esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Noise in PLC Networks
### Noise Library for NBPLC-PRIME
#### 3.2.3 Noise Signals Measured in Actual Operational Deployments
Community antennas are single-phase power elements that can generate noise due to their power supplies. The fact that there is a relatively large distance between the antenna and the smart meter room causes the cable that feeds the antenna to become in turn a large antenna that simultaneously transmits the noise and radiates. This radiation induces the noise in other cables of other smart meters so that, finally, the noise can be seen in smartmeters different from the one of the antenna, although the maximum noise will always be seen in the smart meter corresponding to the antenna. Figure 8 shows the R phase of the community antenna. Two peaks are observed in frequencies at 41.5 and 83 kHz in a relevant level reaching 95 and 100 dBuV respectively. In S and T phases it was lower (85 dBuV). In this situation, all the smart meters of the centralization lost their connectivity. Figure 9 shows the noise associated to a domestic TV receiver. TV receivers are elements that can generate noise due to their power supplies or, in case of old televisions, by degradation of the demodulator components. As it can be seen in Figure 9, the noise introduced by the TV receiver is somehow colored noise, being close to 90 dBuV in the entire PRIME frequency band. This noise was higher than the received PRIME communication level and, due to the proximity of the centralization with the SS, prevented the communications in the whole PRIME network. Figure 10 shows the noise associated to a water pump. Water pumps are infrequent elements in large residential blocks of buildings, but common in house residential areas. These pumps, generally three-phase equipment, can generate noises due to the operation of their motor. As it can be seen in Figure 10, it is also a kind of colored noise, but reaches lower levels than the TV receiver noise (notably, up to 65 dBuV). Anyway, this noise hampered the communications of all the smart  
Figure 8: Community antenna noise measured in the field (Phase R). Figure 9: TV receiver noise measured in the field. meters in the same smart meter room and even of smart meters located in smart meter rooms further away from the SS. Finally, Figure 11 shows the noise associated to an electronic ballast. In a fluorescent lamp the role of the ballast is twofold: (1) to provide the high voltage necessary for the ignition of the tube; and (2) after the ignition of the tube, to limit the current that passes through it. In both cases, the ballast can generate noise in the PRIME communication band. As it can be seen in Figure 11, the measured noise introduced by this electronic ballast affect the upper part of the PRIME frequency band, behaving as a kind of colored noise that reaches levels up to 85 dBuV. Due to the proximity of the centralization with this noise to the SS, this noise prevented the communications in the whole PRIME network too.
# Arbitrary Motion Style Transfer via Contrastive Learning
## 4 Experiments
### Interpolation  
In this experiment, we investigate content and style interpolation. For content interpolation, we utilize a content code \(w_{c}\) that is a linear combination of two target contents, denoted as \(M_{c}\) and \(M_{s}\). Specifically, we compute \(w_{c}\) as follows:  
\[w_{c}=(1-\alpha)*E_{c}(M_{c})+\alpha*E_{c}(M_{s}),\]  
where \(\alpha\) is a scalar value in the range [0, 1]. Figure 4 illustrates that, as \(\alpha\) increases, the direction of the kicking motion gradually aligns with the direction of the stylized motion. This indicates that the direction of the motion content transitions from that of \"Childlike-kicking\" to a direction closer to \"sexy-kicking\".  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **CRA \(\uparrow\) (\%)** & **SRA \(\uparrow\) (\%)** \\ \hline [PERSON] et al. [6] & 29.83 & **54.94** \\ \hline Ours \(\text{w}/\mathcal{L}=\mathcal{L}_{\text{rec}}+\mathcal{L}_{\text{cyc}}+ \mathcal{L}_{\text{pos}}\) & 50.68 & 14.11 \\ Ours \(\text{w}/\mathcal{L}=\mathcal{L}_{\text{rec}}+\mathcal{L}_{\text{cyc}}+ \mathcal{L}_{\text{pos}}+\mathcal{L}_{\text{contra}}\) & **52.81** & 12.81 \\ Ours \(\text{w}/\mathcal{L}=\mathcal{L}_{\text{rec}}+\mathcal{L}_{\text{cyc}}+ \mathcal{L}_{\text{pos}}+\mathcal{L}_{\text{temp-contra}}\) & 52.12 & 16.03 \\ Ours & 50.75 & 16.71 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Content Recognition Accuracy (CRA) and Style Recognition Accuracy (SRA) for additional quantitative evaluation. The results from [PERSON] et al. [6] are quoted directly from their original paper. The symbol “\(\uparrow\)” indicates that a higher value is preferable; the best-performing results are highlighted in bold.  
