for evaluating nutritive properties of fruit and contribute to minimizing decay development and improving resistance [16]. The above results indicated that both AEW and AIEW treatment could retain higher contents of flavonoids for fresh-cut 'Granny Smith' apples during cold storage.
## 2 Overview
### Marchetti Dilatometer: Conceptualization
#### 3.1.7. Triaxial UU  
The undrained, unconsolidated triaxial tests were performed in saturated specimens and tested according to the ASTM D2850 [22]. The saturation of these samples was controlled before their compression with the Skempton value \"B\", which must be greater than 0.95 to consider that one sample is saturated [34]. In addition, to define the confinement applied to the specimens, the results presented in Figure 11 were used, because according to [34] it was appropriate to use the effective in situ stress in the first specimen and the following samples must have been confined using a double stress of the sample tested before.  
The ASTM D2850 mentions that to develop this test, three specimens should be used, and if necessary, at least two to obtain a correct analysis of results [22]. However, given the lack of material or that it was not suitable to be molded (Figure 14), certain tests were performed using only one specimen.  
However, soft soils, such as clay, when subjected to the Triaxial UU test tend to present a completely horizontal Mohr-Coulomb envelope, which means that the soil is purely cohesive since the friction angle is equal to 0. It occurs because the material does not drain, and therefore, the applied load is supported only by the water pressure in the pores [22]. Consequently, considering this behavior, in particular cases, just one specimen was tested and used the value of the radius to obtain the undrained shear strength (\(C_{u}\)). The results of \(C_{u}\) are presented in Figure 15a.  
Elasticity modulus in soils is one of the most difficult parameters to determine; different authors state that its calculation is based merely on predictions using the materials theory. For this reason, there are several methods to find it: initial tangent, secant, recharge, discharge, and cyclic; each of these is based on drawing a line on the curve and calculating the slope of this to find the modulus [35]. The initial tangent model was used since it was commonly used to represent the modulus of undrained soil due to the elastic response that was evident near the origin of the curve. Nevertheless, this does not mean that it was the optimal model when determining this parameter [36]. The results of the elasticity modulus are presented in Figure 15b.  
Figure 14: Samples that were not suitable to be molded.
## 3 Software and Experiment
### Design and Architecture  
Principally, MATLAB application for RBS/C measurements and evaluation was used over the existing accelerator software control system, ARGUS (Figure 6). The control of ARGUS and the Tandetron accelerator system subsequently was carried out using the batch (BTC) files with settings such as the ion beam start, the position of sample-goniometer, irradiation dose, and the automatic movement of the goniometer during spectrum acquisition.  
The system applied both modes leading to the desired parameter determination. This was done automatically by the algorithm or manually by minor user corrections if needed (e.g., by selecting minima).  
Coarse measurements and evaluations of the written program were characterized by the partial steps starting from the BTC file generation to perform experiments, ending with the projections of planar axes (intersection) together with axial axis offset calculations (Figure 7). All the procedures, except the measurements in ARGUS, were carried out in our MATLAB application, which included:  
* Data extraction from
* mpa files into
* dat files while data processing.
* Setting region of interests for measured RBS spectra.
* Fitting the data and locate minima.
* Determining planar axes rotation and getting coordinates of the axial axis (offset).
* Generating control configuration (BTC files) for fine measurement.  
Figure 6: Software architecture of designed system with MATLAB application.  
These outputs help defining fine channeling setup and performing more precise evaluation of the channel spectra including parameters calculation and resulting interpretation (Figure 8).
## 1 Introduction
However, their hardware structure still has room for improvement in terms of the power consumption, delay time, hardware area, and operation range. The architecture of the radix-4 Booth-encoding-based multifunction modulo (\(2^{n}\pm 1\)) multiplier proposed in this paper can achieve significant improvements in power consumption, latency, hardware area, and operation range. The proposed modular multiplication can greatly reduce the hardware cost, delay time, and power consumption, while having wider operation ranges compared to that of [PERSON] and [PERSON]'s method [8]. In this article, the circuit design and implementation are carried out using an FPGA (Field Programmable Gate Array) [3]. FPGA is a popular and important electronic device that can be easily corrected, reusable and reprogrammable by a designer. The advantages of FPGA are that it is easier to be implemented and configured by logic circuit, has a shorter time to develop a prototype circuit and has a lower price than conventional ASICs (Application Specific Integrated circuits). Based on these advantages, FPGA is widely used for circuit prototyping and product development. In the proposed FPGA-based multifunction modulo (\(2^{n}\pm 1\)) multiplier using the radix-4 Booth scheme, the abovementioned delay time, hardware area and power are simulated and verified using Xilinx FPGA (Field Programmable Gate Array) Vivado tools. The target chip is implemented using a Xilinx Artix-7 XC7A35T-CSG324-1 FPGA in the proposed work. The rest of this paper is structured as follows. Section 2 describes the methods reported in the literature. Section 3 presents the proposed radix-4 Booth-encoding-based multifunction modulo (\(2^{n}\pm 1\)) multiplier design, which is the area-delay-power efficient. Section 4 presents the results of the proposed method in comparison with those of various other methods. Finally, Section 5 summarizes the study.
