esa-sceva/satcom-chunk-collection huggingface dataset:
# Tunnel-Free Distributed Mobility Management (DMM) Support Protocol for Future Mobile Networks
## 3 Analytical Modeling and Performance Evaluation
### Data Packet Loss  
#### 3.4.1 Data Packet Loss with the Proposed Approach  
During the handover process, upon the absence of a buffer management technique deployed at the network, the data packets sent to the MN might get lost. The total data packet lost during a handover process can be defined as the sum of all the lost data packets sent from a CN to the MN. Let \(\delta_{P}\) represent the total data packet loss, which can be calculated using:  
\[\delta_{P}=\lambda_{S}S(L)T_{HO}, \tag{22}\]  
where \(\lambda_{S}\) represents the average session arrival rate at the MN's wireless interface, \(S(L)\) represents the average session length in packets, and \(T_{HO}\) is the handover latency for the proposed approach.
###### Abstract  
To effectively prevent land subsidence over abandoned coal mines, it is necessary to quantitatively identify vulnerable areas. In this study, we evaluated the performance of predictive Bayesian, functional, and meta-ensemble machine learning models in generating land subsidence susceptibility (LSS) maps. All models were trained using half of a land subsidence inventory, and validated using the other half of the dataset. The model performance was evaluated by comparing the area under the receiver operating characteristic (ROC) curve of the resulting LSS map for each model. Among all models tested, the logit boost, which is a meta-ensemble machine leaning model, generated LSS maps with the highest accuracy (91.44%), i.e., higher than that of the other Bayesian and functional machine learning models, including the Bayes net (86.42%), naive Bayes (85.39%), logistic (88.92%), and multilayer perceptron models (86.76%). The LSS maps produced in this study can be used to mitigate subsidence risk for people and important facilities within the study area, and as a foundation for further studies in other regions.  
Keywords:land subsidence; Bayes net; naive Bayes; logistic; multilayer perceptron; logit boost +
Footnote †: journal: Applied Sciences
# All-Digital Computing-in-Memory Macro Supporting FP64-Based Fused Multiply-Add Operation
## 2 Overview
### FP64 Data Format  
According to the definition of IEEE binary floating-point arithmetic standard (IEEE 754), FP64 represents a data format that uses 8 bytes, in total, for encoding and storage. The storage space of FP64 is divided into three parts, as shown in Figure 2. The highest bit that expresses sign is abbreviated to \(S\). The middle 11 bits, which express exponent, are abbreviated as \(E\), while the lowest 52 bits, which express fraction, are abbreviated as \(M\). The actual value of a normalized FP64 number \(N\) can be expressed as follows:  
\[N=(-1)^{S}\times 2^{E-1023}\times(1\times M) \tag{1}\]  
The actual exponent value is equal to the difference between \(E\) represented in floating-point format and an exponential bias. With this representation, all exponent values can be represented by unsigned integers, which makes it easier to compare the exponent sizes of two floating-point numbers. If the value of the exponential part in FP64 is between 0 and 2\({}^{11}\)-2, the most significant bit (integer bit) of the fractional part is 1, and \(N\) will be called normalized number. If the value of the exponential part in FP64 is 0 and the fractional part is non-zero, \(N\) will be called denormalized number. Generally, the denormalized number is used to represent numbers that are quite close to zero. IEEE 754 stipulates that the exponential bias of denormalized floating-point numbers is 1 less than that of normalized floating-point numbers [25; 26].
# Analysis of Blasting Efficiency Using Crack-Inducing Holes and Pre-Splitting Method in Blasting
## 5 Conclusions  
To summarize our findings, a comparison of the blasting effects between the combined blasting method using crack induction holes and pre-crack blasting, and the conventional Smooth Blasting method was conducted through full-scale blasting experiments at a railroad tunnel construction site. The propagation characteristics of ground vibrations and overbreak amounts were analyzed for both methods, leading to the following conclusions:  
1. Based on the analysis of the blast vibration reduction rate using the measured PPV for both conventional blasting and combined blasting, the vibration magnitude in combined blasting was found to be reduced by from 9.3% to 33.5% compared to conventional blasting.