Regarding style interpolation, we employ a style code \(w_{s}\) that is a linear combination of two target styles, represented by \(M_{c}\) and \(M_{s}\). Mathematically, \(w_{s}\) is computed as follows:  
\[w_{s}=(1-\beta)*E_{s}(M_{c})+\beta*E_{s}(M_{s}),\]  
where \(\beta\) is a scalar value in the range [0, 1]. Figure 5 demonstrates that, as \(\beta\) increases, there is a noticeable increase in torso and limb movements. This indicates a transition in the motion style from the subtle movements of \"Old-walking\" to the more pronounced movements of \"Proud-walking\".  
These two interpolation experiments demonstrate that our framework establishes a flattened manifold in both content and style dimensions for motion style transfer.
## 2 Modeling Principles
### General Dynamic Vibration Absorbers (DVA) Modeling Approach
#### 2.1.2 Active Mass Damper (AMD) Control and Hybrid Active-Tuned Mass Damper (ATMD) Control  
For a purely active system, the passive control force takes the form of a desired action, \(u_{a}(t)\) determined via a control algorithm such as the Linear-Quadratic-Regulator (LQR), proportional-integral-derivative (PID) controller or similar. With reference to Figure 1c, for the case of AMD control, the force is delivered solely by means of mechanical actuation; thus the actuation force \(f_{a}(t)\) is equal and opposite to the calculated desired action:  
\[u_{a}(t)=-f_{a}(t) \tag{5}\]  
Obviously, for the purpose of limiting the stroke and the requirement of a fail-safe mechanism, an ATMD is found in most practical applications [3,8]. To this end, and with reference to Figure 1d, for an ATMD, the desired force is mathematically expressed as the summation of the passive forces generated by the motion of the mass damper and an additional external force provided by means of mechanical actuation. Because the dynamic characteristics of the mass damper remain unaltered and the desired interaction force has been already calculated by the control algorithm, the required conventional hybrid actuation, \(f_{a\_tmd}(t)\) is determined from:  
\[u_{a}(t)=c_{p}\dot{x}_{r}(t)+k_{p}x_{r}(t)-f_{a\_tmd}(t) \tag{6}\]In Equation (6), the mechanical properties \(c_{p}\), \(k_{p}\) of the device can be selected similarly to a purely passive device. Still, typically a higher damping coefficient \(c_{p}\) is used along with the ATMD device for stroke-restraining purposes [3; 21].
# Constraint Preserving Mixers for the Quantum Approximate Optimization Algorithm
## 4 Full/Unrestricted Mixer
### All-to-All Full Mixer  
For \(|J|=2^{n}\), the full mixer \(T_{A}\) can be written as \(T_{A}=\sum_{j=1}^{n}T_{\text{Ham(j)}}\). For the case \(T_{\text{Ham(2)}}\), the resulting Hamiltonian \(H_{M}\) does not provide transitions between all pairs of feasible states, but we observe that \(H_{M}=\sum_{j,k\in J}(T_{\text{Ham(2)}})_{j,k}|x_{j}\rangle\langle x_{k}|=\sum _{j_{1}}^{n}\sum_{j_{2}=j_{1}+1}^{n}X_{j_{1}}X_{j_{2}}\), i.e., \(H_{M}\) consists of all \(\binom{n}{2}\) possible pairs of Pauli-strings, which contain exactly two \(X\)s. For \(m\leq n\), this can be further generalized to  
\[H_{M}=\sum_{j,k\in J}\big{(}T_{\text{Ham(m)}}\big{)}_{j,k}|x_{j}\rangle\langle x _{k}|=\sum_{j_{1}=1}^{n}\sum_{j_{2}=j_{1}+1}^{n}\cdots\sum_{j_{m}=j_{m}+1}^{n }X_{j_{1}}\cdots X_{j_{m}}, \tag{41}\]  
Figure 6: Valid (white) and invalid (black) transitions between pairs of states, as defined in Theorem 2 for Trotterized mixer Hamiltonians. The first row shows that for \(T_{1}=T_{\text{O(1)},c}\) and \(T_{2}=T_{E(1)}\), the mixer \(U=e^{-i\beta T_{1}}e^{-i\beta T_{2}}\) does not provide transitions between all pairs of feasible states, although \(U=e^{-i\beta(T_{1}+T_{2})}\) does.  
which consists of all \(\binom{n}{m}\) possible pairs of Pauli-strings with exactly \(m\) Xs. The resulting mixer Hamiltonian is therefore given by  
\[H_{M}=\sum_{l_{1}=1}^{n}\bigg{(}X_{j_{1}}+\sum_{j_{2}=j_{1}+1}^{n}\bigg{(}X_{j_{ 1}}X_{j_{2}}+\ldots+\sum_{j_{n}=j_{n-1}+1}^{n}X_{j_{1}}\cdots X_{j_{n}}\bigg{)} \bigg{)}. \tag{42}\]  
This means that the mixer consists of the standard mixer plus \(\binom{n}{k}\) applications of Pauli X-k strings for \(k\) from 2 to \(n\), which is a large overhead compared to the standard X-mixer.