# Error Investigation on Wi-Fi RTT in Commercial Consumer Devices
## 5 Conclusions and Future Works  
This study has fully explored, analyzed, and categorized the errors in Wi-Fi RTT distance with commercial consumer RTT-enabled smartphones and APs for mobile localization algorithms. The paper first summarized and compared the supporting hardware and software for development and research in recent years. Then, multiple experiments were conducted to investigate the errors in Wi-Fi RTT distance measurements with smartphones. Various experimental settings were made to evaluate the impact of hardware, motion status, and NLOS/LOS conditions on the errors in Wi-Fi RTT distance measurements. Through the comprehensive analysis of the experimental results in different sites, this paper summarized four errors in Wi-Fi RTT distance. The errors include hardware-dependent bias, blocker-dependent bias, fluctuations, and outliers. The hardware-dependent bias varies according to different hardware/manufacturers of the smartphones; the blocker-dependent bias is caused by NLOS effects; high fluctuations usually exist when the smartphone is moving, while the mixture of NLOS and motion effects results in severe outliers. In addition, experiments with different materials of blockers showed that all nonhardware-dependent errors increase when the blocker has better conductivity.  
Our future work will mitigate such errors to provide better distance estimation and positioning accuracy in complex indoor environments.  
Conceptualization, Y.D.; Methodology, Y.D.; Validation, D.S.; Formal analysis, Y.D.; Investigation, D.S.; Resources, Y.D.; Data curation, Y.D. and D.S.; Writing--original draft, Y.D. and D.S.; Writing--review & editing, T.A. and Y.Y.; Visualization, D.S.; Supervision, T.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data available on request from the authors.  
The authors declare no conflict of interest.
## 3 Methodology
### Collected Solar Data Analyses  
The collected solar data were analyzed. The purpose of the analysis in this stage is to determine which sensor readings have the greatest impact on predicting solar panel output voltage. These analyses are based on using a Multi-Layer Perceptron regressor (MLP regressor) in Python with the Scikit-learn package. The Scikit-learn package supports more than 25 regressors, but the MLP regressor is chosen for the initial analyses following the positive experience and good results in previous analyses and works. Although generally unsuitable for non-linear regressions, R\({}^{2}\) is the standard method in Scikit-learn library metrics, and it can show which used methods can have non-linear results and can be particularly indicative when R\({}^{2}\) has negative values. Also, a certain number of models used in further research belong to the linear regression model family. We used the R\({}^{2}\) score in combination with other parameters such as MSE and MAE to detect suitable regressors for further implementation of AI. The results presented in Table 1 show that the best estimation can be achieved with the Lux sensor (BH1750) in combination with the UV sensor (RMSE of 0.08, MAE of 0.24, and R2 score of 0.9650) and with the Lux sensor only (0.08, 0.22, and 0.97, respectively). The next single sensor with the highest accuracy is the UV sensor (0.1, 0.29, and 0.94, respectively).  
After the recognition of the estimation accuracy of various combinations of sensors separately, this research continues to explore the efficiency of the two most efficient sensors, BH1750 and UV. For this task, Python is used in combination with the Scikit-learn package.  
Figure 2: The solar data collection platform is based on Arduino.
