esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on a Reductive Deep Chlorine Removal Process for Breaking Through the Solid Film Barrier
## 3 Results and Discussion
### Ascorbic Acid Denlorination Reaction Mechanism
#### 3.6.2. Dechlorination Process Through Reduction of Cu\({}^{2+}\) with Ascorbic Acid  
Based on the study of the experimental procedure and the analysis of the precipitation products, the reaction process for the reduction of Cu\({}^{2+}\) by ascorbic acid to remove chlorine is shown in Figure 16. Ascorbic acid (C\({}_{6}\)H\({}_{8}\)O\({}_{6}\)) is a reducing agent with an end structure in which three carbon atoms and one oxygen atom form double bonds, all of which are sp\({}^{2}\)-hybridized. The \(\pi\), \(\pi\)-conjugated structure that forms between the carbon atoms in the end structure increases the electron cloud density, making the carbon-carbon double bond highly reactive. The \(p\)-electron pair on the hydroxyl oxygen of the end structure slightly biases the charge toward the carbon end, increasing the polarity of the hydroxyl-oxygen-hydrogen bond and making it easier for hydrogen to dissociate. The redox reaction of ascorbic acid follows a proton-coupled electron transfer (PCET) mechanism, in which proton (H\({}^{+}\)) dissociation and electron (e\({}^{-}\)) transfer occur simultaneously. Specifically, theediol structure of ascorbic acid facilitates the dissociation of a proton to form an ascorbate monoanion. The negatively charged oxygen atom in this species can attract positively charged particles such as Cu\({}^{2+}\), enabling the transfer of an electron from the oxygen to the Cu\({}^{2+}\) ion, thereby reducing it to Cu\({}^{+}\). Throughout the process, proton transfer and electron transfer occur in a concerted manner, reflecting the core characteristics of the PCET mechanism, thereby contributing to the effective reduction of Cu\({}^{2+}\) and removal of chloride ions by ascorbic acid. The hydroxyl group of another enolic structure of ascorbic acid can also ionize hydrogen ions and participate in the reduction of Cu\({}^{2+}\). The reduced Cu\({}^{+}\) reacts with chloride ions (Cl\({}^{-}\))-in solution to form a cuprous chloride precipitate (CuCl). During this process, ascorbic acid itself is oxidized to dehydroroscorbic acid (C\({}_{6}\)H\({}_{6}\)O\({}_{6}\)).  
When dissolved oxygen exists in the solution, the CuCl produced by the reaction is very easily oxidized. However, this process uses ascorbic acid as a reducing agent, which effectively removes dissolved oxygen during the declorination reaction, preventing the oxidation and re-disso  
\begin{table}
\begin{tabular}{c c c c} \hline
**Element** & **Cu** & **Cl** & **O** \\ \hline Concentration (\%) & 67.82 & 32.09 & 0.09 \\ \hline \end{tabular}
\end{table}
Table 4: Percentage of elemental content of ascorbic acid dedchlorination products.  
Figure 16: Mechanism of ascorbic acid reduction for dedchlorination.
# Automated Fillet Weld Inspection Based on Deep Learning from 2D Images
## 3 Results and Discussion
### Experiment Results and Discussion
#### 3.2.1 Experiment 1: FCAW-GMAW Weld Seam
The model achieved good performance with high precision (around 0.98), recall (around 0.98), and mAP scores (reaching around 0.97 for mAP50). This suggests that the trained model was able to accurately detect the weld seam, whether it was created using the FCAW or GMAW technique. That is, the model was able to find characteristics specific to each welding method in order to differentiate them.
# A 12 dBm B1 dB N-Path Notch Filter for Transmitter Leakage Suppression in Wideband Receiver
## 5 Measurement Results  
A high-linearity N-path notch filter is implemented using a 130 nm RFSOI process, and the die micrograph is depicted in Figure 13. The total chip area is 2.4 \(\times\) 2.4 mm\({}^{2}\), with the active filter area occupying 0.25 mm\({}^{2}\). To achieve the differential structure, two off-chip 1:1 balun (mini-circuits TC1-1-13 MG2+) are used at the RF input and output ports. The chip is mounted in a QFN 40 package and tested on a PCB. Losses due to cables and PCB signal traces are de-embedded during measurement to ensure accurate results. The power consumption is measured to range from 186 to 242 mW within the 300 to 600 MHz RF range under a 12 dBm high TX blocker. The primary power consumption is attributed to the buffer, which drives large switches with an eight-phase nonoverlap 12.5% duty cycle clock.  
Figure 12: The NAND circuit.
