esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Factor Analysis  
The compiled questionnaire data were subjected to factor analysis to ascertain the main components of the noise sources in the hospital lobby. The contents of the questionnaire were grouped, with the noise situation in the hospital lobby as one group and that in the medical functional area as the other group. The noise situation was divided into two aspects according to the results, with one aspect being the incidental noise timing and the other being the primary noise events for analysis. Events with relatively low correlation were removed during analysis. The experimental results are shown in Table 6.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Team Lobby** & **Factor Loading** & **Eigenvalue** & **Percent of Total Variance Explained** \\ \hline _Factor 1: Lobby primary noise_ & & & \\ (N = 3) (_Kaiser–Meyer–Olkin (KMO) = 0.607_) & & & \\ \hline \hline
**Sound of footsteps and carts** & 0.968 & & \\ \hline
**Door opening and closing sound** & 0.847 & 2.461 & 41.016\% \\ \hline
**Ambulance siren sound** & 0.808 & & \\ \hline _Factor 2: Lobby accidental noise_ & & & \\ (N = 3) (_Kaiser–Meyer–Olkin (KMO) = 0.607_) & & & \\ \hline \hline
**External car sounds** & 0.916 & & \\ \hline \hline
**Talking** & 0.882 & 2.213 & 36.889\% \\ \hline
**Broadcast** & 0.628 & & \\ \hline
**Team Ribbon** & **Factorloading** & **Eigenvalue** & **Percentof total variance explained** \\ \hline _Factor 1: Ribbon primary noise_ & & \\ (N = 3) (_Kaiser–Meyer–Olkin (KMO) = 0.630_) & & & \\ \hline
**Medical machinery sounds** & 0.973 & & \\ \hline
**Sound of footsteps and carts** & 0.890 & 3.012 & 50.196\% \\ \hline
**Elevator** & 0.846 & & \\ \hline _Factor 2: Ribbon accidental noise_ & & \\ (N = 3) (_Kaiser–Meyer–Olkin (KMO) = 0.630_) & & & \\ \hline
**Traffic** & 0.815 & & \\ \hline
**Talking** & 0.708 & 1.784 & 29.741\% \\ \hline
**Emergency room** & 0.626 & & \\ \hline \hline \end{tabular}  
Notes: In this study, IBM SPSS Statistics 22 statistical analysis software was used for analysis.  
\end{table}
Table 6: Analysis of major noise event factors.  
Figure 6: Frequency band (20 Hz–4 kHz) of the 8 hospitals.  
Noise events with low correlation in the lobby group, such as noise from children playing and noise produced by the operation of mechanical equipment, were excluded from the analysis. The results showed that the main noise events common to both sets of data are footsteps, and the noise caused by the sound of medical carts showed the greatest correlation. In occasional noise events, traffic noise is more prominent, followed by noise caused by conversations, and lastly, noise events caused by ambulances and emergency rooms. The difference lies in the fact that the main noise events in the lobby group are also related to the noise caused by opening and closing doors in addition to the noise from ambulances; in comparison, the medical functional area group's highest correlation is with the noise generated by the operation of medical equipment, which is attributed to the concentration of medical equipment in the functional areas.
