esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sample Collection and Analyses  
Based on the sewage discharge of enterprises in the industrial park, the distribution of sensitive points within a certain range in the surrounding area, the original use function of the land parcels, the characteristics of contamination, the soil monitoring network in the industrial park, and the surrounding area were constructed. One hundred samples of differently utilized land surface soil (0-20 cm) were collected; 60 samples were from construction land and 40 from agricultural land. To accurately determine whether the soil was polluted, the pollutant type, and the degree of pollution, sampling points were designed by combining professional judgment and systematic random distribution (Figure 1).  
Soil samples were collected in strict accordance with the China Soil Environmental Quality Risk Control Standards for Soil Pollution of Agricultural Land (GB15618-2018) [19], Soil Environmental Quality Risk Control Standards for Soil Pollution of Construction Land (GB36600-2018) [20], Technical Specification for Soil Environmental Monitoring (HJ/T166-2004) [21], Determination of eight Effective State Elements in Soil (HJ804-2016) [22], and other relevant technical specifications. The samples were prepared using non-metallic tools and utensils to remove impurities such as plant roots and gravel (at least 2 kg per portion), packed in cloth bags, dried naturally, crushed with a wooden stick, passed through a 2 mm aperture sieve, mixed and weighed, and then sent to the laboratory for analysis. Sample testing was performed at the Testing Center of the China Inspection Body and Laboratory Mandatory Approved by the qualified Kunming Geological Survey Institute of the General Administration of Metallurgical Geology of China. The sample testing process adopted national-level standard substances (GBW series) for quality control. Each batch of the analytical process involved quality control measurements of parallel double samples. Eight elements (As, Pb, Cu, Zn, Cd, Hg, Ni, and Cr) were detected. The Cr content was determined using flame atomic absorption spectroscopy. The As and Hg contents were determined using atomic fluorescence spectrometry. Cd, Pb, Ni, and Cu contents were determined by inductively coupled plasma mass spectrometry. Zn content was determined  
Figure 1: Map of the distribution of soil sample collection points in an industrial park in Kunming.  
using inductively coupled plasma emission spectrometry. Finally, pH was determined using a potentiometric method.
# Effect of Space Configurational Attributes on Social Interactions in Urban Parks
## 3 Results
### Relationship between Space Characteristics and Social Interaction
For social groups, DtoR, which represents the depth of space to the city main road network, is positively and negatively associated with interaction intensity on the pathways and in the activity zones, respectively. In all four models, space scale factors (pathway length and zone area) were found to be positively associated with the number of observed people involved in social interaction activities (B = 0.011-0.37, Sig. \(<\) 0.001). With the increase of pathway length and zone area, the capacity to accommodate people increases, which leads to a larger number of observed people. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Controlled Variables**} & \multicolumn{1}{c}{**Predictor Variables**} & \multicolumn{1}{c}{**Coef. (B)**} & \multicolumn{1}{c}{**SE**} & \multicolumn{1}{c}{**Sig.**} & \multicolumn{1}{c}{**VIF**} & **Overall Model** \\ \hline \multirow{4}{*}{Pathway} & Personal Group & Length & 0.027 & 0.003 & 0.000 & 1.026 & R\({}^{2}_{\text{adl}}\) = 0.147; \\  & (643, 13\%) & NAIN-1K & \(-\)4.983 & 1.151 & 0.000 & 1.385 & Sig. = 0.000 \\  & & NACH-10K & 2.758 & 0.865 & 0.002 & 1.370 & & \\ \cline{2-8}  & Social Group & DtoR & 0.003 & 0.000 & 0.003 & 1.014 & R\({}^{2}_{\text{adl}}\) = 0.028; \\  & (425, 9\%) & Length & 0.011 & 0.004 & 0.006 & 1.014 & Sig. = 0.000 \\ \hline \multirow{4}{*}{Activity Zone} & Personal Group & Area & 0.37 & 0.042 & 0.000 & 1.108 & R\({}^{2}_{\text{adl}}\) = 0.492; \\  & (516, 10\%) & NAIN-1K & 120.333 & 22.275 & 0.000 & 1.108 & Sig. = 0.000 \\ \cline{2-8}  & Social Group & Area & 0.056 & 0.008 & 0.000 & 1.170 & R\({}^{2}_{\text{adl}}\) = 0.351; \\  & (3915, 68\%) & DtoR & \(-\)0.016 & 0.004 & 0.000 & 1.170 & Sig. = 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Stepwise regression of the number of observed people with space characteristics. Figure 5: Effect of space type and activity type on social interaction activity in parks.