2. Due to the pre-splitting effect during combined blasting, the blast vibration in the core section was reduced by 33.4%, while the vibration in the enlarged section, perimeter, and floor areas was reduced by 44.6%.
3. The amount of overbreak during combined blasting was reduced by 17.9%, and a comparative analysis of the overbreak cross-sectional area confirmed a reduction of approximately 20.2%.
4. An analysis of the blasting vibration transmission characteristics through time-differential analysis revealed that, in general blasting, the maximum vibration occurred at the expansion area. To reduce vibrations, it is recommended to apply controlled blasting in the peripheral area during blasting to block vibrations in the direction of sensitive structures.
5. When the crack induction hole and pre-splitting blasting methods are applied to underground tunnel blasting, it can reduce overbreak, lower reinforcement costs, and minimize vibration generation, thereby preventing damage to adjacent structures. This is expected to result in both direct and indirect economic benefits for the blasting operation.  
Data curation, J.-W.Y.; formal analysis, J.-W.Y.; investigation, J.-W.Y.; methodology, J.-W.Y.; project administration, J.-W.Y.; validation, J.-W.Y.; writing--original draft preparation, J.-W.Y.; conceptualization, S.S.L.; project administration, S.S.L.; supervision, S.S.L.; writing--review and editing, S.S.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) program (No. 4299990314624) through the National Research Foundation of Korea (NRF) funded by the Korea Ministry of Education.  
Not applicable.  
Data Availability Statement: Data is available upon request unless classified as confidential.  
The authors declare no conflicts of interest. The funders had no role in the design of this study, the collection, analyses, or interpretation of data, the writing of the manuscript, or the decision to publish the results.
###### Abstract  
The aim of the present study was to evaluate the contamination state of the surface soil from 10 parks from Galati, Romania, and the health hazards of the soil. The soil samples, collected in each site from the playing ground and from the edge of the park, were analyzed by using combined Wavelength-(WDXRF) and Energy-Dispersive (EDXRF) X-ray fluorescence techniques. A total number of 27 chemical elements (Ag, Al, As, Ba, Ca, Cd, Cr, Co, Cu, Fe, Hg, K, Mg, Mn, Na, Ni, P, Pb, Rb, Sb, Sc, Sn, Sr, Ti, V, Zn and Zr) were quantified in the urban soils, and the results were compared to the normal and alert values from Romanian legislation for toxic trace elements, as well as with European and world average values of element concentrations. The mineralogical analyses were performed by Scanning Electron Microscopy with Energy-Dispersive X-ray Analysis (SEM-EDX) and the Attenuated Total Reflectance-Fourier Transform Infrared technique (ATR-FTIR). To assess the soil contamination and the impact on human health of the presence of potential toxic elements and heavy metals in the soil, a series of pollution and health risk indices were used. All the results indicated an unpolluted to moderately polluted soil. The soil samples collected from the edge of the parks presented higher values for the specific pollutants, which originated from heavy traffic, such as Cu, Cr, Zn and Pb. The non-carcinogenic and carcinogenic risk to children was assessed using estimated daily intake (EDI) in relation to the pathways whereby pollutants can enter the human body, such as ingestion, dermal contact, inhalation and vaporization. Using the obtained values for EDI, the hazard quotient and hazard index were determined, which strengthen the formerly issued presumption that soil pollution is moderate and, by itself, does not present any threat to children's health.  