## 3 Results
### Training Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Parameters (M)** & **FLOPs (G)** & **Sizes (MB)** & **Avg Epoch Time (s)** \\ \hline AlexNet & 57.00 & 10.61/21.22/42.45 & 217.46 & 60/51/47 \\ InceptionV3 & 21.79 & 91.75/183.50/367.00 & 92.90 & 148/111/107 \\ VGG11 & 128.78 & 120.76/241.51/483.02 & 491.26 & 88/81/78 \\ VGG13 & 128.97 & 179.95/359.89/719.78 & 491.97 & 116/106/104 \\ VGG16 & 134.28 & 246.53/493.07/986.14 & 512.22 & 138/129/122 \\ VGG19 & 139.59 & 313.12/626.25/1252.49 & 532.48 & 159/149/142 \\ ResNet18 & 11.17 & 27.92/58.84/111.67 & 42.62 & 65/58/48 \\ ResNet34 & 21.28 & 57.59/115.19/220.37 & 81.18 & 80/61/58 \\ ResNet50 & 23.51 & 64.85/129.70/259.39 & 89.68 & 96/79/76 \\ ResNet101 & 42.50 & 124.57/249.14/498.29 & 162.13 & 145/113/110 \\ DenseNet121 & 6.95 & 45.08/90.15/180.31 & 26.52 & 167/116/97 \\ DenseNet169 & 12.48 & 53.69/107.39/214.77 & 47.63 & 212/142/123 \\ MobileNetV2 & 2.23 & 5.10/10.21/20.42 & 8.50 & 93/64/52 \\ MobileNetV3 & 1.52 & 0.93/1.85/3.70 & 5.80 & 92/63/50 \\ EfficientNetB2 & 7.71 & 11.09/22.19/44.37 & 29.40 & 138/97/74 \\ EfficientNetB3 & 10.70 & 16.15/32.29/64.58 & 40.82 & 150/11/89 \\ SqueezeNet1\_0 & 0.73 & 9.94/19.87/39.74 & 2.78 & 61/54/48 \\ SqueezeNet1\_1 & 0.72 & 3.98/7.96/15.92 & 2.76 & 63/53/48 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of the training period of a model. The table records the model’s total parameters (in millions), FLOPs (in billions), size (in megabytes), and the average duration (in seconds) used for an epoch. Both FLOPs and the average duration of an epoch are listed in ascending order of batch size (16/32/64). In each column, the first four smaller values are marked in green, and the first four larger values are marked in orange. Figure 6: _Cont_.
# Impact of Subsurface Drainage System Design on Nitrate Loss and Crop Production
## 2 Materials and Methods  
### Site Description  
The study field was set up in 2017 near Church Road in Champaign County (40\({}^{\circ}\)3\({}^{\prime}\)10\({}^{\prime\prime}\)-88\({}^{\circ}\)12\({}^{\prime}\)41\({}^{\prime\prime}\)), Illinois. The study field had six hydrologically separated individual subsurface drained plots, each having an area of 2 ha (total 12 ha, with approximately a maximum width of 405 m and a height of 350 m). The predominant soil at the research site was categorized as Drummer Silty Clay Loam soil. The annual average rainfall received in the area is around 967 mm from 2003 to 2022. The six plots at the research site were installed with different tile drainage configurations (spacing and depth). The tiles installed in plots CS-1 and CS-3 were spaced at 12.2 m and a depth of 1.1 m; in CS-2 and CS-4, the spacing was 24.4 m and the depth was 1.1 m; and in CS-5 and CS-6, the spacing was 18.3 m and the depth was 0.8 m (Figure 1). CS-2 and CS-4, and CS-5 and CS-6 have the same drainage coefficient. We installed perforated PVC without a filter for the lateral tiles and double-walled, corrugated, non-perforated PVC for the main tiles.  
All the CS plots were under the corn and soybean rotation system. Fertilizer in the form of urea and ammonium-nitrate solution (UAN32%) at the average rate (533 L/ha) was applied in each plot before planting corn (2019, 2020, and 2022). Additional monoammonium phosphate (MAP 11-52-0) fertilizer and potash were applied in each plot after  
Figure 1: Field layout and drainage system design of CS-field.  
harvesting corn at the average rate of 2263 L/ha and 286 kg/ha, respectively. Additionally, herbicides (atrazine and resicore) were applied before planting corn each year at the average rate of 2.8 kg/ha and 1.12 kg/ha, respectively.
## 5 Conclusions  
Applying artificial intelligence methods provides promising results for AQI forecasting. This study obtained data collected by EPA and CWB of Taiwan over 11 years. Three regions (North: Zhongli, Central: Changhua, South: Fengshan) in Taiwan were considered, including two notorious places (Changhua and Fengshan) for their bad air quality all year round. With good results for \(R^{2}\), stacking ensemble and AdaBoost offer the best performance of target predictions based on three different datasets. To be more specific, the stacking ensemble delivers the best RMSE results, while AdaBoost provides the best MAE results. All results show that SVM yields the worst results among all methods explored, and only provides meaningful results for 1-h predictions. The results also confirm that the two machine learning methods, AdaBoost and stacking ensemble, employed in this study can outperform popular methods in the literature, such as SVM, random forest, and ANN. In other words, AdaBoost and stacking ensemble can be considered new and superior alternatives for AQI forecast.  