## 2 System Models  
### RDSS Signal Model  
The outbound signal bandwidth of BeiDou-3 is \(2491.75\pm 8.16\) MHz, which is twice as much as that of the old system. The outbound signal of BeiDou-2 uses convolutional code as the channel coding method, whereas BeiDou-3 adds Turbo code, and at the same time, it uses the combination of frequency-guided branch and data branch to replace the former dual data branch in terms of signal branching. The frequency-guide branch is used at the receiving end to track and demodulate the spread spectrum signal, ensuring that the receiver is able to correctly decode and process the navigation messages from the satellite. The outgoing BeiDou-3 RDSS signal adopts Unbalanced Quadrature Phase Shift Keying (UQPSK) with a direct-sequence spread spectrum. UQPSK is a deformation of Quadrature Phase Shift Keying (QPSK), and the UQPSK is two BPSK signals with different transmitting power and spreading gain. The block diagram of the outgoing signal modulation is shown in Figure 2.  
The BeiDou RDSS signal, acquired by the receivers, can be expressed as follows:  
\[S(t)=A_{p}C_{p}(t)d_{p}(t)\cos(2\pi f_{0}+\phi_{0})+A_{d}C_{d}(t)d_{d}(t)\sin( 2\pi f_{0}+\phi_{0}) \tag{1}\]  
where \(A_{p}\) and \(A_{d}\) are the signal amplitudes of the p-branch and d-branch; \(C_{p}\) and \(C_{d}\) are the spreading code sequences of the p-branch and d-branch; \(d_{p}\) and \(d_{d}\) denote the data codes of the two branches; \(f_{0}\) is the signal carrier frequency; and \(\phi_{0}\) presents the initial phase. From the above Figure 2, it can be seen that the P-branch does not use channel coding. The d-branch uses convolutional code coding in the common segment and Turbo coding in the dedicated segment. The fixed information rate of the public segment is 4 kbps, and the symbol rate is 8 kbps after channel coding, and the baseband information rate of the dedicated segment has various cases, and the symbol rate will be unified to 32 kbps after Turbo coding. The simulation obtains the spectrogram of the BeiDou S signal, as shown in Figure 3.  
Figure 2: redRDSS signal modulation block diagram.
# The Role of the Directivity of Various THz Detectors in Multiplexing Systems
## 2 Materials and Methods  
### Detectors  
Four different detectors have been used in the experimental evaluation. Three of them are based on Schottky diodes, and the fourth is based on a field-effect transistor (FET). The first three are zero-biased detectors, while FET needs a gate voltage--in our case, 1V was applied. The Schottky diode detectors were obtained commercially from Virginia Diodes, Inc. Photographs of the detectors used can be seen in Figure 1.  
Two detectors (WR51 and WR34) were mounted inside the WR-5.1 and WR-3.4 waveguides, limiting the frequency band to the ranges of 140-220 GHz and 220-330 GHz, respectively. Detectors employ a simple conical horn (WR51) and a diagonal horn (WR34) to couple the power from the free space. According to the manufacturer, the gains of these antennas are equal to 21 dB (for WR51) and 26 dB (for WR34).  
In the third detector (QOD--Quasi-Optical Detector), the Schottky diode was attached to a log-periodic planar antenna and mounted on the flat surface of an HR-Si (High-Resistivity Silicon) hemispherical lens. This antenna and lens configuration has a directivity of about 24-30 dB [19]. This quasi-optical detector works in a wide range of frequencies from 0.1 to 1 THz.  
The last detector (FET) was a silicon MOSFET manufactured by the Institute of Electron Technology in Warsaw in cooperation with the Institute of Radioelectronics and Multimedia  
Figure 1: Photographs of the detectors used in the experiments: (**a**) Schottky diodes in the waveguides with horn antennas (two different waveguides inside); (**b**) Schottky diode with hemispherical lens and (**c**) field-effect transistor with patch antenna.  
Technology of the Warsaw University of Technology. The transistor was fabricated on a 40 \(\upmu\)m membrane etched within the SOI (Silicon On Insulator) substrate [20]. The source and gate electrodes were connected via microstrip transmission lines to a patch antenna [21]. The antenna was designed for a 250-270 GHz band. The typical directivity for patch antennas is approximately 5-7 dB [22]. All three transistor electrodes were connected through bonded gold wires (aprox. 2 mm long) to external leads in a ceramic holder.  
The antenna directivity parameter allows us to roughly predict which detector should be better suited for the characterization of highly convergent beams. However, it does not provide quantitative information on the geometric limitations of optical systems in which these detectors may be used. Therefore, in this paper, we focus on directivity, expressed as an acceptance angle.