## 4 The Proposed Magnetic Characterization Method
The magnetic field of the sample in the three axes has values B\({}_{\text{x1}}\) = 0.0045 \(\times\) 10\({}^{-6}\) T, By\({}_{\text{1}}\) = 0.0322 \(\times\) 10\({}^{-6}\) T, and B\({}_{\text{z1}}\) = \(-\)4 \(\times\) 10\({}^{-10}\) T for magnetometer 1 and values B\({}_{\text{x2}}\) = 0.0066 \(\times\) 10\({}^{-6}\) T, By\({}_{\text{y2}}\) = 0.0166 \(\times\) 10\({}^{-6}\) T, and B\({}_{\text{z2}}\) = \(-\)1 \(\times\) 10\({}^{-10}\) T for magnetometer 2. The sample magnetization value is found equal to 1.39 emu/gr using this methodology and taking into account the values provided by magnetometer 1, while the magnetometer 2 was used for validation purposes. The methodology is presented in detail in [130]. Figure 9: The experimental setup in the laboratory consisted of two fluxgate magnetometers and (**a**) the under-study sample, (**b**) the three-layer magnetic shield. Moreover, as long as the second experiment is considered, the magnetization and the position of the sample are calculated simultaneously, using the aforementioned values provided by the two magnetometers in tandem. Following the inverse problem solution as formulated in [131], the position is derived equal to 0.077 m, a location that corresponds to cell '4' of the grid with the same value on the magnetization, values in agreement with the previous experiment and the sample characteristics. It should be noted that since the magnetic field is a nonlinear function to the distance, solving the inverse problem and thus calculate all the aforementioned parameters (three magnetic moments and one position of the source) would require at least four measurements (non-correlated); this way both magnetometers are used in this setup. Figure 10: The magnetic field values, when the sample is present, are derived from the two magnetometers in (**a**) x axis, (**b**) y axis, and (**c**) z axis. Consequently, it is evident that the system in one arrangement or the other can predict with satisfactory accuracy the position as well as the magnetization of the sample. This fact can play a crucial role in catalyst characterization, in an uninterrupted catalytic operation, to detect the deactivation of the catalyst as it occurs.
# Effect of Methane Adsorption on Mechanical Performance of Coal
## 4 Discussion
### Effects of Methane Adsorption on Tensile Strength of Coal  
According to the results presented in Table 1, methane adsorption resulted in a significant reduction in coal compressive strength. This occurred because the structural reorganization and coal expansion caused after methane adsorption reduced the coal surface quality.  
\[\Delta\gamma=-\frac{RT}{MS}\int_{0}^{p}Qd(\ln p) \tag{5}\]  
where \(\Delta\gamma\) is the surface energy variation (J), \(R\) is the ideal gas constant (8.314 J / mol \(\cdot\)K), \(T\) corresponds to temperature (K), \(M\) indicates the molecular weight of methane, \(S\) represents the specific surface area of coal (m\({}^{2}\)), \(Q\) is the adsorbed amount (mL/g), and \(p\) is the adsorption equilibrium pressure (MPa).  
The tensile strength required to form a new fracture on coal surface is represented by:  
\[\sigma=\sqrt{\frac{4\gamma E}{\pi L}} \tag{6}\]  
where \(E\) is the elastic modulus of coal (MPa), \(\gamma\) corresponds to surface energy (J), and \(L\) indicates fracture length (mm).  
Combination of Equations (5) and (6) result in the mathematical expression used to calculate the variation of tensile strength required to form a new fracture on coal samples during metal adsorption (Equation (7)):  
\[\Delta\sigma=\sqrt{\frac{4 ERT\int_{0}^{p}Qd(\ln p)}{MS\pi L}} \tag{7}\]  
As shown in Equation (7), as equilibrium pressure for metal adsorption increases, surface energy decreases. This results in a reduction in coal strength and as a consequence,  
Figure 6: Reduction in elastic modulus vs. equilibrium pressure for methane adsorption.  
a decrease in the tensile strength required to form coal fractures. When the pores and fissures in the gas-bearing coal increase, the elastic modulus and the stress at the critical inflection point of brittle ductile failure decrease. To produce the same strain, the axial stress required for the specimen with more gas content is lower. The distribution of pore and fissure content significantly reduces the ability of coal to resist deformation failure.
# Research Progress of Dynamic Measurement Technology of Atom Gravimeter
## 4 Application Direction of Dynamic Measurement
### Gravity Field Mapping  
High-precision gravity field mapping not only helps to accurately study the shape and internal structure of the earth but also reveals the law of the occurrence and development of spatial physical events near the earth, which is of great significance for resource exploration, geophysics, navigation, and environmental monitoring. Vehicle-borne, ship-borne, airborne, and space gravimeters are important gravity field mapping methods that can measure the gravity of forests, mountains, lakes, oceans, and other areas that are difficult for people to reach. Traditional gravity mapping is based on the relative gravimeter. The elastic fatigue of the spring causes the zero drift of the gravimeter. It needs to return to the reference point regularly or compare and calibrate with the absolute gravimeter, which seriously affects its mapping efficiency. The realization of dynamic measurement by an atom gravimeter makes the mapping of long-period gravity fields more efficient, continuous, and accurate. Some research teams have carried out related experiments. For example, ONERA, mentioned above, used the same set of atom gravimeters to carry out the ship-board and air-borne experiments. Gravity reference maps were drawn for both tests, and compared with the existing gravity field database, the accuracy was high, reaching the level of mGal or even sub-mGal [35, 36]. It is expected that with the successful development of more and more atom gravity dynamic measurement equipment and the emergence of commercial products, gravity field mapping will be its primary application field.