esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Algorithm
### SARSA for Solving the MDP Model  
RL can discover an optimal policy and obtain the maximal benefit from the environment. As an online RL algorithm, SARSA was used in this paper to solve the MDP for the channel allocation process. SARSA selected the optimal actions at each time step in real-time and directly updated the policy by the performed actions through interacting with the environment so that the channel allocation algorithm could be adjusted in real-time with the environmental changes.  
First, parameters such as the channel status, the Q-value, the learning rate and the discount factor were initialized. Second, by using the temporal-spatial correlation of handoff calls, action \(a_{t}\) was performed according to the policy \(\pi\) for different call events. Subsequently, the immediate reward \(R_{t}\) was obtained after performing \(a_{t}\) and then the environment reached a new state. Third, with the same policy, an action was selected for the next call event and the current Q-value was updated. Last, the iteration continued in the new state until the ending conditions were satisfied. SARSA for solving the MDP model is shown in Algorithm 1.  
```
Input:\(\alpha\), \(\delta\), \(\gamma\) Output:\(Q^{\prime}(s,a)\) \(X\gets x_{0}(n,k)=0\) //initialize the channel status \(Q(s,a)=0\) //initialize the Q-value whilelist { call event } \(\
eq\)\(\phi\)do if\(e_{t}=e_{n,t}^{max}\) \(a_{t}\leftarrow\pi\left(s_{t}=\left(X_{t},e_{n,t}^{new}\right)\right)\) //perform \(a_{t}\) for the new call event elseif\(e_{t}==e_{n,t}^{end}\) \(a_{t}\leftarrow\pi\left(s_{t}=\left(X_{t},e_{n,t}^{end}\right)\right)\) //perform \(a_{t}\) for the departure call event elseif\(e_{t}==e_{n,t}^{handoff}\) \(a_{t}\leftarrow\pi\left(s_{t}=\left(X_{t},e_{n,t}^{handoff}\right)\right)\) //perform \(a_{t}\) for the handoff call event end if \(X_{t+1}\gets z(s_{t},a_{t})\) //transform the channel status \(R_{t}=\sum_{n=1}^{N}\sum_{k=1}^{K}x_{t+1}(n,k)\) //calculate the immediate reward Update \(Q(s_{t},a)\) \(\alpha\leftarrow\)\(\alpha\leftarrow\)\(\alpha^{*}\delta\) //decline the learning rate with the decay factor \(\delta\) \(s_{t}\gets s_{t+1}\) \(t=t+1\) end
```  
**Algorithm 1**SARSA for solving the MDP model  
In view of different call events, the proposed algorithm had the following policy. For a new call event and a departure call event, the actions were performed with \(\varepsilon\)-greedy and then the Q-value was updated. For a handoff call event, two actions were successively performed with the temporal-spatial correlation of the handoff calls and then the Q-value was updated. The policy is described in detail below.
# Physicochemical and Molecular Properties of Spelt and Wheat Starches Illuminated with UV Light
## 3 Results and Discussion
### Molecular Weight Distribution
Similar results were obtained by the researchers who irradiated oat and barley starches with UV light for 5 or 30 min [22]. Greater degradation of the starch polysaccharide chains occurred for barley, which has a lower lipid content. Prolonged exposure caused a large increase in the mass of the chains, with a simultaneous increase in their radii of gyration. Longer irradiation led to a twofold reduction in the molecular weights of the polysaccharides while further irradiation caused a slow decrease in M\({}_{\mathrm{w}}\) to an average weight of about 7 \(\times\) 10\({}^{6}\) g/mol. Research conducted by [13] shows that the molecular weight values of cassava amylopectin decreased with increasing exposure time (4-16 h). Studies by the authors [13] on the effect of UV radiation on corn and cassava starch showed that the exposure time had a significant effect on the properties of the starch polysaccharides. With an increase in irradiation time, parameters such as the molecular weight of the starch polysaccharides and the viscosity of the starch pastes decreased. The mechanism proposed by the authors assumes that UV radiation disrupts the C2-C3 bond of glucopyranose units and, consequently, causes the gradual degradation of polysaccharide chains. The authors in [13] also reported that the botanical origin of starch influences its degree of UV depolymerization. Additionally, they indicate that cassava starch is more susceptible to depolymerization than corn starch. Other researchers [21] suggest that oxidative depolymerization occurs to a greater extent for cassava than for other grain starches, which may be due to the lack of lipids in cassava starch. As previously mentioned, lipids are more susceptible to oxidation and are the first to oxidize, which may provide protection for starch in the UV depolymerization process. It is noteworthy that some of the authors [4] conducted research on the influence of UV radiation on the properties of cassava starch products previously treated with lactic acid. Using HPSEC chromatography, they determined that UV-B radiation only degraded amylose chains, and only the application of higher energy radiation modified amylose and amylopectin. Prolonged exposure (15 h) to UV-C radiation reduced the susceptibility for baked good growth. According to the literature [23], the researchers illuminated four types of starch with UV light for 8 h. The data suggest a significant drop in the molecular weights of amylose and amylopectin in wheat starch, corn starch, and waxy corn starch. The authors concluded that this was evidence of main chain scission. A different type of behavior after UV-irradiation was observed with potato starch. An increase in in M\({}_{\mathrm{w}}\) values was assigned to photoaggregation or photocrosslinking reactions.