# Recent Lipid Membrane-Based Biosensing Platforms
## 3 Lipid Membrane-Based Biosensing
### Polymeric Lipid Membranes Supported on Graphene Microelectrodes  
Graphene nanomaterials have been used in biosensors because of their advantages, such as enhanced physicochemical properties (mechanical, electrical and thermal), large surface-to-volume ratio, high biocompatibility, and low toxicity. The large surface-to-volume ratio minimizes the device size and provides rapid response times and lower sensitivity without biofouling. Our group has constructed a device that was composed from a stabilized lipid membrane on a copper wire that contained graphene nanosheets [(69; 70)]. These nanodevices have been used for the rapid determination of food toxicants, environmental pollutants, and compounds of clinical interest [(71; 72; 73)].  
The preparation of graphene microelectrodes has been extensively described in the literature [(71; 72; 73)]. \(N\)-methyl-pyrrolidone (NMP) was mildly sonicated for 180 h and centrifuged at 700 rpm for 2 h which provides a homogeneous dispersion (\(\sim\)0.4 mg/mL). This dispersion was poured onto a copper wire (0.25 mm in diameter), which was placed on a glass microfiber filter and the solvent was evaporated. The copper acted as the connection for the electrochemical experiments.  
The method of preparation of the lipid membrane biosensors was previously described in detail [(71; 72; 73)]: The stabilized lipid films were prepared by polymerization, as described above.  
The \"receptor\" molecules were incorporated in these lipid film devices prior to polymerization by injecting 15 \(\upmu\)L of the \"receptor\" suspension on the polymerization mixture. The filter-supported polymeric BLMs were finally mounted onto the copper wire that contained the graphene nanosheets.
## 5 Experimental Verification
### Efficiency  
Figure 21a and 21b show the efficiency curves in the buck (\(k\) = 1.5) and boost (\(k\) = 0.667) modes, respectively. The overall efficiency in boost mode is slightly higher than that in buck mode. At medium and high power levels, the efficiency ranking is EIOS-TPS = CSO-TPS \(>\) CSO-DPS \(>\) SPS. At low power levels, the ranking is EIOS-TPS \(>\) CSO-TPS \(>\) CSO-DPS \(>\) SPS. As shown in Figure 21, EIOS-TPS demonstrates a significant efficiency improvement over CSO-TPS, CSO-DPS, and SPS in the low-power region. The maximum efficiency  
Figure 19: Experimental waveforms of high power (706 W) using EIOS-TPS when \(V_{1}\) = 80 V and \(V_{2}\) = 120 V. (**a**) The waveforms of the voltages and current during operation. (**b**) Switching waveforms.  
Figure 20: Current-stress variation with output power \(P\) when \(V_{1}\) = 80 V and \(V_{2}\) = 120 V.  
improvement of EIOS-TPS over the second-best strategy, CSO-TPS, is 2.2% in buck mode and 2% in boost mode, consistent with theoretical analysis. EIOS-TPS achieves ZVS and low current stress across the full load range, ensuring the highest efficiency in both buck and boost modes. In contrast, CSO-TPS exhibits ZCS issues under light-load conditions, leading to higher switching losses. Since switching losses dominate at low power levels in the DAB converter, the efficiency improvement of EIOS-TPS is particularly pronounced in this region.
## 3 Methodology
### Data Preprocessing  
The CICIDS2017 dataset, while comprehensive, contains missing or NaN (not a number) values that require careful handling during preprocessing to ensure high-quality data. The preprocessing began with merging all eight constituent files into a unified dataset before addressing any missing values. Next, duplicate entries were removed, and columns containing only a single unique value were discarded. The remaining NaN values were imputed and leading spaces stripped from feature names for consistency. After these cleaning steps, samples were taken from the dataset. In the case of multi-class classification, the Normal class was removed after sampling. Outliers were then removed using LOF to  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**File Names (csv File Format) + Activity Day** & **Instances** & **Classes** \\ \hline Friday Working Hours Afternoon DDos & 225,745 & Benign, DDoS \\ \hline Friday Working Hours Afternoon PortScan & 286,467 & Benign, Port Scan \\ \hline Friday Working Hours Morning & 191,033 & Benign, Bot \\ \hline Monday Working Hours & 529,918 & Benign \\ \hline Thursday Working Hours Afternoon Infiltration & 288,602 & Benign, Infiltration \\ \hline Thursday Working Hours Morning WebAttacks & 170,366 & Benign, Web Attack Brute Force, Web Attack Sql Injection, Web Attack XSS \\ \hline Tuesday Working Hours & 445,909 & Benign, FTP Patator, SSH Patator \\ \hline Wednesday Working Hours & 692,703 & Benign, DoS-GoldenEye, DoS-Hulk, DoS Slowhttptest, DoS-Slowloris, Heartbleed \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset and Class Types [16].  