soil; playgrounds; parks; heavy metals; XRF; FTIR; SEM-EDX Article
# CPU-GPU-Memory DVFS for Power-Efficient MPSoC in Mobile Cyber Physical Systems
## 4 System Model and Problem Formulation
### Hardware and Software Infrastructure  
We chose the Odroid XU4 [8] development board to implement our CPU-GPU-Memory DVFS. Odroid XU4 employs the Samsung Exynos 5422 [9] MPSoC, which is popularly used in Samsung mobile devices, especially the Samsung Galaxy S5. The Odroid XU4 is a representational development board of the Galaxy S5 smart-phone. The Exynos 5422 MPSoC contains clusters of big (4 Cortex A-15) and LITTLE CPU cores (4 Cortex A-7). This MPSoC provides DVFS features per cluster, with the big CPU cluster having nineteen frequency scaling levels, ranging from 200 MHz to 2000 MHz with steps of 100 MHz, and the LITTLE CPU cluster having thirteen frequency scaling levels ranging from 200 MHz to 1400 MHz with steps of 100 MHz. Exynos 5422 comes equipped with a GPU cluster, called Mali-T628 MP6 GPU, consisting of six shader cores and has seven frequency-scaling levels as follows: 600, 543, 480, 420, 350, 266 and 177 MHz, respectively. This MPSoC supports 2 GB RAM, which has the following nine frequency scaling levels: 825, 728, 633, 543, 413, 275, 206, 165 and 138 MHz, respectively. DVFS in big and LITTLE CPUs in Exynos 5422 is performed cluster-wise and the voltage value for a particular frequency is fixed for that frequency. It should also be noted that below some frequencies, voltage remains the same, but above a certain point, the voltage increases linearly [37]. Examples of this include that for A7 (LITTLE) CPUs frequencies of 200-500 MHz have the voltage of 0.913 V, whereas for A15 (big) CPUs, frequencies of 200-700 MHz have a voltage of 0.9125 V.  
The Exynos 5422 MPSoC also has five temperature sensors, four of which are located on four big CPUs and one on the GPU. The Odroid XU4 board does not have an internal power sensor on-board; hence, Odroid SmartPower2 [38], which is an external power monitor with networking capabilities over WIFI, was used in this study to take power consumption readings.  
The Odroid XU4 was running on UbuntuMate version 14.04 (Linux Odroid Kernel: 3.10.105) and executing the performance governor. During the time of implementing and conducting our experiments the average ambient temperature of the room was 21 \({}^{\circ}\)C.
## 7 Applications of the Typical Medium DCA to Systems with Disorder
### Results for Models with More Realistic Parameters
#### 7.2.1 Off-Diagonal Disorder
As can be seen from Figure 34, while the single-site TMT does not change much under the effect of off-diagonal disorder, the TMDCA results are significantly modified. The bands for a larger cluster become highly asymmetric with significant widening of the A sub-band. The local \(N_{c}=1\) boundaries are narrower than those obtained for \(N_{c}>1\) indicating that the TMT strongly underestimates the extended states regime in both diagonal and off-diagonal disorder. On the other hand, comparing the mobility edge boundaries for \(N_{c}>1\) with those obtained using TMM, we find very good agreement. This again confirms the validity of our generalized TMDCA. Figure 34: Disorder-energy phase diagram of the A-B binary alloy model with off-diagonal and diagonal disorder. Parameters used are \(t^{AA}=1.5\), \(t^{BB}=0.5\), \(t^{AB}=1.0\), and \(c_{A}=0.5\). The mobility edges obtained from the TMT \(N_{c}=1\) (black dashed line), TMDCA \(N_{c}=3^{3}\) (green dot-dashed line), \(N_{c}=4^{3}\) (purple double-dot-dashed line) and \(N_{c}=5^{3}\) (red solid line), and the transfer matrix method (TMM) (blue dotted line). The single-site \(N_{c}=1\) strongly underestimates the extended states region especially for higher values of \(V_{A}\). The mobility edges obtained from the finite cluster TMDCA (\(N_{c}>1\)) converges gradually with increasing \(N_{c}\) and shows good agreement with those obtained from the TMM, in contrast to the single-site TMT. See the text for parameters and details of the TMM implementation. Reprint from [211].
# A Blockchain-Based Supervision Data Security Sharing Framework
## 4 Performance Evaluation and Analysis of Proposed Framework
### Analysis of Security Assessment
#### 4.2.4 Protocol Attacks  
This framework can effectively resist identity disguise attacks and replay attacks. The requester sends a file download request to the supervisor data uploader, and after passing identity verification, the uploader authorizes and obtains the ciphertext. Only users who meet the policy _Pa_ attribute can decrypt the ciphertext. During this process, third-party attackers are unable to obtain plaintext supervision data, and protocol attacks are divided into the following two situations.  