# A Practical Positioning Method in End-Plate Surface Distance Measurement with Nano-Meter Precision
## 6 Conclusions  
The length value disseminated in the way of end-plate surface distance is widely used in industry and science. Surface positioning is an important step for end-plate surface distance measurement. In this paper, a practical method is introduced for realizing the surface positioning with nano-meter precision. The method was used experimentally by making 0.1 mm step sizes in the measurement system. The measurement uncertainty analysis shows that the measurement uncertainty is 21 nm. Since the 0.1 mm length is relatively small, the main contribution of measurement uncertainty is the repeatability, and the measures to enhance repeatability are studied as the emphasis of the paper. The measures include the optimization of optical and mechanical parts of the system, using appropriate signal processing methods, and the appropriate measurement methods, etc. In conclusion, the combination of laser interference and white light interference for positioning of the surface is the effective measure to obtain good measurement repeatability. The method is verified to be effective. It will be applied to the length measurement primary standard (AIC) of NIM(National Institute of Metrology of China) in future studies.  
Conceived the Methods and Wrote the Paper, H.G. and Y.C.; Data curation, Y.L.; Performed some Confirmatory Experiments, S.S. and Z.S.; Resources, Z.W.; Edited the Manuscript, Y.C.  
This research was supported by the Plan of Enhancing the Ability of National Standard Devices of General Administration of Quality Supervision, Inspection and Quarantine of China, Grant Number 24-ANL1804; Special Scientific Research on Civil Aircraft of Ministry of Industry and Information Technology of China, Grant Number MJ-2018-J-70; Sichuan Province Key Research and Development Plan of China, Grant Number 2019 YFSY0039; National Key Research and Development Plan of China, Grant Number 2016 YFF0203801.  
The authors declare no conflict of interest.
## 4 Experiment and Results
### Performance with a Five-Month Lead Time
From the prediction results over these two years, it can be concluded that for the prediction of Arctic sea ice during the melt season, the PDED-ConvLSTM model is capable of keeping the error range for most regions within (\(-20\%\), \(20\%\)), which is considered an ideal outcome. Figure 9: RMSE and MAE trends for different lead months.
# Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning
## 2 Related Work
### Secure Multi-Party Computation and Homomorphic Encryption  
Since distributed machine learning is based on a center dispatching tasks to the outside, and, in this case, data are transparent to the system, and data privacy cannot be protected effectively. Generally, distributed learning involves multi-party computing, which often gives the complicated or unknown computing process to a third party. In 1986, [PERSON] proposed the Garbred Circuit method based on the millionaire problem, which can be used to solve general problems, including almost all two-party password problems [17]. In addition, then, in 1998, [PERSON] proposed the concept of secure multi-party computation (SMPC) [18]. Thus far, SMPC is regarded as a subfield of cryptography that enables dis tributed parties to jointly compute an arbitrary functionality without revealing their won private inputs and outputs.  
Currently, homomorphic encryption has become a commonly used method in SMPC. In 1978, [PERSON] et al. proposed the concept of homomorphic encryption for bank applications [19]. As one of the first public-key cryptosystems, the well known RSA (Rivest-Shamir-Adleman) has multiplicative homomorphism [20; 21]. In 1999, the Paillier algorithm was invented [22]. Since Paillier satisfied the addition of homomorphism, it has been widely used in cloud ciphertext retrieval, digital auction, digital elections, and some other privacy protected applications. In 2009, [PERSON] first proposed a fully homomorphic encryption (FHE) algorithm based on ideal lattices which satisfied both additive homomorphism and multiplicative homomorphism [23]. Since FHE has extremely high security, it has been widely used [24; 25; 26]. Especially in cloud computing, homomorphic encryption has made great contributions to privacy protection [27].  