This study also indicates that prediction performance varies over different regions in Taiwan. Comparing results from datasets sourced from three different regions displays best results for Fengshan AQI prediction (Southern Taiwan), where performance decay with increased time step is less pronounced than those in Zhongli (north) and Changhua (central). Also, 95% confidence intervals for 1-h, 8-h and 24-h forecast are calculated, respectively. Compared to the single value prediction, the 95% C.I. can provide a better reference to the decision-maker. For example, an event planner can decide if the outdoor activities can go on based on the air quality forecast with better confidence. Future work  
Figure 9: Illustration of Air Quality Monitoring and Forecasting System.  
should focus on improving performance using stacking ensemble, AdaBoost and random forest with hyperparameter optimization, particularly for predictions with larger time steps (F8-AQI and F24-AQI).  
Conceptualization, Y.M., J.R.C.J., and Y.-C.L.; methodology, Y.M. and Y.-C.L.; software, Y.M.; validation, Y.M. and Y.-C.L.; formal analysis, Y.M.; data curation, Y.M.; writing--original draft preparation, Y.M.; writing--review and editing, Y.-C.L. and A.H.-L.C.; visualization, Y.M.; supervision, Y.-C.L.; project administration, Y.-C.L.; funding acquisition, Y.-C.L. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Ministry of Science and Technology, Taiwan, MOST 106-2221-E-155-025, MOST 107-2221-E-155-043.  
The authors declare no conflict of interest.
## 3 Fault-Tolerant Aircraft Controller Design
### Adrc Controller Design  
ADRC, first proposed by [PERSON] [35], is mainly composed of a tracking differentiator (TD), an extended state observer (ESO), and nonlinear state error feedback (NLSEF). In this study, based on the traditional ADRC, a reduced-order ESO was used to reduce the system delay, and a new RBFNN was used to estimate the system signal, while adapting to the occurrence of faults and interference.  
**A Tracking Differentiator (TD)**  
In transition design, the transition signal \(v_{1}\) and its derivative \(v_{2}\) are provided simultaneously. A TD offers better ability to solve the derivative of the reference signal \(\
u\). A second-order TD, if used in a controller, was designed as follows:  
\[\left\{\begin{array}{l}\dot{v}_{1}=hv_{2}(k)\\ \dot{v}_{2}=hthan(v_{1}-v,v_{2},r_{0},h_{0})\end{array}\right., \tag{16}\]  
where \(v_{1}\) is the tracking signal of \(v\), \(v_{2}\) is the derivative of \(v_{1}\), \(\;\;\)\(han(\cdot)\) is the optical time solution of the expected output signal \(v_{1}\) to converge to \(\
u\) and ensures the fast convergence  
Figure 2: Fault-tolerant aircraft controller.  
of the expected signals to the input signals without overshoot, \(h\) is the ultimate amplification coefficient of acceleration, and \(r_{0}\) and \(h_{0}\) affect the control accuracy of the system, where \(fhan(\cdot)=-r_{0}\big{(}\frac{a}{d}-sign(a)\big{)}\cdot s_{a}-r_{0}sign(a)\). The corresponding parameters were designed as follows:  
\[\begin{cases}d=h_{0}r_{0}^{2},a_{0}=h_{0}v_{2},y=v_{1}+a_{0}\\ a_{1}=\sqrt{d(d+8|y|)}\\ a_{2}=a_{0}+sign(y)(a_{1}-d)/2\\ s_{y}=(sign(y+d)-sign(y-d))/2\\ a=(a_{0}+y-a_{2})s_{y}+a_{2}\\ s_{a}=(sign(a+d)-sign(a-d))/2\end{cases}. \tag{17}\]
## 2 Related Work
### Machine Learning for Path Loss Estimation
In this case, to avoid time consumption and expensive measurement campaigns, the PL values to train/validate the ML algorithms were generated with a 3D ray-tracing model in the city center of Tripoli, Greece. As a result, they obtained estimations with a Mean Absolute Error (MAE) less than 3 dB for the three ML algorithms, which prove that ML can be both a fast and accurate approach to approximate complex PL models (e.g., ray-tracing). Looking for further improvements, we use in our research the Ensemblestrees: the Bagging algorithm, which is a combination of Random Forest algorithm and the Bagging technique to reduce the standard deviation of the error (we refer the reader to Section 3.1.2). Further, we unlock the opportunities that such a quick ML-based PL model brings. Wireless network planning using (ML-approximated) advanced PL models is made possible, allowing for better results and more flexibility in the planning process.