## 2 Passive Wake-Up Radio
### Review of the Existing Passive Wake-Up Radio Hardware Design
#### Broadcast-Based Wake-Up Radio Design
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Addressing** & **Components Used** & **Reference** & **Frag. (MHz)** & **Min. V\({}_{\text{in}}\) (V)** & **V\({}_{\text{out}}\) (V)** & **Sensitivity** & **Data Rate** & **Power** & **Dissipated (\(\mu\)W)** & **Implementation** \\ \hline \multirow{6}{*}{ID-Based} & \multirow{2}{*}{Discrete} & [72,73] & 433 & - & 0.6 & \(-\)23 & - & 84 & Simulation \\  & & [74] & 2400 & 0.05 & 1.13 & \(-\)28 & 1 & 39 & Prototype \\ \cline{2-10}  & & [75] & 915 & - & \(>\)1.9 & \(-\)4 & - & - & PCB \\  & & [76] & - & - & - & - & 100 & Prototype \\  & & [21] & 915 & 0.92 & 1.8 & \(-\)80 & 1 packet/min & 0.043 (\(\mu\)/plkt) & Prototype \\  & & [77] & 900 & 0.92 & 1.8 & \(-\)80 & 1 packet/min & 0.043 (\(\mu\)/plkt) & Prototype \\  & & [81] & 915 & 0.15 & 2.074 & \(-\)20 & - & 0.25 & PCB \\  & & [79] & 900 & - & - & -86 & - & - & Prototype \\  & & [80] & 915 & 0.15 & \(-\)20 & - & - & PCB \\  & & [82] & 868 & 1.8 & - & N/A & - & 54 & Prototype \\ \cline{2-10}  & 130-nm CMOS & [83] & 2400 & 1 & - & \(-\)47 & 200 & 2.3 & Prototype \\  & 65 nm CMOS & [84] & 2400 & - & 0.03 & \(-\)56.5 & & 0.236 & Prototype \\  & 65 nm CMOS & [89] & 2400 & 1 & - & \(-\)39 & 8.192 & 0.104 & Prototype \\  & 65 nm CMOS & [89] & 2400 & 1 & - & \(-\)50 & 100 & 13 & Simulation \\ \hline \multirow{6}{*}{Broadcast} & 180 nm CMOS & [90] & 900 & 0.8 & 0.7 & \(-\)17 & - & 2.64 & Prototype \\  & 130 nm CMOS & [91] & 868 & - & 0.8 & \(-\)33 & 100 & 0.5 & Simulation \\ \cline{1-1}  & 130 nm CMOS & [92] & 915 & 1.2 & - & \(-\)53 & 100 & 0.2 & Prototype \\ \cline{1-1}  & 180 nm CMOS & [93] & 2400 & 1.2 & 1.8 & \(-\)23 & - & 5 & Simulation \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Passive wake-up radio technology and its performance characteristics and properties.
## 3 Design and Simulation of a Velocity Loop Controller Based on Discrete SMC
### Basic Theory of Discrete SMC
#### 3.1.2 Discrete Sliding Mode Function and the Sliding Mode Surface  
In this study, the classical sliding mode function was adopted, which is defined as follows:  
\[s(k)=C_{e}e(k)=C_{e}(x(k)-R_{n}(k)), \tag{12}\]  
where \(C_{e}=[C_{e(1)}\), \(C_{e(2)}\),..., \(C_{e(n-1)}\), 1] is the sliding mode coefficient matrix, and \(e(k)\) represents the error matrix between the ideal state and the actual state. Therefore, the sliding mode surface can be defined as:  
\[S=\{e(k)\big{|}C_{e}e(k)=0\}. \tag{13}\]  
**Lemma 1**.: _The sliding mode coefficient matrix parameters \(C_{e(1)}\),\(C_{e(2)}\),..., \(C_{e(n-1)}\) satisfy the polynomials \(p^{n-1}+C_{e(n-1)}p^{n-2}+\ldots+C_{e(2)}+C_{e(1)}\), and must be Hurwitz polynomials; \(p\) is the Laplace operator [35]._
###### Abstract  
Effective methods for extending the storage capacity of tailings for a mining company include expanding and increasing the height of the tailings dam. However, this change could lead to an uplift in the phreatic line and a decrease in the slope stability. In this paper, a new drainage system combining a horizontal drainage pipe with an upward bending slotted pipe was proposed and applied to the design of a seepage-proof system for the Xigou tailings dam with an increased height. To accurately simulate the performance of the seepage control system, a three-dimensional finite element model was established on the basis of a geological investigation of the site conditions. In this work, a substructure technique was used to model the drainage pipe with a small radius and dense spacing to reduce the difficulty in mesh generation, and a back-analysis method called MPSO-BP (modified particle swarm optimization algorithm and a back propagation neural network) was used to correct the measured permeability coefficients. The results show that the new drainage system can effectively dissipate the seepage pressure, decrease the phreatic surface, and improve the safety factors of the slope stability. The proposed drainage system can also meet the seepage stability requirements of the higher tailings dam. Additionally, this system can be widely deployed in similar projects.  
