esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Results of the Supervised Method
#### 3.2.2 Data Analysis and Results
The performance of the models with different predictors can be seen in more detail in Figure 6, where they can be closely compared with ground truth data for a randomly selected day from the testing period. For this day, the model which used lighting as its predictor showed significant underprediction during peak hours, while total floor electricity consumption showed overprediction, especially at 9 o'clock. Figure 5: Measured and predicted occupancy during the test period. The predicted occupancy was calculated using all predictors. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Set of Predictors** & **RMSE (Persons)** & **MBE (Persons)** & **EV (\%)** \\ \hline Lighting & 10.52 & \(-\)1.6 & 93 \\ Equipment & 8.87 & \(-\)2.34 & 95 \\ Light and Equipment & 8.14 & \(-\)2.07 & 96 \\ Total floor electricity consumption & 8.8 & 0.41 & 95 \\ Water consumption & 9.67 & \(-\)0.5 & 94 \\ All predictors & 7.88 & \(-\)0.66 & 96 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the supervised method for occupancy rate prediction shown through three error indices: RMSE, MBE and explained variation (EV). In Figure 7, RMSE is shown for every hour of the testing period. It can be seen that using lighting consumption as a predictor created a larger error in the second half of a workday. This could be explained by employees turning the lights on later in the day or forgetting to turn them off when leaving the office. At 18 o'clock, the error from the model with lighting consumption predictor decreased, most likely because of the scheduled corridor lights being shut down. On the other hand, the model which used water consumption as a predictor showed a significant performance gap, compared to other predictors, around 11 o'clock, which was the usual time for employees to visit the office canteen. Figure 6: Comparison between predicted occupancy of different predictors and with the measured occupancy for a selected day. Figure 7: Comparison between models with different predictors by hourly RMSE for an average day of the test period. Interestingly, from 19 o'clock, the RMSE value showed a nearly constant and similar value between all predictors. The reason for this was the issue with the method of collecting ground truth data, which was explained in Section 2.1.4. This issue is also visible in Figures 5 and 6, where the ground truth does not reach zero occupancies at the end of the day. Moreover, the assumption was that there was nobody in the office during those hours.
# Mechanism and Application of Soilbags Filled with Excavated Soil in Soft Soil Subgrade Treatment
## 2 Principle of Soilbag Treatment for Soft Soil Subgrade  
In the eastern coastal areas of China, there is a large amount of soft soil subgrade distribution. In the process of road construction, a common method for soft soil subgrade treatment is the granular soil replacement method, as shown in Figure 1a. That is, the soft soil is excavated and replaced with granular soil and stones, materials with high strength and permeability characteristics, to improve the bearing capacity of soft soil subgrade quickly. This soft soil treatment method involves the transportation and subsequent burial of the excavated residual soil, engendering myriad concerns, including land occupation, air pollution, and environmental degradation. To better comprehensively utilize the excavated soil, the soilbag-treated subgrade method has been proposed, as shown in Figure 1b. With this method, polypropylene bags are filled with the excavated soil, and the subgrade is backfilled with these soilbags for soft soil treatment.
# Performance Analysis of Soft-Switching FSO/THz-RF Dual-Hop AF-NOMA Link Based on Cognitive Radio
## 2 System and Channel Models
### RF Link  
On the other hand, the PDF and CDF of the unordered variable of the channel gain \(|h_{RUL_{n}}|^{2}\) in the second slot follow the Nakagami-\(m\) distribution and can be represented as [32]  
\[f_{|\overline{h}_{RUL_{n}}|^{2}}(x)=\frac{m_{n}^{m_{n}}x^{m_{n}-1}}{\Omega_{n }^{m_{n}}\Gamma(m_{n})}\exp\left(-\frac{m_{n}x}{\Omega_{n}}\right), \tag{8}\]  
Figure 2: Description of the soft-switching FSO/THz hybrid link.  