# Microarchitectural Characterization on a Mobile Workload
## 7 Results  
Our experiment gathers events in the sampling method as explained. The sampling period specifies the number of such events that are allowed to occur between two samples. In this experiment, we set 10 ms as the default. After several trials of the test, the experimental results are close to reliable data.  
Table 5 shows the summarized result of instruction mix, cache performance, IPC, branch performance, and bus access. The instruction mix explains instruction-level stress. Here, we can see that integer instructions are the most executed operations. L1, L2, and L3 cache performances are also described. However, the overall cache performance is not quite meaningful since there are three distinct sections in Geekbench 5. For IPC, if the IPC number is high (bigger than 1.5), this means the workload is more likely to be a synthetic workload or highly optimized workload. In real-world apps, IPCs are generally from 0.5-1 based on our previous study. We also studied the overall branch impact. The experimental result explains that the branch performance impact is minimal. However, some low branch performance workloads need to consider branch improvement.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **KPI** & **Formula** \\ \hline \multirow{8}{*}{Instruction mix} & Crypto \% & 0.97\% \\  & Integer \% & 52.81\% \\  & Float \% & 21.16\% \\  & LD/ST \% & 25.07\% \\  & (Sum of Instruction mix) & 100.00\% \\ \hline IPC & IPC & 1.96 \\ \hline \multirow{8}{*}{Cache} & L1D TLB Miss Rate & 0.39\% \\  & L1I Cache Miss Rate & 0.21\% \\  & L1D Cache Miss Rate & 5.03\% \\  & L2D Cache Miss Rate & 13.54\% \\  & L3D Cache Miss Rate & 23.49\% \\  & L1D TLB MPKI & 1.93 \\  & L1I Cache MPKI & 0.63 \\  & L1D Cache MPKI & 18.57 \\  & L2D Cache MPKI & 7.82 \\  & L3D Cache MPKI & 2.99 \\ \hline \multirow{2}{*}{Branch} & Retired Branch Miss Rate & 1.88\% \\  & Retired Branch MPKI & 2.71 \\ \hline \multirow{4}{*}{Others} & Frontend Cycle Idle Rate & N/A \\  & Backend Cycle Idle Rate & N/A \\ \cline{1-1}  & Bus Access Per Cycle & 0.14 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summarized result.  
Figure 1: Main thread in image compression.  
In the following sections, we describe the details of microarchitecture analysis, which includes instruction mix, IPC, cache, branch, and other performance-related events.
## 6 Performance Evaluation
### Communication Overhead  
The communication overhead is the additional communication overhead caused by the increment of revocation messages and signature sizes. Tables 1 and 2 show the additional message sizes in bytes for the algorithm according to Formulas (5) and (8).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \(CN_{i}\) & \(M_{i}\) & \(th_{c}\) & \(UID_{i}\) & \(Pub_{i}\) & \(Pub_{i}/Contr\) & \(T_{i}\) & \(Sig_{i}\) & \(\alpha_{i}\) & \(SW_{i}\) \\ \hline
4 & 64 & 1 & 32 & 64 & 64 & 2 & 64 & 1 & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Revocation message sizes (bytes).  