**Keywords: heightened tailings dam; tailing dam drainage system; seepage performance assessment; substructure technique; slope stability**
## 6 Application of Plasma Treatment in Advanced and Emerging Packages
### Flip-Chip Packaging  
Flip-chip technology involves attaching a die to a substrate by flipping it upside down and connecting it through solder bumps. Plasma cleaning of copper pillar bumps and under-bump metallization (UBM) are crucial for removing oxides and contaminants that can hinder solderability and lead to joint failures. Plasma treatment also enhances the adhesion of underfill materials, which improves the mechanical reliability of the package.  
Regarding the improvement in solderability, the reviewed literature presented the use of hydrogen plasma to clean the bumps, instead of flux in a process known as fluxless soldering. An RF capacitively coupled plasma, the technology of which was outlined earlier in this paper, was used to generate hydrogen radicals that removed the oxide layers, thereby cleaning the bumps instead of flux. This process provides a 48-hour time window in which to carry out the flip-chip connects after the plasma treatment has been conducted [140]. Other papers presented similar work using hydrogen plasmas to remove oxide layers through reduction [141; 142].  
With respect to the improvement of the underfill process, various papers were reviewed. The reviewed literature showed that gas mixtures of argon and oxygen in a 6:1 ratio were used to produce the plasma necessary to reduce void formation in the underfill. Plasma treatment increased the amount of hydrophilic carbonyl functional groups (O-C=O) on the solder mask surface, which improved underfill wetting. The reviewed paper also underlined that increasing the staging time increased the percentage of unit failures due to void formation [143].  
Under-chip plasma cleaning was also found to be used in the improving of the moisture sensitivity level (MSL) performance for flip-chip packaging, thereby enhancing adhesion between silicon nitride (SiN) passivation and underfill, a critical interface prone to failure in MSL tests. In this case, an RF plasma system operating at 300 W was utilized with oxygen and a proprietary unnamed gas being used to produce the plasma. Pure oxygen plasma, when used alone, presented lower contact angles than when the plasma was used with the proprietary gas. However, an improvement in adhesion was noted with the addition of the proprietary gas [144].  
The literature comparing the suitability of N2O2, and Ar plasmas for surface activation beneath flip chips used atomic force microscopy (AFM) to analyse surface roughness and topography after treatment with plasma. The conclusions indicated that Ar plasma treatment caused an imbalance in roughness between the corner and central regions of the substrate and die. N\({}_{2}\) plasma treatment, on the other hand, tended to smoothen the surfaces, while O\({}_{2}\) plasma treatment increased surface roughness and the surface area difference percentage. The final conclusion was that O\({}_{2}\) plasma treatment resulted in the highest surface energy and was therefore the most suitable for improving underfill adhesion and flow in flip-chip packaging due to its effectiveness in activating the surface [145].
# Chosen Plaintext Combined Attack against SM4 Algorithm
## 3 Methodologies
### Complexity Analysis and Further Improvement  
As mentioned above, there are two steps for our attack. In the first step, i.e., the chosen plaintext attack, to determine the round keys in one round of analysis, three CPAs in one round of analysis are carried out to construct two differential relations of the input and output of S-box. There are \(2^{8}\) candidate values for each byte of the sensitive intermediate value, and 3 groups of curves need to be analyzed for attack. Hence, the sum key search space complexity for recovering the four round keys is \(6\times 4\times 2^{8}\). Meanwhile, the number of traces needed for our attack is \(6\times N\), where N is the number of traces needed for each CPA.  