\[F_{|\vec{\eta}_{RL_{n}}|^{2}}(x)=\frac{\mathrm{Y}\Big{(}m_{n},\frac{m_{n}x}{\Omega_ {n}}\Big{)}}{\Gamma(m_{n})}, \tag{9}\]  
where \(m_{n}\) represents the fading coefficient and \(\Omega_{n}=d_{n}^{-\alpha}\) is the average power with \(d_{n}\) being the distance of the \(n\)th link and \(\alpha\) the path loss exponent, \(\Gamma(a)=\int_{0}^{\infty}x^{a-1}e^{-x}dx\) denotes the Gamma function, and \(Y(m,x)\) is the lower incomplete gamma function. Specifically, \(\mathrm{Y}(a,x)=\int_{0}^{x}x^{a-1}e^{-x}dx\). Additionally, it is worth noting that \(\mathrm{Y}(a,x)=(a-1)!\)\(\left[1-e^{-x}\sum_{i=0}^{x-1}\left(x^{i}/{i!}\right)\right]\) when \(a\) is an integer greater than or equal to one. In this paper, we consider integer values for \(m_{n}\); thus, we can rephrase the expression \(F_{|\vec{\eta}_{RL_{n}}|^{2}}(x)\) as follows:  
\[F_{|\vec{\eta}_{RL_{n}}|^{2}}(x)=1-\exp\!\left(-\frac{m_{n}x}{\Omega_{n}}\right) \sum_{r=0}^{m_{n}-1}\frac{\left(\frac{m_{n}x}{\Omega_{n}}\right)^{r}}{r!}. \tag{10}\]  
With some algebraic calculations, the respective PDF and CDF for the ordered variable \(|h_{RL_{n}}|^{2}\) can be written as [32]  
\[f_{|h_{RL_{n}}|^{2}}(x)=\sum_{k=0}^{M-n}{(-1)^{k}\binom{M-n}{k}f_{|\vec{\eta}_ {RL_{n}}|^{2}}z(x)\left[F_{|\vec{\eta}_{RL_{n}}|^{2}}(x)\right]^{n+k-1}}, \tag{11}\]  
\[F_{|h_{RL_{n}}|^{2}}(x)=Q\sum_{k=0}^{M-n}\frac{(-1)^{k}}{n+k}\binom{M-n}{k} \left[F_{|\vec{\eta}_{RL_{n}}|^{2}}(x)\right]^{n+k}, \tag{12}\]  
where \(Q=M!/[(M-n)!(n-1)!]\). By substituting the specific parameters of the two users, we can derive the corresponding PDF and CDF.  
It is worth noting that the PN spectrum band is occupied in the second slot. Assuming the channel gain \(|h_{RP}|^{2}\) follows a Rayleigh distribution, its CDF is  
\[F_{|h_{RP}|^{2}}(x)=1-\exp\!\left(-\frac{x}{\Omega_{P}}\right), \tag{13}\]  
where \(\Omega_{P}=d_{p}^{-\alpha}\) holds the same significance as \(\Omega_{n}\).
_Article_  
Two-Step Plasma Treatment on Sputtered and  
Electroplated Cu Surfaces for Cu-To-Cu  
Bonding Application  
[PERSON] 1, [PERSON] 2 and [PERSON] 1,*(c)  
Graduate School of Nano-IT Design Convergence, Seoul National University of Science and Technology, Seoul 01811, Korea  
Department of Mechanical Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea  
Correspondence: [EMAIL_ADDRESS]; Tel.: +82-2-970-6599  
Received: 2 August 2019; Accepted: 26 August 2019; Published: 28 August 2019  
**Featured Application: 3D packaging, Flip chip packaging.**  
**Abstract:** The technology trends of next generation electronic packaging are moving toward heterogeneous 3D packaging systems. One of the key processes of 3D packaging system is Cu-to-Cu bonding, which is highly dependent on the planarized, activated, and oxygen-free Cu surface. A two-step plasma treatment is studied to form a Cu surface that does not react with oxygen and improves the Cu bonding interface quality at low bonding temperature (300 \({}^{\circ}\)C). In this study, the effects of two-step plasma treatment on both sputtered and electroplated Cu surfaces were evaluated through structural, chemical, and electrical analysis. The Cu bonding interface was studied by scanning acoustic tomography analysis after the thermocompression bonding process. Both sputtered and electroplated Cu thin films had the preferred orientation of (111) plane, but sputtered Cu exhibited larger grains than the electroplated Cu. As a result, the roughness of sputtered Cu was lower, and the resistivity was higher than that of electroplated Cu. Based on X-ray photoelectron spectroscopy analysis, the sputtered Cu formed more copper nitrides and fewer copper oxides than the electroplated Cu. A significant improvement in bonding quality at the Cu bonded interface was observed in sputtered Cu.  