1. The attacker queries the file address _CID_ on the blockchain and sends a download request to the IPFS file system. Due to the data uploader not authorizing the user, the attacker was unable to authenticate and the IPFS node refused the data download request.
2. Attackers perform replay attacks by intercepting user-sent request messages. Attackers can disguise themselves as data requesters and send download requests to data uploaders to obtain their signature information. Through identity authentication of IPFS nodes, combined with the file address _CID_ on the chain, attackers can obtain data ciphertext. During this process, attackers can obtain encrypted files through replay attacks, but the key to the ciphertext is only owned by users who meet policy attributes, so the attacker cannot decrypt and obtain the original file.
## 3 Dynamic Link Establishment of Mega-Constellations  
In the \"3 + 1\" topology control scheme, dynamic links play a crucial role in enabling flexible and continuous inter-satellite communications. Unlike traditional static links [3; 11; 12; 13; 14], dynamic links consider the relative positions and motion states of satellites to maximize the link duration. This section explores strategies for establishing dynamic links and selecting optimal connection methods within specified constraints, enhancing both the efficiency and stability in inter-satellite communications.
# Optimization for an Indoor 6G Simultaneous Wireless Information and Power Transfer System
## 2 System Model  
In the channel model, the ray-tracing method is used to calculate the loss and frequency response of 6G terahertz communication. The frequency response is denoted as follows:  
\[H(f)=\sum_{i=1}^{N}\alpha_{i}(f)e^{j\varphi(f)} \tag{1}\]  
where \(N\) is the total number of paths, \(f\) is the frequency, \(i\) is the path index of the ray, \(\alpha_{i}\) is the \(i\)-th amplitude comprising the phase and intensity information, and \(\varphi\) is the phase shift delay. The time-domain impulse response of the equivalent baseband can be obtained by using the inverse Fourier transform.  
\[h(t)=\int_{-\infty}^{\infty}H(f)e^{j\pi ft}df \tag{2}\]  
Figure 1 shows the SWIPT splitter structure. For simulation's sake, the split rate of the power divider is set to \(\alpha\) for the power harvester. The harvesting power collector and the information decoder are arranged in front of the power distributor.  
The wireless harvesting power is modeled as the received energy per bit:  
\[HP=\alpha\eta\int_{-\infty}^{\infty}h(t)^{2}dt \tag{3}\]  
where \(\eta\) is the portion of RF signal used for power acquisition, and \(\alpha\) is the energy split ratio. The information decoder for BER is used to study the inter-symbol interference in our SWIPT system. Its calculation formula is as follows [29; 30]:  
\[BER=\sum_{n=1}^{2^{N}}P(\stackrel{{\rightarrow}}{{d}}_{n})\cdot \frac{1}{2}erfc[\frac{V_{n}}{\sqrt{2}\sigma}(d_{N})] \tag{4}\]  
where \(erfc(x)=\frac{2}{\sqrt{\pi}}\int_{x}^{\infty}e^{-y^{2}}dy\) is the complementary error function, and \(V\) is the received signal after demodulation. \(\{\stackrel{{\rightarrow}}{{d}}_{n}\}=\{d_{1},d_{2},\ldots d_{N}\}\) is the binary sequence, and \(N\) is the number of bits.  
The array equivalent mode is calculated by the array factor, and the coordinate relationship is shown in Figure 2.  
Figure 1: Decentralized structure of SWIPT system.  
According to Figure 2, we can express the array factor as follows:  
\[AF(\theta,\phi,f)=\sum_{n=1}^{M}W_{n}e^{\mathrm{i}ka[sin\theta sin\theta sin\theta _{n}cos(\phi-\phi_{n})+cos\theta_{n}cos\theta]} \tag{5}\]  
where \(M\) is the total number of antennas in the array, \(a\) is the distance between the antenna location and the origin, \(\theta_{n}\) and \(\phi_{n}\) are the polar angle and azimuthal angle of the transmitter, respectively, the azimuthal angle \(\phi\) and polar angle \(\theta\) are the spherical coordinates system, and \(k\) is equal to \(2\pi\) divided by the wavelength. The adjustment for phase delay and antenna power can be expressed as follows:  
\[W_{m}=I_{m}e^{iD_{m}(f)} \tag{6}\]  
where \(D_{m}(f)\) is the excitation current and \(I_{m}\) is the phase delay. The relationship between feed length and \(D_{m}(f)\) can be expressed as follows:  
\[D_{m}(f)=-\frac{2\pi f}{c}\sqrt{\epsilon_{r}}.\ell_{n} \tag{7}\]  
where \(e_{r}\) is the relative dielectric constant of the feeder and \(\ell_{n}\) is the feeder length of the transmission line, which can be used to adjust the phase delay; \(c\) is the light speed.
