esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods  
The pilot-scale setup (Figure 2, Figure A1) simulates manure storage and ventilation of deep pits under swine barns (Figure 1) [5]. There are 15 manure simulator units; each unit has a height of 1.22 m (4 feet) and a diameter of 0.38 m (15 inches). Initially, 74.6 L of manure from three different farms in central Iowa was pumped into each storage unit. Biweekly, 9.5 L of manure was added into each simulator unit to simulate storage at a real farm. Flowrates were controlled by FL-3839 ST rotameters (Omega Engineering Inc, USA) with \(\pm\) 2% full-scale accuracy and \(\pm\) 0.25% full-scale repeatability to achieve 7.5 headspace air exchange per hour (ACH) and adjusted to 7.5 ACH when fresh manure was added (Figure 2, Figure A1). Since the source of the inlet air was indoors, the temperature of manure was close to the room temperature. The room has temperature control (and an air conditioning and a space heater) to keep the room around 14 to 21 \({}^{\circ}\)C. In north central Iowa, measured temperatures of the manure pit can be as cold as 9 \({}^{\circ}\)C in the winter and as warm as 22 \({}^{\circ}\)C in the summer [6]. Trial 1 was conducted from December to February, and the mean room temperature for trial 1 was 15 \({}^{\circ}\)C. Trial 2 was conducted from April to May, and the mean room temperature was 16.2 \({}^{\circ}\)C. Trial 3 was conducted from July to September, and the mean temperature was 21.4 \({}^{\circ}\)C. The last trial was conducted from October to December, and the mean room temperature was 16.6 \({}^{\circ}\)C. The application rate and methods of manure additives were followed by each product description (Table A1). Some additive products contain instruction for an application rate based on the manure pit surface area. We converted the per surface area instructions into manure volume by using the standard (in US) space area per head and the average production of manure per head.  
sources of swine manure. There were four trials of experiments conducted. The first two trials tested four manure additive products with n = 3 replicates. The third trial tested three products with n = 3 replicates. The last trial tested only one product with n = 3 replicates. All trials lasted for eight weeks.
# On Self-Interference Cancellation and Non-Idealities Suppression in Full-Duplex Radio Transceivers
### Contributions  
In order to resolve the problem of SI in the FD communication system, several SIG techniques have been proposed in the past. To reduce the transceiver's SI effects on the receiver's performance, both in the analog and digital domain, i.e., RF/analog and digital SI cancellation, much research have been conducted in the radio layer. Any one of the cancellation techniques alone does not have the capability to provide SI cancellation to the desired level. Moreover, existence of non-idealities in the transceiver's components act as a huge hurdle in mitigation of SI.  
In this work, in order to gain high efficiency and SIC to the desired level, different SIC techniques, both in the analog and digital domains, along with linearization techniques of the transceiver's component is presented. This manuscript is focused on the functioning of the FD communication system and mitigation of its impediments at the radio layer. The FD widely linear direct conversion transceiver model is utilized to validate the performance of the proposed methods. The main contribution of this work is setting out the following:  
* Three RF/analog SIC techniques, i.e., CSI, GALL filter and Kautz filter for SI cancellation to level below the receiver's thermal noise floor or sensitivity level.
* A digital SIC technique i.e., Kalman filter for residual SI cancellation.
* Linearization techniques for the suppression of the transceiver's component (LNA, PA, IQ mixer) non idealities, i.e.,
* Baff and BA algorithm for IQ mixer imbalance
* Doherty amplifier technique with feedforward linearization technique and pre-distortion linearization technique for the linearization of PA
* Negative feedback and feedforward linearization technique for LNA.  
Metrics used for FD system evaluation are link-budget analysis, power calculation of different SI signal components, and signal to interference plus noise ratio (SINR). Performance analysis of the FD transceiver's transmitter chain components is carried out separately, and results are shown through constellation diagrams and spectrum analyzers. Tx's IQ mixer performance is analyzed through constellation diagrams. PA linearity analysis is performed by examining PA power spectral density (PSD) and its input to output relationship. Unlike the Tx chain, Rx chain performance analysis is carried out by using the metric bit error rate (BER) vs. signal to noise ratio (SNR). LNA linearity analysis is performed separately and examined through PSD and the output vs. input relationship of LNA with and without linearization techniques.