**Keywords:** Cu bonding; 3D packaging; nitrogen plasma treatment; copper nitride passivation; low temperature thermo-compression bonding
# Data Security-Based Routing in MANETs Using Key Management Mechanism
## 5 Experimental Results and Comparative Analysis  
The simulation results are evaluated extensively under varying network conditions, such as mobility, network size area, and node count. The performance metrics are obtained and analyzed when the proposed model is implemented by varying the number of nodes and mobility to prove that the proposed model can successfully implement energy-aware trust to provide secure communication via MANETs.
## 2 Mobile Cellular Network Basics  
Mobile Cellular Networks are made up of two main components, namely circuit switched (CS) and packet switched (PS) networks [15]. The CS network is used mostly for voice traffic while the PS network is used for data traffic. In other words, a CS network could be called as a Radio Network and a PS network as a Packet Network, as shown in Figure 1 below.  
GGSN (Gateway GPRS Support Node) assigns the IP and Domain Name System (DNS) addresses to User Equipment (UE) such as mobile phones, smartphones, GPRS devices, _etc._ When the UE connects to the network, the GGSN handles packet data protocol (PDP) contexts [16] and also performs packet forwarding between the Packet Network (PN) and the Internet. Serving GPRS Support Nodes (SGSN) are placed in the most critical location in cellular networks. They have direct connection to almost all elements in a cellular network and have responsibilities in managing data channels, such as the PS domain originated paging channels and providing Call Data Register (CDR) information to billing systems. The Radio Network Controller (RNC) is mostly responsible for managing radio resources. User Equipment firstly communicates with NodeB via the air interface. The NodeB performs tasks similar to those performed by a base station in a 2G network. In addition, all the UEs use different timeslots and signaling channels for voice and data communication.  
In Cellular Networks, different kind of protocols, such as the GPRS Tunneling Protocol (GTP) tunnels, the User Datagram Protocol (UDP) and the IP, are used between devices. Mostly, the IP protocol is used but other upper layer protocols are also used on different layers of the OSI model [17].
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## 3 State of the Art of Sky Wave Sounding
### Whisper
#### 3.4.5 Results and Conclusions  
The outcomes of this project are given as three different study cases at three different frequencies--3.9 MHz, 5.7 MHz and 6.7 MHz [56]. They compute the Scattering function, the delay and Doppler power profile over 140 seconds of wideband PN received signals. By means of a chirp signal they can compute the state of the ionosphere simultaneously to the transmission of wideband PN signals. The goal is to compare both outcomes and better understand the behavior of the ionosphere.  
Study Case 1: 3.9 MHz  
In this case study gathered on 10 th April 07:38 UTC they detected 4 propagation modes that, according the chirp ionogram, can be interpreted as 1E, 1F2, 2F2 and 3F2 modes. The power delay profile shows a delay spread around 4 ms and a Doppler spread of \(\pm\)0.5 Hz. It is an example of a static channel that almost does not change throughout the 140 seconds of the experiment.  