Besides that, differential privacy is also a privacy assurance technique used to prevent privacy leakage by adding noise to the samples [28; 29; 30]. Since introducing noise, when the amount of data is small, the influence of noise will inevitably affect the model training. How to reduce the influence is a big challenge.
## 3 Materials and Methods  
### Strains, Plasmids, and Culture Conditions  
_Trichoderma afroharzianum_ ACCC 33109 and _Fusarium oxysporum_ f. sp. _cucumerinum (Foc)_ ACCC 37438 were obtained from the Agriculture Culture Collection of China (ACCC). The chassis strain and mutant strains of ACCC 33109 constructed in this study were conserved in the laboratory stock. The fungal culture were positioned onto Petri dishes with potato dextrose agar (PDA; Oxoid, UK) medium or into potato dextrose broth (PDB; Oxoid) and thereafter subjected to incubation at 28 \({}^{\circ}\)C for 5 days. The plasmids, pKS666 and pKH-KO, donated by Drs. [PERSON] and [PERSON] from the Institute of Plant Protection, Chinese Academy of Agricultural Sciences, were used in the construction of the gene knockout mutant strains. The vectors pEASY-T3 and pET28a (TransGen, Beijing, China) were used to construct the recombinant plasmids for DNA manipulation and gene cloning, respectively. The _Escherichia coli_ strains Trans1-T1 and BL21 (DE3) were propagated in Luria-Bertani (LB) medium with appropriate antibiotics for plasmid DNA isolation and gene expression, respectively.
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies
### Minimizing Transmission Energy Costs
#### 5.2.2 Compressed Sensing
Moreover, if the data are not perfectly sparse, or if there is noise, then the process of reconstruction can introduce errors, leading to inaccuracies in the final analyses.
## 8 Experiment with the Multimodal User Emotion Detection System
### Performance of the Multimodal User Emotion Detection System Tested with Real Users
#### 8.2.2 Confusion Matrix of the Multimodal User Emotion Detection System  
Again, analyzing the data recorded, the confusion matrix is obtained to prove the usefulness of the whole system; see Table 4. As observed, the biggest values for each row are situated at the diagonal of the matrix, proving the good performance of the system. In this case, when the system is tested with real users, _the average success rate is 77%_. This value is near to the average success rate computed statistically (83%) and better than the ones obtained for each classifier: 56.37% for J48, 57.10% for JRIP, 75.55% for SHORE and 62.66% for CERT.  
These results prove that the use of several modes (audio and visual) to detect the user emotion improves the success rate obtained by each mode separately. Moreover, the whole system seems to be sufficiently good at detecting the user emotion. In fact, it does not seem to present any serious problems in recognizing any emotion in particular. In detail, the proposed multimodal system is quite good at detecting happiness, relatively good at surprise and, finally, just fair at recognizing the neutral and sad emotions (although they do present acceptable success rates).
## 2 Experimental Details  
### Synthesis of TiO\({}_{2}\) Films and Gold Deposition  
Hydrothermal synthesis assisted by microwave irradiation has been used for the production of the TiO\({}_{2}\) nanostructured films. For the TiO\({}_{2}\) microwave synthesis, it has been used deionised water, titanium (IV) isopropoxide (Ti[OCH(CH\({}_{3}\))\({}_{2}\)]\({}_{4}\), TTIP, 97% from Sigma Aldrich), and three types of acid, i.e. nitric acid (HNO\({}_{3}\), 65%), hydrochloric acid (HCl, 37%) and acetic acid glacial (CH\({}_{3}\)COOH, 99%). In each synthesis, 55 mL of water was added to 5 mL of each acid and stirred for 5 min. Then, 2 mL of TTIP was mixed, leaving the final solution under stirring for 10 min before synthesis.  
Whatman chromatography paper grade two was used as substrate. This pure cellulose paper has been selected due to the lack of impurities or additives present together with its uniformly absorbent properties [42,43]. Prior to synthesis, the paper substrate was subjected to the conductive layer deposition. At this point, a gold film of 140 nm was deposited by e-beam evaporation in a homemade system and at room temperature. Gold is the most common thin-film conductor material, however its electrical properties are largely influenced by several parameters including the substrate used [44]. Oneimportant parameter that influences the EC performance is the sheet resistance of the contact, in fact the decrease of the sheet resistance has been reported to accelerate switching speeds of the EC devices [45]. Nevertheless, in the present study, the deposition parameters have been systematically maintained and highly reproducible thin films have been obtained, so the influence of Au sheet resistance is expected to be equivalent for all the devices produced.  