Therefore, the total cost of a one-time revocation voting message is \(4+64+1+32+64+64+2+64+1+1=297\) bytes. The communication cost of the voting process is \(4+1+32+32+64+1+64=166\) bytes. The communication cost of RVWL containing _UIID_, \(SW_{xnew}\) (1 byte), and \(\alpha_{x}\) is n \(\times\) (32 + 1 + 1) = 34 \(\times\) n bytes. Therefore, the communication overhead of the whole event is (34 + 166 + 297) \(\times\) n = n \(\times\) 497 bytes where n is the total number of voters, which are in Section 6.3.
# Single Trace Analysis on Constant Time CDT Sampler and Its Countermeasure
## 3 Single Trace Analysis on a CDT Sampler
### Remarks on the Proposed Attack  
As denoted in Section 2, for the public-key \((A,b)\), a standard LWE-based cryptosystem takes \(s\) as the secret key where \(b=As+e\) for an error matrix \(e\) sampled from the discrete Gaussian distribution. If \(e\) is revealed, then \(As=b-e\) so that \(s\) can be solved in polynomial time using the Gaussian elimination. As we demonstrated above, any lattice-based cryptosystem that uses the constant time CDT sampler is vulnerable to our attack. This includes the FrodoKEM proposed by [PERSON] et al. and Lizard proposed by [PERSON] et al. (2015, 2018). The secret key of FrodoKEM is \((s\|pk,S)\), where \(pk\) is the public-key, \(s\) is a random seed chosen uniformly at random, and \(S\) is a sample through the Gaussian sampler ([PERSON] et al., 2018). Although we can directly obtain \(S\) through our STA, the secret key cannot fully be recovered since there is no information relating to \(s\). However, our attack reduces the substantial amount of entropy of the secret key. For a numerical example, consider FrodoKEM-640, which aims at the brute-force security of AES-128. The size of \(S\) is equal to \(640\times 8\times 2=10240\) bytes, while the size of \(s\) equals to 16 bytes. Considering the public-key, we can recover 99% of the secret key through our attack. On the other hand, the secret key of Lizard is \(S\), where \(S\) satisfies the equation \(B=-AS+E\) for the public-key \((A,B)\) and \(E\) sampled from the Gaussian distribution ([PERSON] et al., 2018). Therefore, by performing our STA to recover \(E\), we were able to obtain \(S\) through Gaussian elimination.
## 4 Experiments and Results  
The proposed LETCP aims to achieve good pre-training effects with unlabeled data by using small amounts of labeled data to reach good segmentation accuracy. Experiments were performed in terms of the pre-training effect and segmentation accuracy.
## 3 Results
### Head-Neck Angles  
As shown in Table 4, there were statistically significant decreases in the head-neck angles between before and after the saddle horse conversion training in all events of Walk and in E2, E3, E5, and E6 of Canter. In Walk, the head-neck angle started at 75.54\({}^{\circ}\), increased until E2, began to decrease until E3, and then increased in E4. In this way, it went up and down at every event until the last event. As a result, the average head-neck angle in Walk was 76.98 degrees. In addition, it was found that the head-neck angles decreased after retraining in all events of Walk and Canter. In Canter, the head-neck angle, which started at 66.65 degrees, gradually increased until E7, and Canter's overall average head-neck angle was 68.65 degrees.