Study Case 2: 5.7 MHz  
The second case study gathered on 9 th April 20:31 UTC shows a more rapidly changing channel within the measure timescale than study case 1. The oblique ionogram shows multiple F-layer returns which corresponds with a series of five multipath modes discernable in the scattering function. The power delay profile shows a delay spread around 10 ms and a Doppler spread of of \(\pm\)5 Hz.  
Study Case 3: 6.7 MHz  
The third case study gathered on 10 th April 08:10 UTC shows once again an almost static channel throughout the 140 s of transmission. The scattering function shows a delay spread of 6 ms and a Doppler spread of \(\pm\)2 Hz.  
As a final conclusion, they present the WHISPER project as a feasible tool to sound the ionosphere in a wide bandwidth (i.e., wider than the standard 3 kHz), which is corroborated by a chirp sounder that simultaneously collect data on the same link. The results confirm the ability of the ionospheric sounder to measure the scattering function with enough multipath and Doppler resolution to measure each path amplitude and phase. This resolution is sufficient to identify mid-latitude multipath conditions which are likely to have an adverse effect on the majority of HF data modems.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Waveform** & **Chip-Rate** & **Delay** & **Multipath** & **Doppler** & **No. of** & **Doppler** & **Measure** & **Processing** \\
**BPSK** & **(kchip/s)** & **(ms)** & **Res. (\(\mu\)s)** & **(Hz)** & **CIRs** & **Res. (Hz)** & **Time (s)** & **Gain (dB)** \\ \hline PN-1023 & 81 & 12.6 & \(\sim\) 10 & \(\pm\)40 & 8192 & 0.01 & 103 & 70 \\ PN-1023 & 61.4 & 16.6 & \(\sim\) 15 & \(\pm\)30 & 8192 & 0.008 & 136 & 70 \\ PN-511 & 40.8 & 12.5 & \(\sim\) 35 & \(\pm\)40 & 8192 & 0.01 & 102 & 67 \\ Barker-13 & 2.4 & 12.5 & \(\sim\) 600 & \(\pm\)40 & 128 & 0.06 & 1.6 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of different WHISPER sounder waveforms
## 2 Preliminaries
### Compressed Sensing Approach  
Above, we briefly discussed how channel estimation problems in UAC, across different channel models and communication schemes, can be converted into a linear programming problem, as shown in (1). A key challenge in solving (1) in the context of UAC is the underdetermined nature of the system. Channel estimation is often performed with fewer training symbols or equations compared to the greater number of unknown channel parameters, i.e., typically \(M\ll N\). Fortunately, the inherent sparsity of both CIR and DD spread function of UWA channels makes it possible to solve (1) by using CS-based techniques [6; 35]. In Gaussian noise environments, the problem is commonly transformed into the \(\ell_{1}\)-norm regularized LS problem \[\underset{\mathbf{x}}{\text{minimize}}\ ||\mathbf{y}-\mathbf{\Phi}\mathbf{x}||_{2}+ \lambda|\mathbf{x}||_{1}, \tag{10}\]  
where \(\lambda\) is the regularization parameter. The unconstrained convex optimization problem above can be solved by various optimization algorithms [21; 23; 25; 34; 36]. However, as demonstrated in [37], the performance and robustness of sparse estimation algorithms based on (10) will be degraded when the measurement \(\mathbf{y}\) is contaminated by impulsive noise or heavy-tailed non-Gaussian noise.  