Any seed layer has been used for assisting the TiO\({}_{2}\) growth [10; 43]. Afterwards, the gold coated paper substrate was cut (\(30.0\times 15.0\) mm) and prepared for synthesis. A part of the substrate was covered on its edge (\(5.0\times 15.0\) mm) to prevent the deposition of the TiO\({}_{2}\) film on the conductive layer (Figure 1). The novel approach developed to protect the Au contacts was imperative to maintain the conductive character of the inner contact.  
Microwave synthesis was carried out using a CEM Focused Microwave Synthesis System Discover SP. Time, power, temperature and pressure have been set at 120 min, 100 W, 80 \({}^{\circ}\)C and 200 Psi, respectively. Then 20 mL of the final solutions were transferred into capped quartz vessels of 35 mL, which were maintained sealed during the entire reaction. In the microwave vessel, a piece of the gold coated substrate was placed at an angle against it, with gold facing down.
## 3 Methodology
### Data Pre-Processing
#### 3.2.1 Data Denoising  
In contexts involving smart wearable devices, including domestic environments, outdoor athletics, and routine employment, a smartwatch's integrated inertial measurement unit (IMU) could create noise. This distortion may arise from surrounding magnetic fields, external interference, and equipment exactness, impacting the efficacy of the individual's behavior identification technique. Employing data from various wearable inertial measurement units can mitigate noise effects in multi-sensor systems. Nonetheless, noise can substantially impact the identification method in routine scenarios where a single IMU is employed for data acquisition. Consequently, data filtration in single-sensor networks is imperative.  
Diverse algorithms, such as median, low-pass, and Kalman filtering, are available. Studies demonstrate that routine interactions between individuals generally oscillate between 1 and 15 Hz, a low-frequency spectrum amenable to filtration via a low-pass filter on IMU signals [25]. The Butterworth filter is a conventional, accessible filter utilized as a low-pass filter to attenuate high-frequency noise. This filter exhibits a maximum flat frequency response curve in the passband and gradually increases attenuation to zero in the stopband. The amplitude attenuation ratio in the stopband is directly correlated to the filter's order. The Butterworth filter is extensively utilized in signal processing because of its superior response to frequency properties.  
The Butterworth filter can be represented by Equation (1):  
\[|H(\omega)|^{2}\ =\ \frac{1}{1+(\frac{\omega}{\omega_{c}})^{2N}}\ =\ \frac{1}{1+\varepsilon^{2}(\frac{\omega}{\omega_{p}})^{2N}} \tag{1}\]  
where \(N\) represents the filter hierarchy, \(\omega_{c}\) is the cut-off frequency, and \(\omega_{p}\) is the boundary frequency of the passband.  
This study establishes the wearable device's IMU sampling rate at 100 Hz to ensure high-precision behavioral interpretation. The filter's cut-off frequency is established at 20 Hz with an order of 4 to preserve movement data below 15 Hz. Following the segmentation of sensor data from public datasets, the accelerometer and gyroscope data frames from a selected sensor are subjected to filtering. Figure 2 illustrates the signal waveforms prior to and subsequent to filtering.
## 4 Simulation Results
### Write Operation  
In MTJ-based memories, write operations typically require significantly more time (and consequently consume more energy) than read accesses. While the SIMPLY scheme drastically reduces the number of write accesses, write operations are still needed for defining inputs to logical operations and potentially the output, if the MTJ state needs to be flipped. We considered the conventional STT-MTJ write scheme, utilizing two access transistors per cell for SMTJ (2 TSMTJ) write configuration) and a single access transistor for DMTJ (1T1 DMTJ) write configuration). The two transistors in the SMTJ cell are used to avoid the source degeneration effect that can compromise successful writing to the  
Figure 4: PCSA-based comparator: (**a**) schematic and (**b**) timing diagram. In (**b**), two read operations are shown: \(V_{\text{G1}}\) (read **P** = **Q** = \({}^{\circ}\)0) and \(V_{\text{G2}}\) (read **P** \(\
eq\)**Q**).  
SMTJ [26]. On the contrary, the DMTJ-based bit cell requires only one nMOS transistor per MTJ mainly due to the symmetrical switching property of the DMTJ device [26].  