# Upscaling Gross Primary Production in Corn-Soybean Rotation Systems in the Midwest
###### Abstract  
The Midwestern US is dominated by corn (_Zea mays_ L.) and soybean (_Glycine max_ [L.] Merr.) production, and the carbon dynamics of this region are dominated by these production systems. An accurate regional estimate of gross primary production (GPP) is imperative and requires upscaling approaches. The aim of this study was to upscale corn and soybean GPP (referred to as GPPcalc) in four counties in Central Iowa in the 2016 growing season (DOY 145-269). Eight eddy-covariance (EC) stations recorded carbon dioxide fluxes of corn (n = 4) and soybean (n = 4), and net ecosystem production (NEP) was partitioned into GPP and ecosystem respiration (RE). Additional field-measured NDVI was used to calculate radiation use efficiency (RUEmax). GPPcalc was calculated using 16 MODIS satellite images, ground-based RUEmax and meteorological data, and improved land use maps. Seasonal NEP, GPP, and RE (\(\overline{x}\pm\) SE) were \(678\pm 63\), \(1483\pm 100\), and \(-805\pm 40\) g C m\({}^{-2}\) for corn, and \(263\pm 40\), \(811\pm 53\), and \(-548\pm 14\) g C m\({}^{-2}\) for soybean, respectively. Field-measured NDVI aligned well with MODIS fPAR (\(R^{2}=0.99\)), and the calculated RUEmax was \(3.24\) and \(1.90\) g C MJ\({}^{-1}\) for corn and soybean, respectively. The GPPcalc vs. EC-derived GPP had a RMSE of \(2.24\) and \(2.81\) g C m\({}^{-2}\) d\({}^{-1}\), for corn and soybean, respectively, which is an improvement to the GPPmodis product (\(2.44\) and \(3.30\) g C m\({}^{-2}\) d\({}^{-1}\), respectively). Corn yield, calculated from GPPcalc (\(12.82\pm 0.65\) Mg h\({}^{-1}\)), corresponded well to official yield data (\(13.09\pm 0.09\) Mg h\({}^{-1}\)), while soybean yield was overestimated (\(6.73\pm 0.27\) vs. \(4.03\pm 0.04\) Mg h\({}^{-1}\)). The approach presented has the potential to increase the accuracy of regional corn and soybean GPP and grain yield estimates by integrating field-based flux estimates with remote sensing reflectance observations and high-resolution land use maps.  
ACPF; eddy covariance; MODIS fPAR; NDVI; radiation use efficiency
# Heart Failure Prediction Based on Bootstrap Sampling and Weighted Fusion LightGBM Model
## 5 Experimental Results and Analysis
### Comparative Analysis of Model Effects  
#### 5.2.1 The Setting of the Experimental Sample  
The sample number of bootstrap sampling is as follows.  
In the process of optimizing the parameters of the LightGBM model, bootstrap sampling technology is adopted. For each bootstrap sampling, samples are randomly selected from the training set (about 643 samples), and the number of samples extracted is set to be the same as that of the original samples in the training set, that is, the sample set obtained by each bootstrap sampling also contains about 643 samples. This setting is because the purpose of self-help sampling is to simulate different training data sets through repeated random sampling, thus increasing the diversity of model training and improving the generalization ability of the model. Maintaining the number of samples to be consistent with that of the original training set enables the model to encounter similar data sizes and  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Combination Type** & **Accuracy** \\ \hline Age + Gender + ChestPainType + ExerciseAngina & About 79\% \\ ChestPainType + ExerciseAngina + Oldpeak + ST slope & 79–81\% \\ Oldpeak + ST\_Slope + RestingECG + ExerciseAngina & About 84\% \\ RestingECG + MaxHR + ExerciseAngina & 83–84\% \\ Oldpeak + ST\_Slope + RestingECG & 81–83\% \\ Full feature combination & 82–85\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy of different feature combinations.  
distributions during the training process on different bootstrap sample sets. Consequently, this practice enhances the comparability when assessing the model's performance under various parameter combinations.  