# Multimodal Deep Learning for Group Activity Recognition in Smart Office Environments
## 3 Deep Multimodal Architecture  
In the following section we provide the details of the problem domain and our proposed approach for activity recognition in office environments and present the structure of the multimodal deep network.  
The area of activity recognition in office environments is rather complex as a computer vision classification problem. In contrast to the problem of object recognition, wherein the goal is to identify distinct objects in images, activity recognition from video data requires scene understanding over a certain time horizon, wherein subjects are moving and interacting. For instance, in order to distinguish between a _presentation_ and a _meeting_, it is not enough to identify the activities of individual users; one must also identify the combinations of activities and interactions between them. Moreover, the presence and detection of certain objects in the scene can inform and help with discerning between different activities. Using a whiteboard, for example, would provided different clues regarding the ongoing activity, as opposed to working on a laptop.  
The proposed procedure for activity recognition essentially consists of two steps. First, we need to extract a joint audio-visual feature representation suitable for activity recognition. To this end, our deep network is composed of two streams. On the one hand, a visual model is used for processing the visual cues, and on the other hand, an audio model is responsible for processing the audio signal.  
Second, a fusion network combines the visual and audio extracted features. To this end, we use an LSTM network [18] to model the spatio-temporal evolution of the feature representation over time in order to perform the classification on video segments. For understanding the behavior and interactions of objects and persons in the scene, it is key to model the spatio-temporal progression of the audio-visual data.
# Optical Link Design for Quantum Key Distribution-Integrated Optical Access Networks
## 4 Integration of CV-QKD Protocol in OANs
### CV-QKD Implementation Technique
#### 4.2.5 Optical Receiver  
The GG02 protocol suggests that [PERSON] modulates two quadratures using Gaussian modulation, while [PERSON] randomly selects and measures one of the quadratures using homodyne detection [22]. Homodyne detection requires an optical coupler for combining the QKD and LO signals, a balanced photodetector (BPD) for measurement, and a phase modulator to lock the LO phase and select the measurement basis. Later research demonstrated that using heterodyne detection, where both quadratures are simultaneously measured, does not compromise security [52]. In heterodyne detection, the QKD signal and LO are mixed via an optical hybrid, and two BPDs are used for detection. This method introduces a 3 dB optical power penalty due to signal splitting but doubles the SKR. While homodyne detection may suffer from excess noise due to real-time phase locking errors, heterodyne detection allows for carrier phase estimation algorithms to compensate for phase noise, enhancing system performance.
## 2 Materials and Methods
### Circadian Stimulus
#### 2.4.1 Calculation Metrics of Illumination
This CS value is derived from the combined SPD of the light, which includes the natural light SPD modified by reflections on the room's interior surfaces as well as the received light flux. Consequently, an illuminance threshold that ensures the appropriate CS can be identified, considering the resulting SPD influenced by the architectural environment and prevailing climatic conditions. Figure 3 depicts the average spectral irradiance distribution derived from the SPD of each sky type, factoring in the indoor reflections within the venue. This distribution served as the basis for determining the appropriate illuminance threshold. Notably, all the analyzed SPDs displayed a marked reduction in the short-wavelength fraction, attributed to the lower spectral reflectance values of the room's interior surfaces for these wavelengths. Using the resulting SPDs shown in Figure 3 and the selected CS values, the minimum illuminance thresholds were calculated using Equation (2), as illustrated in Figure 4. As observed in Figure 4, clear skies require lower illuminance levels to achieve a specific CS value compared to overcast skies. Additionally, it is notable that an illuminance of 300 lx corresponds to a CS value near 0.4, depending on the prevailing climate conditions, while an illuminance of approximately 100 lx produces a CS of around 0.2. Based on these findings, the study establishes two illuminance thresholds that are independent of climate conditions and rely solely on the spectral reflections within the indoor environment. Figure 3: Resulting SPD according to different sky types (clear, intermediate, and overcast), considering the spectral reflections of the inner surfaces.