Table 2 shows the write pulse (\(t_{\text{write}}\)) for a write error rate of \(1\times 10^{-7}\) and energy (E\({}_{\text{write}}\)) for the 2 TISMTJ and 1T1 DMTJ cells operating at 300 K and 77 K. The \(t_{\text{write}}\) results refer to the worst-case transition between AP\(\rightarrow\)P and P\(\rightarrow\)AP. At 77 K, \(t_{\text{write}}\) increases mainly due to the rise of the critical switching current at cryogenic temperatures [29]. This effect is particularly dramatic for the SMTJ (see Table 1).
## 4 Transmissibility
Although these resistance values reduce the peak resonance, they also decrease the depth of vibration attenuation within the local resonance attenuation zone. Moreover, the maximum power output is predominantly confined to the lower frequency domain for both hierarchies, while the higher frequency peak vibration transmission is significantly suppressed. The zero-order hierarchy follows a trend similar to that of the outward configuration. The transmissibility of the outward and inward configurations showcase some differences as the resistance of the shunt circuit varies. Considering the inward configuration, when the resistance of the shunt circuit is \(10^{5}\) ohms, as shown in Figure 13c, an ultrabroad first local resonance attenuation zone is observed. This is beneficial for both lower frequency vibration attenuation and energy harvesting. Since the inward configuration shows a unique displacement transmissibility feature in the strong coupling case and its band structure is not symmetric with respect to \(\beta_{1}\), the displacement transmissibility at \(\beta_{1}=0.85\) is depicted in Figure 14. Figure 14a shows that the first local resonance vibration attenuation zone is very broad but shallow with a resistance of \(10^{4}\) ohms. The third category is observed when the resistance exceeds \(10^{5}\) ohms, which is characterized by the shift of the local resonance attenuation zones to higher frequencies. In the strongly coupled first-order outward configuration, designed with a combination of \(\beta_{1}=0.15\) and a shunt circuit resistance of \(10^{7}\) ohms, two attenuation zones are observed at high frequencies. In contrast, for the inward configuration at \(\beta_{1}=0.15\) and \(10^{7}\) ohms,  
Figure 13: Optimizing the shunt circuit resistance of strongly coupled hierarchical metamaterial at \(\beta_{1}=0.15\) and \(\theta_{1}=0.9\) N/V. Optimum displacement transmissibility (**a**) zero-order, (**b**) outward, and (**c**) inward. Optimum normalized total power output (**d**) zero-order, (**e**) outward, and (**f**) inward configurations. the first local resonance attenuation region disappears due to mode merging. In this configuration, increasing \(\beta_{1}\) to 0.85 reduces the bandwidth to a normalized frequency of 4. The optimal power output of the strongly coupled system depends on both the configuration type and the shunt circuit resistance values. Due to the narrow first band gap in the inward configuration at low shunt circuit resistances, it enables a broad frequency range for energy harvesting. Additionally, the disappearance of the first local resonance band gap in the inward configuration at high shunt circuit resistances enhances the lower frequency bandwidth, thereby maximizing power output. Table 2 summarizes the effects of different parameters on the vibration attenuation and energy harvesting performance of hierarchical metamaterials.
# Enhanced Learning Behaviors and Ability Knowledge Tracing
## 6 Results and Analysis
### Student Performance Prediction Results and Analysis (**Rq1**)
The experimental results indicate that the length of the learning sequence significantly impacts the model's performance, with the best predictive performance occurring when the sequence length is 200, and the worst when it is 10. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**ASSIST2012**} & \multicolumn{2}{c}{**ASSISTChall**} & \multicolumn{2}{c}{**Algebra0607**} \\ \cline{2-7}  & **AUC** & **ACC** & **AUC** & **ACC** & **AUC** & **ACC** \\ \hline DKT & 0.72430 & 0.71265 & 0.72185 & 0.71963 & 0.81001 & 0.80510 \\ DKT+ & 0.72510 & 0.71312 & 0.72210 & 0.71352 & 0.81104 & 0.80950 \\ DKVMN & 0.71488 & 0.72415 & 0.70630 & 0.70198 & 0.79158 & 0.80428 \\ SAKT & 0.72213 & 0.72750 & 0.78743 & 0.74150 & 0.80011 & 0.77266 \\ LPKT & 0.77189 & 0.75936 & 0.79837 & 0.73774 & 0.76138 & 0.74891 \\ LBAKT & 0.77414 & 0.76072 & 0.81037 & 0.75307 & 0.77945 & 0.75946 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of comparison of models on student performance prediction. As the sequence length decreases, the predictive gap between LBAKT and LPKT increases, suggesting that LBAKT is more adept at handling short sequences and is therefore better suited for application in real-world learning environments.