Figure 2: Sample distribution of the CICIDS2017 dataset for each class.  
ensure the dataset was free from extreme values that could have distorted model performance. Then the data were split into input and output variables. Numerical features were then normalized using MinMaxScaler 1.2.2 to ensure consistent scaling across the inputs. Following these preprocessing tasks, the dataset was split into training and testing sets. To address class imbalance, the enhanced hybrid ADASYN-SMOTE technique was applied for oversampling in binary classification and enhanced SMOTE was used for oversampling in multi-class classification to generate synthetic samples within the training set. Moreover, ENN was employed for undersampling the training data, ensuring a balanced class distribution for both binary and multi-class tasks. The preprocessing steps effectively cleaned, balanced, and optimized the dataset, ensuring it was well-prepared for model training and positioned to deliver high-quality and dependable results.
# Optimizing Energy Efficiency in Cloud Data Centers:
## 4 System Model  
### Power Model and QoS Metrics  
#### 4.1.1 Power Model  
In our system model, a data center is defined as a collection of physical machines (PMs). Each PM can host multiple VMs. The power model in a data center is determined by the CPU, memory, storage, and cooling system, which primarily govern the energy consumption of a server. Numerous studies [24] have shown that server energy consumption is defined as a linear relationship to its CPU utilization:  
\[E(u)=K\times E_{\max}+(1-K)\times E_{\max}\times u \tag{3}\]  
where  
* \(E_{\max}\) represents the energy consumed by a fully utilized server.
* \(K\) represents the average consumption by an idle server.
* \(u\) represents the CPU utilization.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Uses RL/DRL** & \begin{tabular}{c} **VM** \\ **Sensitivity** \\ \end{tabular} & \begin{tabular}{c} **Energy** \\ **Optimization** \\ \end{tabular} & \begin{tabular}{c} **SLA** \\ **Violation** \\ \end{tabular} & \begin{tabular}{c} **Main Methodology** \\ **Type** \\ \end{tabular} \\ \hline [PERSON] et al. [18] & ✗ & ✗ & ✗ & ✗ & \begin{tabular}{c} Heuristics and \\ Metaheuristics \\ \end{tabular} \\ \hline [PERSON] et al. [19] & ✗ & ✗ & ✗ & ✗ & \begin{tabular}{c} Heuristics \\ \end{tabular} \\ \hline [PERSON] et al. [21] & ✗ (DQL) & ✗ & ✗ & \begin{tabular}{c} DQL \\ \end{tabular} \\ \hline [PERSON] et al. [22] & ✗ (Q-learning) & ✗ & ✗ & \begin{tabular}{c} Hybrid: Q-learning + \\ Game Theory (V2 PQL) \\ \end{tabular} \\ \hline [PERSON] et al. [23] & ✗ (Multi-obj RL) & ✗ & ✗ & \
\begin{tabular}{c} RL \\ \end{tabular} \\ \hline Our work & ✗ (RL + Firefly) & ✗ & ✗ &
\begin{tabular}{c} Hybrid: Metaheuristic \\ (Firefly) + RL \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of related methods for VM consolidation according to RL/DRL adoption, energy optimization, SLA minimization, and VM sensitivity consideration.  
Since CPU utilization can fluctuate over time due to workload variability, it is treated as a time-dependent function, denoted as \(u(t)\). Therefore, to estimate a server's total energy consumption, the following formula is used:  
\[E_{total}(u)=\int_{t}^{\infty}E(u(t))dt \tag{4}\]
# Confidence Learning for Semi-Supervised Acoustic Event Detection
## 1 Introduction
To improve the training efficiency, we compress the number of iterations of the framework into two stages: in the first stage, pseudo-labels and the evaluation of pseudo-label quality are generated. In the second stage, unlabeled data are used distinguished according to the quality of pseudo-labels. Specifically, since the posterior probability cannot effectively measure the quality of the pseudo-label, we introduce a method of training confidence in the first stage without additional confidence annotation. Inspired by [24], the first-stage model is made to predict audio events and confidence value at the same time. In the second stage, we use unlabeled data discriminately, increasing the weight of the data with a more reliable label in the training process. The contributions of this paper are as follows:  
1. C-SAED realizes the co-training of classification and confidence with only classification labels in the first stage by designing a multi-task model. The experimental results show that the generated confidence can effectively measure the correctness of the label. 2. Compared with the traditional self-training method, differentiated training rather than screening strategies in the second stage effectively improves the utilization efficiency of unlabeled data. Our experiments illustrate that the training effect is significantly improved under the same number of iterations. 3. C-SAED uses the mean teacher model as the backbone of each stage model that effectively fuses two semi-supervised methods: the consistency principle and pseudo-labels. The ER decreases compared to adapting mean teacher only.