In our preceding work [28], we presented a robust channel estimation method utilizing an alternative objective function  
\[\underset{\mathbf{x}}{\text{minimize}}\ \tau||\mathbf{y}-\mathbf{\Phi}\mathbf{x}||_{ 1}+||\mathbf{x}||_{1}, \tag{11}\]  
which applies \(\ell_{1}\)-norm regularization to both solution \(\mathbf{x}\) and the residue vector \(\mathbf{r}=\mathbf{y}-\mathbf{\Phi}\mathbf{x}\), and \(\mathbf{r}\) is related to background noise. Compared to (10), optimizing with an \(\ell_{1}-\ell_{1}\) objective function provides a more robust solution when the measurement \(\mathbf{y}\) contains large errors or impulsive noise, as the squared errors imposed by the \(\ell_{2}\)-norm are more sensitive to outliers [38]. \(\ell_{1}-\ell_{1}\) optimization is also widely utilized for denoising and restoration in image processing [39; 40]. In this work, we introduce an improved ADMM-based method that integrates the APG approach with a non-monotone line search strategy to solve the problem in (11), offering a faster convergence and a more robust and more accurate solution for UWA channel estimation in different scenarios.
## 3 Results
### Dynamic Satellite Number Selection Experiment
The IGD values for the optimization results of different algorithms across the entire  
Figure 3: Comparison of DGDOP between NSWOA, NSGA-II, PSO, and GWO algorithms. The true Pareto front refers to the fully ideal front obtained by exhaustively evaluating the DGDOP values of all possible satellite combinations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & **NSWOA** & **GWO** & **PSO** & **NSGAII** \\ \hline
**Runtime (s)** & 0.7821 & 1.0722 & 0.9722 & 2.4873 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Execution time per epoch (unit: seconds). 2185 epochs of the UAV flight are computed. The boxplot and statistical results for the different performance metrics are shown in Figure 4.  
From the comparison in Figure 4, it can be observed that the box for the NSWOA is positioned lower, indicating smaller values, with better median and optimal values. The overall shape of the box is flatter, suggesting good convergence and diversity. The minimum IGD value for the NSWOA is 0.01, outperforming the GWO and NSGA-II algorithms and performing similarly to the PSO algorithm. In terms of average and median IGD values, the NSWOA surpasses the GWO, PSO, and NSGA-II algorithms by approximately 35%, 14%, and 17%, respectively. Moreover, it remains highly competitive in terms of standard deviation and worst-case values, demonstrating that the NSWOA possesses the best overall optimization capability. The experimental results presented in Figures 3 and 4 further confirm that the proposed NSWOA satellite selection algorithm produces solutions that are closer to the theoretical Pareto front. The algorithm demonstrates superior convergence and diversity throughout the entire flight and within a given satellite count range \(n_{i}\leq 10\), the overall optimization performance of the proposed NSWOA still outperforms the other algorithms.
# A Systematic Compact Model Parameter Calibration with Adaptive Pattern Search Algorithm
## 4 Pattern Search Algorithm

Table 1: The list of parameters and specification for the experiment. Lower and upper bound is set up to keep the physical meaning up in the optimization process. The number format for the boundary is the engineering format. The type and the physical parameters for the experiment is N channel MOSFET and length/width = 0.1 \(\upmu\)m/1 \(\upmu\)m, respectively. The value in the parentheses follows by bound value of parameter is the relative variation of the on current by parameter change at the bound compared to the reference, BSIM4’s default parameter. meaningless, a minimum step size is set to 1e-6, as the main termination criterion of the whole process, as well as the fitness target. A detailed interpretation and process flow chart is shown in Figures 1 and 2. Figure 1: The graphical conceptualization of the exploratory move and pattern move of patter search algorithm: (**a**) Exploratory move. In the multi-dimensional parameter space, adjacent points around the exploration basis (blue dot surrounded by black line) are evaluated in the process and the displacement between best point and the exploration basis is set to pattern vector (red arrow). 1-dimensional projection along the pattern vector will be the stage for the following process; (**b**) Pattern move. One-dimensional naive line search is performed with the given pattern vector. The process is terminated when the newest point is not improved more then set the previous point to be the next exploration basis (settle point). Figure 2: Flow chart of an original construction of Hooke-Jeeves method. \(I_{D}\) is the target current vector measured from fabricated MOSFET and \(\mathcal{L}\) is the relative mean-squared error served as a fitness function. In exploratory move, the algorithm evaluates the adjacent points around the exploration basis in all axes and takes the best point among them. In the pattern move, the algorithm evaluates the points where the direction vector reaches until the fitness is no longer improving.