## 2 Materials and Methods
### Materials
#### 2.2.2 Microwave Satellite Images  
A series of microwave satellite images were acquired by TerraSAR-X (29 images, \(\lambda\) = 3.1 cm). X-band data were delivered at a HH polarization state using two beam modes (Stripmap (SM) and Spotlight (SL)), with an incidence angle ranging from 27.3 to 53.3\({}^{\circ}\) and pixel spacing ranging from 1.5 to 3 m [28]. The main features of the TerraSAR-X images are shown in Table 1.  
C-band data were delivered at a full quad-polarization (FQP) state (HH, VV, HV, and VH) by Radarsat-2 (22 images, \(\lambda\) = 5.5 cm). The angle of incidence varied from 24\({}^{\circ}\) to 41\({}^{\circ}\) and the pixel spacing was around 5 m [29]. The dates of each acquisition, together with details of the image configurations, are given in Table 2.  
Figure 2: Temporal evolution of the number of plots (among the 37 monitored plots) with bare soil during the 2010 cropping year.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode** & **Pass** & \begin{tabular}{c} **Incidence Angle** \\ (°) \\ \end{tabular} & \
\begin{tabular}{c} **Pixel Size** \\ (°) \\ \end{tabular} &
\begin{tabular}{c} **Acquisition Date** \\ **(MM/DD)** \\ \end{tabular} \\ \hline Spotlight & D & 28.7 & 2 & 03/15 \\ Spotlight & A & 32.3 & 2 & 04/14 \\ Spotlight & A & 45.5 & 1.75 & 04/08; 04/30; 08/29 \\ Spotlight & D & 53.3 & 1.5 & 03/05; 05/21; 07/15; 08/17 \\  & & & 09/30; 10/11; 10/22 \\  & & & 11/02; 11/13; 11/24 \\ Stripmap & D & 27.3 & 2.75 & 02/21; 03/26; 05/09; 05/20 \\  & & & 07/14; 08/16; 09/29; 10/10 \\  & & & 10/21; 11/12; 11/23 \\ Stripmap & A & 31.8 & 2.75 & 09/15 \\ Stripmap & D & 41.7 & 3 & 02/27; 07/31 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of satellite images acquired by TerraSAR-X in 2010.  
Figure 3: Color compositions produced on the super site from three TerraSAR-X images acquired in HH polarization in Stripmap mode (R: 20 March 2010, V: 17 August 2010, B: 24 November 2010) (**a**), and from Radarsat-2 images (R: HH, V: VV, and B: HV) acquired on 15 April 2010 (**b**).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode** & **Pass** & \begin{tabular}{c} **Incidence Angle** \\ (°) \\ \end{tabular} & \
\begin{tabular}{c} **Pixel Size** \\ (°) \\ \end{tabular} &
\begin{tabular}{c} **Acquisition Date** \\ **(MM/DD)** \\ \end{tabular} \\ \hline FQP5 & A & 23.3–25.3 & 4.7 \(\times\) 4.9 & 03/05; 11/24 \\ FQP6 & D & 24.6–26.5 & 4.7 \(\times\) 4.7 & 10/21; 11/14 \\ FQP10 & A & 29.1–30.9 & 4.7 \(\times\) 5.1 & 02/26; 04/15; 05/09; 09/30 \\ FQP11 & D & 30.2–32.0 & 4.7 \(\times\) 5.5 & 03/26; 08/17 \\ FQP15 & A & 34.3–36.0 & 4.7 \(\times\) 4.8 & 03/15; 04/08; 05/02; 08/30; \\ FQP16 & D & 35.4–37.0 & 4.7 \(\times\) 5.1 & 05/20; 07/31; 10/11 \\ FQP20 & A & 39.1–40.7 & 4.7 \(\times\) 4.8 & 11/03 \\ FQP21 & D & 40.1–41.6 & 4.7 \(\times\) 5.1 & 02/20; 03/16; 07/14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of satellite images acquired by Radarsat-2 in 2010.