To make our attack more feasible for experiments, we have made the following improvement. We first discuss the 2 nd round of analysis. Since CPA is byte-wise carried out, each byte of \(rk_{0}\) has two candidate values with near 100% probability. Alternatively, we carry out not three but two CPAs. Consequently, there are \(2^{4}\) candidate values for \(rk_{0}\). Moreover, \(rk_{1}\) also has \(2^{4}\) candidate values corresponding to \(rk_{0}\) one by one, since \(rk_{1}\) is determined by \(rk_{0}\). Unlike the analysis of Section 3.1, we continue to analyze the 2 nd round and guess the \(2^{4}\) candidate values of \(rk_{0}\) and \(rk_{1}\). Based on the guessed round keys, we recalculate the correlation coefficients between the S-box output and the traces. The round key corresponding to the maximum coefficient is the correct one. Then, we carry out another two CPAs in the 4 th round of analysis with known \(rk_{0}\) and \(rk_{1}\). Likewise, the similar differential analysis is carried out in the 4 th round, and \(2^{4}\) candidate values of \(rk_{2}\) and \(rk_{3}\) are recovered. The correct values of \(rk_{2}\) and \(rk_{3}\) can be picked out corresponding to the maximum coefficient when guessing the candidate values and recalculating the correlation coefficients.  
From the improvement, only 4 main CPAs are carried out and the number of traces for analysis has been reduced into \(4\times N\). Moreover, the key search space complexity decreases to \(\left(4\times 2^{8}+2^{4}\right)\times 2\). To sum up, our attack has obvious advantages at not only the number of traces needed for our attack but also the time complexity. This makes our attack more practical and feasible for experiments.
# AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities
## 4 Results
### The Academic Characteristics of AI Governance
#### 4.3.1 Educative and Advisory Guidance  
Except for the IT policies related to data sharing and privacy issues, most of the guidelines do not enforce or prohibit any actions for the university community. Instead, they focus on providing resources for people to learn more about AI and recommend possible behaviors to avoid risks and promote benefits:  
_Purdue University continues to support the autonomy and choice of faculty and instructors to utilize instructional technology that best suits their teaching and learning environments. As such, there is no official university policy restricting or governing the use of Artificial Intelligence, Large Language Models or similar generative technologies.--_ Purdue U  
As this statement shows, the guidelines are written with the mindset to support the autonomy of its community to make the best use of the technology. In addition to being educative, the guidelines also make specific suggestions on appropriate or inappropriate usage of AI (see Figure 3). However, it should be noted with the educative and advisory characteristics; there also comes uncertainty regarding the use and management of AI in some scenarios. An obvious example is the use of AI detection tools. Northwestern U concluded that \"We do not recommend using this detection tool as the basis for reporting a suspected case of academic dishonesty\", which seems a consensus among the Big Ten. Yet, it remains unclear for faculty regarding what could be used as the basis for reporting academic dishonesty related to AI usage.
## 2 Pipeline Deformation Calculation
### Settlement of Soil Around the Pipeline  
After producing a large number of datasets, RB [PERSON]'s [16] organization's research found that a loss of excavated soil will cause deformation of the surrounding soil, which in turn causes the ground to settle. Under the assumption that the ground settlement trough shows a normal distribution, a ground settlement estimation formula is proposed:  
\[S(x)=S_{max}e^{\dfrac{-x^{2}}{2i^{2}}} \tag{5}\]  
\[S_{max}=\dfrac{V_{1}}{i\sqrt{2\pi}} \tag{6}\]  
\[i=\dfrac{Z}{\sqrt{2\pi}\tan(45-\dfrac{\alpha}{2})} \tag{7}\]  
where  
\(x\) represents the horizontal distance from the tunnel axis;  
\(S(x)\) represents \(x\) at the location of the ground settlement; \(S_{max}\) represents the maximum ground settlement above the tunnel axis;  
\(V_{1}\) represents the unit stratum loss, \(V_{1}=\pi R^{2}\eta\), for the tunnel excavation radius \(R\);  
\(\eta\) is the rate of soil loss in Hangzhou area [17], which can be taken as \(\eta=6.574Z^{-0.7348}\);  
\(i\) is the ground settlement groove width coefficient;  
\(Z\) represents the tunnel axis of the depth of burial;  
\(\alpha\) is the weighted average value of the friction angle for the tunnel around the stratum.  
According to Equation (5), the influence of a large number of double-line shield tunnels on the transverse surface settlement is studied and analyzed. [PERSON] et al. [18] established a two-dimensional Peck's formula applicable to the prediction of the lateral ground settlement caused by two-lane parallel tunnel excavation using the principle of superposition based on [PERSON]'s formula:  
\[S(x)=S_{max}e^{\dfrac{-(x+\dfrac{A}{2})^{2}}{2i^{2}}}+S^{{}^{\prime}}_{max}e^{ \dfrac{-(x-\dfrac{A}{2})^{2}}{2i^{2}}} \tag{8}\]  
where  
\(S^{{}^{\prime}}_{max}\) is the maximum ground settlement after disturbance;  
\(A\) is the distance between the left and right line tunnels.  