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & **Walk** & **Walk** & **P** & **Canter** & **Canter** & **t** & **\(p\)** \\  & & **(Before)** & **(After)** & & & **(Before)** & **(After)** & **t** & **\(p\)** \\ \hline \multirow{3}{*}{Center of mass} & x & 33.10 \(\pm\) 20.40 & 23.20 \(\pm\) 14.91 & 2.546 & 0.016 \({}^{*}\) & 22.13 \(\pm\) 12.82 & 11.73 \(\pm\) 4.96 & 2.559 & 0.023 \({}^{*}\) \\  & y & 492.03 \(\pm\) 77.07 & 496.40 \(\pm\) 97.54 & \(-\)0.158 & 0.875 & 524.93 \(\pm\) 75.54 & 522.67 \(\pm\) 82.89 & 0.077 & 0.940 \\  & z & 115.90 \(\pm\) 3.63 & 116.57 \(\pm\) 4.28 & \(-\)1.646 & 0.110 & 114.53 \(\pm\) 2.59 & 114.07 \(\pm\) 3.24 & 0.640 & 0.533 \\ \hline \multirow{3}{*}{Center of head} & x & 31.67 \(\pm\) 14.49 & 26.45 \(\pm\) 15.85 & 2.106 & 0.043 \({}^{*}\) & 22.88 \(\pm\) 19.09 & 12.06 \(\pm\) 8.42 & 2.812 & 0.013\({}^{*}\) \\  & y & 532.73 \(\pm\) 78.32 & 539.73 \(\pm\) 98.19 & \(-\)0.296 & 0.769 & 601.35 \(\pm\) 45.61 & 593.06 \(\pm\) 32.90 & 0.350 & 0.731 \\ \cline{1-1}  & z & 162.06 \(\pm\) 11.85 & 165.55 \(\pm\) 10.74 & \(-\)1.612 & 0.117 & 155.18 \(\pm\) 16.80 & 155.29 \(\pm\) 12.53 & \(-\)0.035 & 0.972 \\ \hline \multicolumn{8}{c}{\({}^{*}\): \(p\) \(<\) 0.05.} \\ \end{tabular}
\end{table}
Table 2: Displacements of the center of mass and the center of the head. Unit: cm.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Walk** & **Walk** & & **Canter** & **Canter** & **(After)** & **t** & **\(p\)** \\  & **(Before)** & **(After)** & **t** & **\(p\)** & **(Before)** & **(After)** & **(After)** & **t** & **\(p\)** \\ \hline \multirow{2}{*}{Center of mass} & 1.58 \(\pm\) 0.26 & 1.57 \(\pm\) 0.31 & 0.079 & 0.938 & 4.20 \(\pm\) 0.56 & 4.09 \(\pm\) 0.67 & 0.474 & 0.643 \\ \cline{1-1}  & 1.73 \(\pm\) 0.28 & 1.71 \(\pm\) 0.30 & 0.167 & 0.868 & 4.83 \(\pm\) 2.49 & 4.63 \(\pm\) 1.23 & 1.038 & 0.315 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The center of mass and the center of head velocities. Unit: m/s.
# Context-Aware Enhanced Application-Specific Handover in 5G V2X Networks
### AR Implementation Constraints for V2X  
To effectively integrate AR into V2X systems, some of the key challenges related to infrastructure and interoperability must be addressed. Handover (HO) is a process that is central to the maintenance of seamless and uninterrupted communications, especially in 5G V2X networks where UEs move rapidly across the network. HO ensures that UEs are continually connected with the best signal quality and low latency. These connection qualities are critical to vehicular applications such as autonomous driving and Augmented Reality (AR). The HO process must be effective enough to reduce packet loss, delays, and connectivity failures to enhance system reliability and user experiences in V2X environments. The implementation of heterogeneous networks (HetNets) creates complexities that could hinder the efficient implementation of V2X technologies in all cases of use. HetNets comprise diverse types of dense small cells (SCs) within a short range of areas, resulting in the frequent switching of connections and link failure. Owing to high network density, traditional handover techniques are found to be inadequate and/or inefficient in V2X networks, where User Equipment (UE) generally moves at high speed, and HO failure rates and service interruptions, ping-pong, and unnecessary handovers increase exponentially [4; 5]. The ping-pong occurs when the UE connects to a new cell and then reverts back to the source cell [6]. Network congestion causes handover inefficiencies that affect resource allocation. Quality of service (QoS) is a requirement that needs to be addressed, and traditional HO usually does not address QoS. Latency requirements are  
Figure 1: Immersive technologies for V2X.  
highly significant in AR V2X networks, and traditional HO can result in a higher delay, which can negatively impact safety and low-latency requirements; the seamless handover in HetNets is challenging in these aspects. A predictive and proactive handover is now a requirement for a high-speed network environment. A mechanism that allows handovers to scale well is required to optimize the connectivity and maintain a reliable connection for AR applications in a V2X environment.