## 3 The Main Parameters and Indices of MOT
### Conventional Performance Indices
#### 3.2.1 The Atomic Number  
In an MOT, atoms are trapped by the trapping force but can also be hit by background atoms and escape. The number of atoms eventually trapped in the MOT is determined by the capture and escape processes. If the number of atoms is \(N\), the rate of change is  
\[\frac{dN}{dt}=R-\frac{N}{\mathcal{E}}-\beta\frac{N^{2}}{v} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Serial Number** & **Control Parameter Name** & **Unit** \\ \hline
1 & Cooling laser detuning & MHz \\
2 & Cooling laser intensity & mW/cm\({}^{2}\) \\
3 & Cooling laser radius & m \\
4 & Cooling laser polarization & — \\
5 & Magnetic field gradient & G/cm \\
6 & Repumping laser intensity & mW/cm\({}^{2}\) \\
7 & Repumping laser detuning & MHz \\
8 & Compensated field gradient & G/cm \\ \hline \hline \end{tabular}
\end{table}
Table 1: MOT control parameters.  
Figure 3: Atomic motion in an MOT.  
where \(\mathcal{L}\) is the atomic lifetime and \(R\) is the trapping probability. The third term on the right is caused by the double-body collision effect of atoms. This effect can be ignored when the atomic density is low. The trapping probability is related to the number of atoms entering the potential trap at a rate lower than the trapping rate \(v_{c}\) per unit time. It can be calculated as follows:  
\[R\approx An_{0}5\frac{v_{c}^{4}}{\overline{v}^{3}} \tag{6}\]  
where \(A\) represents the correction factor, \(n_{0}\) represents the background gas density, \(S\) represents the total surface area of the potential trap region, and \(\overline{v}\) represents the root-mean-square velocity of the background gas atoms.  
By substituting the initial conditions \(N=0\), the variation rule of the atomic number can be obtained as follows [14]:  
\[\begin{array}{ll}N(t)&=R\mathcal{L}(1-e^{-t/\mathcal{L}})\\ &=\frac{1}{\sqrt{6}}\frac{S}{\sigma_{c}}v_{c}^{4}\Big{(}\frac{m}{2k_{B}T} \Big{)}^{2}(1-e^{-t/\mathcal{L}})\end{array} \tag{7}\]  
where \(\sigma_{c}\) is the collision cross-section of the background vapor atom colliding with the trapped atom, \(m\) is the atomic mass, and \(T\) is the cold atomic group temperature. The formula indicates that the number of atoms in the stable state is directly proportional to the fourth power of the trapping rate. This implies that the number of atoms can be estimated via the trapping rate. Moreover, the number of atoms is affected by the temperature of cold atomic groups and the lifetime of the atoms.  
The atomic number is typically measured via fluorescence detection in practice.
# Privacy and Utility of Private Synthetic Data for Medical Data Analyses
## 3 Data Privatization Techniques
### Differential Privacy  
The definition of Differential Privacy (DP) was introduced by [PERSON] in 2006 [19]. The aim was to establish a privacy guarantee that is independent of any background knowledge. One of the main principles is that it should make no difference for an individual's privacy whether their data are included in a dataset. This property is also reflected in the definition of DP.  
**Definition 1** (\(\epsilon\)-Differential Privacy).: _A randomized algorithm \(\mathcal{K}\) is \(\epsilon\)-differentially private if, for all \(\mathcal{S}\subseteq Range(\mathcal{K})\) and for all databases \(D_{1}\) and \(D_{2}\), which differ by at most one entry, the following holds [20]:_  
\[Pr[\mathcal{K}(D_{1})\in\mathcal{S}]\leq e^{\epsilon}\times Pr[\mathcal{K}(D_ {2})\in\mathcal{S}].\]  
The choice of \(\epsilon\), which is also called privacy budget, is non-trivial. Although it is obvious that a small \(\epsilon\) leads to higher privacy, there are a lot of different values that are recommended or used in the literature. The choice also depends on the use case and which privacy-utility trade-off is acceptable. In addition, there are different ways to fulfill the definition. One common way is to use the Laplacian-distributed noise, while other methods work with randomized responses. Furthermore, there are different types of DP. In the central model, the data are stored in a central database. This database contains the raw data, so individuals must trust it. DP algorithms are applied when data or aggregated results are requested. The local variant of DP applies the DP mechanism before the data are shared. Thus, individuals are not required to trust a central database and instead privatize the data before sharing it.