## 3 Results and Discussion
### Reactive Azo Dye Oxidation
#### 3.2.5 Kinetics and Thermodynamics
Other various thermodynamic variables, such as the enthalpy (\(\Delta\)H\({}^{\prime}\)), entropy (\(\Delta\)S\({}^{\prime}\)), and free energy (\(\Delta\)G\({}^{\prime}\)) of activation, were assessed utilizing [PERSON]'s equation with the utilization of E\({}_{\rm a}\) and k\({}_{2}\) values according to the following relation [24]:  
\[\rm k_{2}=\frac{k_{B}T}{h}e^{(-\frac{\Delta\rm G\prime}{RT})} \tag{15}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Kinetic Model**} & \multirow{2}{*}{**Parameter**} & \multicolumn{4}{c}{**Temperature**, \({}^{\circ}\)**C**} \\ \cline{3-5}  & & **25** & **40** & **50** & **60** \\ \hline \multirow{3}{*}{Zero-order} & k\({}_{0}\) (mg.min\({}^{-1}\)) & 2.673 & 2.62 & 2.459 & 2.403 \\  & t\({}_{1/2}\) (min) & 72.171 & 70.794 & 66.393 & 64.881 \\  & R\({}^{2}\) & 0.54 & 0.49 & 0.39 & 0.37 \\ \hline \multirow{3}{*}{First-order} & k\({}_{1}\) (min\({}^{-1}\)) & 0.223 & 0.293 & 0.33 & 0.317 \\  & t\({}_{1/2}\) (min) & 3.107 & 2.365 & 2.100 & 2.186 \\  & R\({}^{2}\) & 0.90 & 0.94 & 0.81 & 0.72 \\ \hline \multirow{3}{*}{Second-order} & k\({}_{2}\) (L.mg\({}^{-1}\) min\({}^{-1}\)) & 0.044 & 0.164 & 0.328 & 0.341 \\  & t\({}_{1/2}\) (min) & 0.4208 & 0.1129 & 0.0564 & 0.0543 \\ \cline{1-1}  & R\({}^{2}\) & 0.97 & 0.90 & 0.98 & 0.97 \\ \hline \hline \end{tabular}  
* \({}^{*}\)k\({}_{0}\), k\({}_{1}\), k\({}_{2}\): kinetic rate constants of zero-, first-, and second-reaction kinetic models; C\({}_{0}\) and C\({}_{\rm C}\): dye concentrations at initial time and time \(t\); \(t\): time; R\({}^{2}\): correlation coefficient; t\({}_{1/2}\) half-life time.  
\end{table} 
Table 3: Fitted rate constants for the oxidation reaction of dye-containing wastewater *. Figure 11: Plot of ln k\({}_{2}\) versus 1000/T for the modified [PERSON]’s earth Fenton system. where k\({}_{\text{B}}\) is the [PERSON] constant, and h is [PERSON]'s constant. The thermodynamic parameters for Levafix dye oxidation were estimated accordingly, and they are displayed in Table 4.  
An investigation of the data in Table 4 found that the positive values of \(\Delta\)H/ across the studied temperature range indicate that the reaction is endothermic. Moreover, \(\Delta\)G/ exhibited positive values, which means that the process is non-spontaneous. This result might be because of the formation of a well-solvated structure between the dye molecules and the OH radical species. Moreover, the negative entropy values also support this.
## 5 Dynamic Bandwidth Allocation  
In this work, the centralized DBA component is used to provide fair bandwidth guarantees based on VDC. The redundant bandwidth of a link onto which VDCs are scheduled should be allocated among these VDCs fairly. In order to achieve work-conservation, the bandwidth allocated for each VDC should be reallocated in a timely manner according to its actual bandwidth demands on the narrowest link. The allocation mechanism is introduced in detail in this section.