## 4 Discussion
Due to the reduced effective index and lower modal confinement, the reduced height-waveguides have a threshold that is >3 dB higher than that of standard waveguides (500 nm \(\times\) 220 nm) [20], thus allowing the use of higher power signals in the ONU. In particular, while the threshold power (in the waveguide) for standard waveguides that causes significant nonlinear effects is close to \(-\)1 dBm, it is necessary to reach about +4 dBm to observe the same effect in the reduced-height structures [19]. As expected, the measured propagation loss of the reduced-height (i.e., 500-800 nm \(\times\) 100 nm) Si-waveguides is lower than that of standard (i.e., 500 nm \(\times\) 220 nm) Si-waveguides [19]. While the standard waveguides on the DUT had propagation losses of 2.8-3.0 dB/cm, the propagation losses of the reduced-height waveguides was 2.4-2.7 dB/cm for the 500 nm \(\times\) 100 nm cross-section, and 1.7-2.0 dB/cm for the 800 nm \(\times\) 100 nm cross-section. When comparing our results with [14], which appears to be the only existing publication reporting similar performances, it appears that using rib waveguides has a similar effect to reducing the waveguide height, probably due to the optical mode extending further away from the waveguide axis. The solution reported in [14] allowed an increase in the coupling coefficient of 220-nm height waveguides, and the realization of high performance filters, thanks to the realization of a small bus-ring gap (180-nm, obtained by high resolution lithography). On the other hand, the use of rib waveguides imposes a strong limitation on the minimum curvature radius, and thus on the maximum FSR, which in fact, was limited to about 8 nm. Thanks to our design based on reduced height waveguides, we achieved better performances, both in terms of insertion losses (0.8 dB) and FSR (12.8 nm).
# Overload Mitigation of Inertial Grid-Forming Inverters Under Frequency Excursions
## 4 Performance Evaluation of Overload Mitigation Strategies
### Grid RoCoF Disturbance Test
#### 4.2.1 Overload Mitigation Performance  
In the proposed testing scenario, the lack of an overload mitigation strategy results in a 50% overload during the first frequency excursion. The VP strategy reduces the overload by half, to 25%. This is achieved by disabling the primary frequency regulation  
Figure 13: Power response of GFM inverter using different overloading protection schemes. (**a**) Grid frequency profile. (**b**) Active power response. (**c**) 50 to 49.5 Hz transient. (**d**) 49.5 to 50.25 Hz transient.  
during overload conditions. The lack of frequency regulation power can be identified in the time range [2; 3] s, where the active power is kept to 1 pu despite the frequency deviation. Removing the frequency regulation might not be enough as the inertia of the GFM inverters increases.  
Conversely, the three overload mitigation strategies can further reduce inverter overloading by decreasing inertial contribution. The power response of these strategies is shown in detail in Figure 13c. Parallel PI and EFS show nearly identical responses, as both overloading mitigation strategies are based on a PI controller which is tuned following the same criterion. Both strategies show a damped response, without overshoot, and a maximum overload of 0.05 pu. This value corresponds to the expected inertial power for a GFM inverter with an equivalent inertia of 0.64 s at a RoCoF of 2 Hz/s, according to (7). The AL also shows a good overloading mitigation capability. However, its underdamped response slightly increases the overload to around 0.07 pu due to the overshoot. The achieved overshoot matches the analytical results of a system with a damping of \(\xi=0.32\), according to (10).  
The slight difference in the response of the parallel PI and the EFS is due to the simplifications that are carried out in the parallel PI. In the small-signal analysis of this strategy, the PSL dynamics are reduced to its high frequency dynamics in order to simplify its transfer function. The small difference in the response of both strategies validates this approach.  
On the other hand, the AL shows a steady-state power limitation of around 0.01 pu. This error is observable when t \(<\) 1 s, where the active power is limited to 0.99 instead of 1 pu. The reason for this error is that minimum and maximum angles are originally derived under the assumption of a purely inductive filter (17). In practice, the filter has a resistive term \(R_{c}\) that slightly modifies this angle. For high \(L_{c}/R_{c}\) ratios, (4) can be modified to consider the impact of the resistance according to [43].