## 3 Materials and Methods
### The \(k\)-NN Classifier from Big Spatial Data Perspective  
The \(k\)-NN algorithm is a popular non-parametric method that can be used for both classification and regression tasks. In following, we will discuss the \(k\)-NN classification problem from the big spatial data viewpoint. The main components of \(k\)-NN classifier are:  
* \(TR\) is a training mobile objects dataset of size \(N\),
* \(TS\) is a testing mobile objects dataset of size \(M\),
* \(\mathbf{o}_{n}\) is a mobile object represented as a tuple in the form of \((f_{n1},f_{n2},\dots,f_{np},cl)\), where \(f_{np}\) is the value of the \(p\)-th feature of the \(n\)-th object and \(cl\) is the class it belongs to, denoted as \(\mathbf{o}_{n}^{cl}\) and
* \(cl\) is only known to \(TR\) dataset.  
In the classification process, for each test object \(\mathbf{t}\in TS\), the \(k\)-NN algorithm searches the \(k\) closest objects in the \(TR\) set, computing the distances (specifically the Euclidean distance) between the test mobile object and all the mobile objects in \(TR\). The distances from all training objects are ranked in ascending order and then, the \(k\) nearest objects \((kmn_{1},knn_{2},\dots,knn_{k})\) are kept to find the dominant class \(cl\). Despite its remarkable performance in real-world applications, it lacks the scalability to manage large-scale datasets. Initially, the time complexity to find the \(k\) nearest neighbors for a single test mobile object is \(O(Np)\), where an extra complexity \(O(Nlog(N))\) for distances sorting process is involved; in this additional complexity, \(N\) is the size of training dataset and \(p\) is the number of object features. The classification process needs to be restated for all the test mobile objects. Additionally, the \(k\)-NN model requires the training data to be stored in memory in order to achieve a fast computation of the distances. However, large-scale \(TR\) and \(TS\) sets may not fit in the RAM memory.
# Investigation of IEEE 802.16e LDPC Code Application in PM-DQPSK System
## 3 PM-DQPSK System and Simulation Results  
The PM-DQPSK coherent optical transmission system is one of the commercialized solutions for high-speed data transmission. It combines the optical polarization multiplexing technique and multiple phase shift keying (MPSK) techniques and applies coherent detection and digital signal processing (DSP) techniques, thereby reducing the implementation complexity and improving the spectral efficiency and OSNR sensitivity. The coherent optical transmission system is constructed by the optical simulation software VPItransmissionMaker10.1, and the digital signal processing compensation is carried out by MATLAB; the performance results before and after implementing LDPC encoding and decoding are compared in this paper.
## 3 Results and Discussion
### In Vitro Biocompatibility Assays  
The cytotoxicity of AgNP was investigated in vitro against two human cell lines. A keratinocyte cell line (HaCaT) was used since the biosynthesized AgNP are intended for topical application; thus, it is critical that they are safe for skin cells. The human leukemia monocyte cell line THP-1 was used as host cells for microbes that infect the skin [45]. These assays were performed using the MTT colorimetric test for THP-1 cells and the MTS colorimetric test in the case of HaCaT cells. Cellular viability data for HaCaT and THP-1 macrophage-like cells exposed to AgNP are shown in Figure 6. AgNP did not show toxic effects in HaCaT cells in the range of tested concentrations. As for THP-1 cells, a dose-dependent reduction in cell viability was observed, with around 100% viability at the lower concentration decreasing to around 50% at the highest AgNP concentration tested. Overall, these findings indicate that the AgNP are not cytotoxic to either macrophages or skin cells. In vitro CC50 (50% cytotoxic concentration) values were > 181 mg/mL for HaCaT cells and 146.03 mg/mL for THP-1 cells.