# Reducing PAPR with Low Complexity Filtered NOMA Using Novel Algorithm
## 3 Simulation Results
### Power Saving Performance  
The proposed SLM algorithm's power-saving performance is evaluated for various transmission schemes, as shown in Table 4 and illustrated in Figure 8a. It is shown that the power performance of the Solid State Power Amplifier (SSPA) is effectively improved after PAPR is reduced by applying F-NOMA. The SLM (S = 4 \(p\) = 4) obtained 70% power saving. In conclusion, the SSPA efficiently enhanced the signal levels.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**S.No** & **Proposed PAPR Algorithms** & **Original PAPR for 256-QAM (dB)** & **PAPR Reduction (dB)** & **Power Saving *100 (\%)** \\ \hline
1 & SLM (S = 4 \(p\) = 4) & 10 & 4.9 & 51\% \\ \hline
2 & SLM (S = 4 \(p\) = 2) & 10 & 5.8 & 42\% \\ \hline
3 & SLM (S = 2 \(p\) = 4) & 10 & 6.7 & 33\% \\ \hline
4 & SLM (S = 2 \(p\) = 2) & 10 & 9  
\end{table}
Table 6: Power saving of SLM in 256-QAM-F-NOMA.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**S.No** & **Proposed Algorithm** & **Transmission Schemes** & **No of Additions** & **No of Multiplications** \\ \hline
1 & SLM (S = 4 \(p\) = 4) & 16-QAM & 12,288 & 20,480 \\ \hline
2 & SLM (S = 4 \(p\) = 4) & 64-QAM & 16,384 & 24,576 \\ \hline
3 & SLM (S = 4 \(p\) = 4) & 256-QAM & 33,088 & 32,768 \\ \hline \end{tabular}
\end{table}
Table 3: Complexity Comparison.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**S.No** & **Proposed PAPR Algorithms** & **Original PAPR for 16-QAM (dB)** & **PAPR Reduction (dB)** & **Power Saving *100 (\%)** [16] \\ \hline
1 & SLM (S = 4 \(p\) = 4) & 8.2 & 2.4 & 70.70\% \\ \hline
2 & SLM (S = 4 \(p\) = 2) & 8.2 & 4 & 51.21\% \\ \hline
3 & SLM (S = 2 \(p\) = 4) & 8.2 & 4.8 & 41.46\% \\ \hline
4 & SLM (S = 2 \(p\) = 2) & 8.2 & 6.6 & 19.51\% \\ \hline \end{tabular}
\end{table}
Table 4: Power saving of SLM in 16-QAM-F-NOMA.  
Figure 7: Graphical representation of Complexity.  
The proposed SLM algorithm's power-saving performance is evaluated for various transmission schemes, as shown in Table 5 and illustrated in Figure 8. It is shown that the SLM (S = 4 \(p\) = 4) obtained 45.26% power saving.  
Figure 8: (**a**) Graphical representation of Table 4. (**b**) Graphical representation of Table 5. (**c**) Graphical representation of Table 6.  
The power-saving performance is shown in Table 6 and exemplified in Figure 8c. The SLM (S = 4 \(p\) = 4) obtained 50% power saving for F-NOMA; therefore, it is established that the SSPA proficiently improved the signal levels.
## 5 Results
### Performance
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Percentage Using Amplitude-Range and**} & \multicolumn{3}{c}{**Classifiers**} \\ \cline{2-4}  & **ANN** & **SVM** & **KNN** \\ \hline
80\% Training and 20\% Testing & 97.87\% & 96.40\% & 96.72\% \\
70\% Training and 30\% Testing & 95.91\% & 95.97\% & 95.63\% \\
60\% Training and 40\% Testing & 94.72\% & 93.53\% & 93.62\% \\
50\% Training and 50\% Testing & 94.40\% & 93.21\% & 93.23\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results for Amplitude-Range and Band-Power Features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No** & **Title** & **Author** & **Features** & **Classifier** & **Accuracy** \\ \hline \multirow{4}{*}{\(1\)} & Automatic epilepsy detection using the instantaneous frequency and sub-band energies of the EEG signals (2011) & [PERSON] and [PERSON] (2012) & frequency and the energies of the EEG signals in different sub-bands & Artificial neural network (ANN) & 94\% \\ \cline{2-6}  & & & maximum value in the non-redundant region, minimum value in the non-redundant region, mean value of the non-redundant region, & \\ \cline{2-6}  & Neural network classifier for the detection of epilepsy (2013) & [PERSON] and [PERSON] (2013) & region, maximum value along the principal & ANN & 81.67\% \\ \cline{2-6}  & & & minimum value along the principal & \\ \cline{2-6}  & & & diagonal, & \\ \cline{2-6}  & & standard deviation along the principal diagonal & & \\ \hline \multirow{4}{*}{3} & Early detection of epilepsy using EEG signals (2014) & [PERSON] and [PERSON] (2015) & bispectral entropy, bispectrum phase entropy, mean bispectrum magnitude, weighted center of bispectrum & \\ \cline{2-6}  & & Epileptic seizure detection in EEG signals using wavelet transforms and neural networks (2015) & [PERSON] et al. (2014) & mean, absolute, median, variance & Feed-forward neural network & 93.23\% \\ \cline{2-6}  & EEG based detection of Epilepsy by a mixed design approach (2016) & [PERSON] and [PERSON] (2016) & mean, standard deviation, variance, FFT, wavelet transform & Support-vector machine & \\ \cline{2-6}  & & & approximate entropy, sample entropy, hurst exponent & Support-vector machine & \\ \cline{2-6}  & & & & \\ \cline{2-6}  & & & & \\ \hline \multirow{4}{*}{6} & Epileptic seizure detection using nonlinear analysis of EEG (2016) & [PERSON] et al. (2015) & \multicolumn{2}{c}{approximate entropy, sample entropy, hurst exponent} & Support-vector machine & \\ \cline{2-6}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Table of Comparison. Figure 13: Classification Performance of KNN.
## 3 Results
Using HPLC, it was determined that PEF had no effect on \(\beta\)-lactoglobulin content and remained the same in the raw milk samples at 3.28 mg/mL (Figures 6a and 7). Similar \(\beta\)-lactoglobulin concentration results were achieved in samples treated with microfiltration. During microfiltration, the filtrate was treated with low-temperature pasteurization for a short period of time. These treatment conditions had only a negligible effect on the \(\beta\)-lactoglobulin concentration, which remained relatively high for MF1 at 2.96 mg/mL and MF2 at 2.41 mg/mL, respectively (Figures 6c and 7). For representation purposes, the \(\beta\)-lactoglobulin standards and samples treated with pulsed PEF are presented in Figure 6b. Milk samples treated with UHT showed the most significant decrease in \(\beta\)-lactoglobulin concentrations, which were not detectable. The HPLC results of the milk samples treated with PEF and UHT are presented in Figure 6d. Milk samples treated with the LTLT pasteurization technique yielded 0.83 mg/mL \(\beta\)-lactoglobulin content (Figure 7). Although relatively high temperatures were not generated at 63 \({}^{\circ}\)C, prolonged exposure for 30 min had a significant effect on protein structure, which corresponded to a 75% decrease in \(\beta\)-lactoglobulin content compared to raw milk samples. Several reports state that heat treatment at temperatures above 60 \({}^{\circ}\)C initiates the unfolding of the globular structure of whey proteins, which results in protein denaturation [55; 56], and a long treatment time of 30 min may result in a decrease in whey protein content during pasteurization. Figure 5: Undenatured whey protein content in milk samples treated with different pasteurization techniques: C—control (raw milk); PEF—pulsed electric field; LTLT—low-temperature long-time; UHT1—ultra-high temperature; UHT2—ultra-high temperature; UHT3—ultra-high temperature; MF1—microfiltration; MF2—microfiltration. Different lowercase letters indicate significant differences between the samples (\(p<0.05\)). Figure 6: HPLC results on \(\beta\)-lactoglobulin content in raw milk samples and those treated with PEF, **(a)**; HPLC results on \(\beta\)-lactoglobulin content in PEF-treated samples and \(\beta\)-lactoglobulin standard, **(b)**; HPLC results on \(\beta\)-lactoglobulin content in milk samples treated with MF and PEF, **(c)**; HPLC results on \(\beta\)-lactoglobulin content in milk samples treated with UHT and PEF, **(d)**. Figure 7: \(\beta\)-lactoglobulin content in milk samples treated with different pasteurization techniques: C—control (raw milk); PEF—pulsed electric field; LTLT—low-temperature long-time; UHT1—ultra-high temperature; UHT2—ultra-high temperature; UHT3—ultra-high temperature; MF1—microfiltration; MF2—microfiltration. Different lowercase letters indicate significant differences between the samples (\(p<0.05\)).