The number of samples for cross validation is as follows.  
We use five-fold cross-validation to evaluate the performance of the model. In each iteration of the cross-validation process, the training set, which consists of approximately 643 samples, will be further partitioned into a training subset and a validation subset. Because it is a five-fold cross-validation, the training subset contains about \(643\times(4/5)\approx 514\) samples, and the validation subset contains about \(643\times(1/5)\approx 129\) samples. Through this process of multiple partitioning and validation, a more comprehensive evaluation of the model's performance on different data subsets can be achieved. This approach helps to mitigate the biases arising from data partitioning and renders the performance assessment of the model more robust. At the same time, different parameter combinations are evaluated based on the same cross-validation method and sample size division rules, which ensures the fairness and effectiveness of model comparison.
## 3 Results  
### DTPA Extracted Cd, Pb, Cu, and Zn in the Soil  
The contents of the DTPA-extracted metals contents were significantly decreased with HA treatment (\(p\) < 0.05, Figure 1) as compared with the CK treatment. The HA10.8-amended soil presented lower concentrations of DTPA-extracted metals than the other treated soils, with DTPA-extracted Cd, Pb, Cu, and Zn contents reduced by 37%, 39%, 18%, and 29%, respectively, as compared with those in CK. The reduction of the DTPA-extracted metal fraction was more significant for Cd and Pb than for Cu and Zn.
## 8 Anonymity and Security Analysis
### Routing Privacy  
Although short-path routing is used in this framework, choosing the next hop is done according to certain _probabilistic_ algorithm, which accounts for the residual energy of the sensors and the usage frequency to increase the route privacy, as exhibited in Figure 16. ADV cannot relate routes to nodes due to the triple anonymity. Even if two messages for one sensor follow the exact same route, ADV will see them as if they are two different routes since each hop along the route carries messages with different PIDs.
# How Environmental Factors Affect Forest Fire Occurrence in Yunnan Forest Region
## 4 Discussion
According to previous studies, the combustibility of different tree species varies significantly [63; 64]; oil pine forests are more likely to ignite, which strongly affects the forest fire probability in the region. Altitude dominates as a fire-causing factor in a few county-level models, which might be due to human activities in these tropical forest zones, bringing fire sources and thus triggering forest fires [65]. Additionally, in alpine areas, arson also occurs to maintain grazing grassland [66].
## 6 IoT Encapsulation Network Layer Standards
### 6 TiSCH  
The IPv6-based time-slotted channel-hopping (TSCH) mode is an extension of the IEEE 802.15.4 standard for industrial automation and process control. It converges operational technology (OT) with information technology. The time-slotted channel hopping makes 802.15.4 (TSCH) introduce reliability to the communication in terms of reduced multi-path fading because of its narrow bandwidth channels [42]. It also extends battery life on constrained nodes. A specific portion of bandwidth is allotted for the communication of a node with its neighbor for a fixed interval of time. The standard does not guarantee any scheduling mechanism, hence it is not allowing maximum network throughput in its current form. When neighbors use data for the same application, scheduling poses a relatively bigger network optimization problem and reduces the flexibility of the process. They must be programmed to access media with respect to the information gathered from surrounding nodes. In such a case, a centralized scheduling mechanism can be proposed, but to reduce power consumption, the centralized node must adopt some constraint-based distribution of decision making to one of its high-residual-energy neighbors.