The principle of superposition is used to superpose the plane settlement curves caused by the successive excavation of the left and right lines of shield tunnels and the interaction between the two until all the double lines have been excavated, and ultimately, a W-shaped total settlement curve is obtained, as shown in Figure 2.  
Figure 2: Calculation of double-line tunnel settlement by the superposition method.  
Shield tunnel boring will lead to stratum loss of the soil around the tunnel, which in turn causes settlement of the soil around the pipeline. In this paper, combined with engineering practice, based on Equations (5) and (8), the calculation formula for soil settlement around a pipeline caused by shield tunnel boring is optimized, and the expression is as follows:  
\[S_{p}(x)=\frac{V_{L}}{i\sqrt{2\pi}}\sum_{j=0}^{n}e^{\frac{-(x-\frac{Aj}{2})^{2} }{2i^{2}}} \tag{9}\]  
where  
\(S_{p}(x)\) is the settlement of the layer where the pipeline is located;  
\(V_{L}\) is the amount of ground loss in one excavation step;  
\(X\) is the tunneling distance of the shield machine;  
\(j\) is the number of excavation steps, as shown in Figure 3.
# NOMA or OMA in Delay-QoS Limited Satellite Communications: Effective Capacity Analysis
## 3 Effective Capacity  
To provide services with different delay-QoS requirements, we adopt the concept of effective capacity, which provides a measure for the constant arrival supportable source rate for a given delay exponent requirement characterized by \(\theta\) (\(\theta\geq 0\)) [20]. In this paper, an uncorrelated service process across different slots is further assumed and the normalized effective capacity is adopted. In this context, the normalized effective capacity of User \(j\) with NOMA and OMA scheme can be, respectively, written as  
\[C_{j}^{N}\bigl{(}\theta_{j}\bigr{)}=\frac{-1}{\theta_{j}T_{f}B}\ln\Bigl{(} \mathcal{E}\Bigl{\{}e^{-\theta_{j}T_{f}BR_{j}^{N}}\Bigr{\}}\Bigr{)}, \tag{9}\]  
and  
\[C_{j}^{T}\bigl{(}\theta_{j}\bigr{)}=\frac{-1}{\theta_{j}T_{f}B}\ln\Bigl{(} \mathcal{E}\Bigl{\{}e^{-\theta_{j}T_{f}BR_{j}^{T}}\Bigr{\}}\Bigr{)}, \tag{10}\]  
where \(T_{f}\) and \(B\) are, respectively, the frame duration and the occupied bandwidth, \(\mathcal{E}\) denotes the expectation operator, and \(\theta_{j}\) is a delay-QoS exponent for User \(j\).  
By submitting (2), (4), and (6) into (9), we can, respectively, obtain the expressions of effective capacity for Users \(c\) and \(t\) with the NOMA scheme as  
\[E_{c}^{N} =-\frac{1}{\varphi_{c}}\ln\Biggl{(}\mathcal{E}\left[e^{-\varphi_ {c}\log_{2}\Bigl{(}1+\frac{(1-\alpha)\rho|g_{c}|^{2}}{\alpha\rho|g_{c}|^{2}+1} \Bigr{)}}\right]\Biggr{)}\] \[=-\frac{1}{\varphi_{c}}\ln\Biggl{(}\mathcal{E}\left[\left(1+ \frac{(1-\alpha)\rho|g_{c}|^{2}}{\alpha\rho|g_{c  
where \(\varphi_{j}=\theta_{j}T_{f}B\)\((j=c,t)\). Similarly, the expressions of effective capacity of Users \(c\) and \(t\) with the TDMA can be, respectively, calculated by inserting (7) and (8) into (10) as  
\[E_{c}^{T}=-\frac{1}{\varphi_{c}}\ln\biggl{(}\mathcal{E}\left[\left(1+\rho|g_{c} |^{2}\right)^{-0.5\varphi_{c}/\ln 2}\right]\biggr{)}, \tag{13}\]  
and  
\[E_{t}^{T}=-\frac{1}{\varphi_{t}}\ln\biggl{(}\mathcal{E}\left[\left(1+\rho|g_{t }|^{2}\right)^{-0.5\varphi_{t}/\ln 2}\right]\biggr{)}. \tag{14}\]  
In the following subsections, the influences of key parameters on users' performance approximations and exact closed-form expressions in both NOMA and OMA schemes, and on each user's and considered system's capacity difference between NOMA and OMA for the considered communication network are investigated.