## 2 Materials and Methods
### In Silico Model Development  
In a previous study [30], we used the hugging face [33] implementation of ProtBERT [26]. We again used the hugging face implementation of ProtBERT. ProtBERT, unlike the original BERT model, used 30 encoder layers, whereas the BERT-based model only used 12 encoder layers. With more encoder layers, more in-depth contextual data can be drawn from the inputs. Unlike the BERT model pre-trained on natural language, ProtBERT is pre-trained using protein sequences. In this paper, we chose ProtBERT-BFD, which is pre-trained on the Big Fantastic Database (BFD) data for fine-tuning experiments conducted in [26]. The study reveals that the BFD pre-trained ProtBERT model yields a better understanding of the protein sequence contextual data. To further improve the efficiency of the model, we applied LoRA to the ProtBERT model. Adaptors allow one to train on significantly fewer parameters with minor to no degradation in model performance [28; 29]. Much like the original LoRA paper, we applied low-rank decomposition to the query and value attention heads. The model structure of both ProtBERT and ProtBERT with LoRA is shown below in Figure 1.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Fungi Species** & **Normal** & **Toxin** & **Virulence** \\ \hline _Alternaria alternata_ & 23,204 & 22 & 13 \\ _Apergillus niger_ & 59,030 & 25 & 6 \\ _Bjerkandera adusta_ & 18 & None & None \\ _Chaetomium globosum_ & 10,242 & 9 & 1 \\ _Cladosporium cladosporoides_ & 321 & None & None \\ _Copriellus radians_ & 9 & None & None \\ _Fusarium equisefi_ & 11,820 & 6 & 3 \\ _Fusarium proliferationatum_ & 15,139 & 26 & 5 \\ _Neurospora tetraspermea_ & 20,004 & 6 & 15 \\ _Penicillium brasiliatum_ & 19,957 & 14 & 4 \\ _Penicillium chrysogenum_ & 10,772 & 62 & 4 \\ _Penicillium oxalicum_ & 9536 & 4 & 5 \\ _Planerochaete sordida_ & 16,324 & 2 & None \\ _Schizophyllum commune_ & 12,549 & 6 & 3 \\ _Trichoderma harzianum_ & 47,665 & 74 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of protein sequences collected for the 15 fungal species excluded from the training dataset for comparison with in vitro experiment results.  
Figure 1: (**A**) depicts the original ProtBERT model with an additional classification layer. (**B**) depicts the modified ProtBERT model with LoRA. Both models take protein sequences with a maximum length of 1024. We apply zero padding to sequences shorter than 1024. For (**B**), we apply LoRA to the query and value attention heads in the feed-forward layer.
## 5 Simulation Studies
### Desired Motion and Initial Conditions  
It is assumed that the robot's desired motion consists of three phases:  
* Accelerating with maximum acceleration \(a_{Rmax}\) (m/s\({}^{2}\)) on the distance of \(l_{r}\) (m);
* Steady motion with constant velocity \(v_{Ru}\) (m/s);
* Braking with maximum deceleration \(a_{Rmax}\) (m/s\({}^{2}\)) on the distance of \(l_{h}\) (m). In turn, the desired motion path contains the following:
* A straight line segment of length \(L_{r}\) (m);
* A circular arc of radius \(R_{z}\) (m);
* The second straight line segment of length \(L_{h}\) (m). As a result of turning with maximum angular velocity \(\omega_{0 zu}=v_{Ru}/R_{z}\) (rad/s) and maximum angular acceleration \(\varepsilon_{0 zmax}\) (rad/s\({}^{2}\)), the robot should turn by the angle \(\varphi_{0 zmax}\) (rad).  
The convention is that a positive value of the turning radius \(R_{z}\) (m) means turning to the left (rotation of the robot in accordance with the direction of the \({}^{R}z\) axis), and a negative value to the right. The desired time histories of the robot's motion parameters are generated in the form of polynomials; i.e., for accelerations, a second-degree polynomial is assumed with respect to the time \(t\) (s), and, for velocities, a third-degree polynomial.  
Assuming lengths of acceleration and braking distances \(l_{r}=l_{h}=l\) (m), the necessary maximum acceleration value \(a_{Rmax}\) (m/s\({}^{2}\)) can be determined for the given velocity profile. However, it cannot exceed the maximum value resulting from the dynamics of the drive units. In turn, the maximum value of the angular acceleration \(\varepsilon_{0 zmax}\) (rad/s\({}^{2}\)) can be selected in such a way that, in the case of rotation in place, it corresponds to analogous values of accelerations of the geometric centers of the middle wheels \(a_{Am}\) (m/s\({}^{2}\)).  
Therefore, for the adopted method of generating the desired robot motion parameters, one can use the following dependencies:  
\[a_{Rmax}=3(v_{Rn})^{2}/(4),\quad\varepsilon_{0 zmax}=2a_{Rmax}/W. \tag{43}\]  
It can be noticed that a large value of \(W\) (m) is disadvantageous due to \(\varepsilon_{0 zmax}\) (rad/s\({}^{2}\)) and, consequently, \(\omega_{0 zmax}\) (rad/s).  
Next, based on the knowledge of velocities \(v_{Rn}\) (m/s) and \(\omega_{0 zu}\) (rad/s), accelerations \(a_{Rmax}\) (m/s\({}^{2}\)) and \(\varepsilon_{0 zmax}\) (rad/s\({}^{2}\)), and lengths \(L_{r}\) (m) and \(L_{h}\) (m), as well as angle \(\varphi_{0 zmax}\) (rad), the characteristic time instants are determined.