esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, a particle swarm optimization with a sigmoid increasing inertia weight (SIPSO) algorithm is proposed and the parameters of the sigmoid increasing inertia weight have been investigated. Based on the simulation results, conclusions can be drawn as follows:  
1. After comprehensively considering the results of the three criterion functions and structural damage identification, the parameters \(a=2\) and \(b=0.5\) are selected as the optimal parameters of the SIPSO algorithm.
2. Compared with the other three PSO-based algorithms, the SIPSO algorithm corresponds to the optimal function value with all three criterion functions. The convergence curve of the SIPSO algorithm is located at the bottom and converges rapidly, which is better than the other three methods. After verifying with the three criterion functions, the SIPSO algorithm is the best method among the four PSO-based algorithms.
3. Considering the numerical simulation results of the three cases of structural damage identification, the identification results of the PSO algorithm are the worst, while the identification results of the SIPSO algorithm are the best among the four PSO-based algorithms. Meanwhile, the SNPSO algorithm and the SAPSO algorithm have some improvements compared with the PSO algorithm.
4. Finally, the SIPSO algorithm has many advantages in structural damage identification, such as fast convergence rate, high identification accuracy, and the ability to address the noise interference problems excellently. The SIPSO algorithm is preferred for structural damage identification in the field due to its remarkable performance.  
Conceptualization, Z.C. and T.H.T.C.; methodology, Y.W.; validation, X.L. and T.H.T.C.; formal analysis, Z.C.; investigation, X.L.; resources, Z.C.; data curation, Y.W.; writing--original draft preparation, Y.W.; writing--review and editing, S.Z.; funding acquisition, Z.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, China, grant number \"U2004184\"; Training Plan for Young Key Teachers in Colleges and Universities in Henan Province, China, grant number \"2021 GGJS078\"; Key Sci-Tech Research Project of Henan, China grant number \"202102310272\"; Special Joint Research Project of Zhengzhou City and NCWU, China grant number \"2021014\"; Fund of Innovative Education Program for Graduate Students at North China University of Water Resources and Electric Power, China grant number\" YK2020-15\".  
Not applicable.  
This work is jointly supported by the National Natural Science Foundation of China (Grant Number U2004184), Training Plan for Young Key Teachers in Colleges and Universities in Henan Province (Grant Number 2021 GGJS078), Key Sci-Tech Research Project of Henan, China (202102310272), Special Joint Research Project of Zhengzhou City and NCWU, China (2021014), and Fund of Innovative Education Program for Graduate Students at North China University of Water Resources and Electric Power, China (Grant Number YK2020-15).  
The authors declare no conflict of interest.
# Capacity-Operation Collaborative Optimization for Wind-Solar-Hydrogen Multi-Energy Supply System
## 3 Bi-Level Capacity-Operation Collaborative Optimization Method
### Objective Function  
#### 3.2.1 Lower-Level Objectives  
The goal for the secondary level of optimization is to maximize the rate of renewable energy consumption. This research defines the primary renewable energy rate (PRER) as the target function for this lower-tier optimization model [36]. The intention is to minimize wasted wind and solar energy by devising a suitable operational strategy (detailed in Section 2.2). This approach aims to utilize as much renewable energy as feasible for either power generation or energy storage.  
\[PRER=\frac{E_{useful}}{E_{tot}}=\frac{\sum\limits_{t=1}^{8760}(P_{\text{PV}}^{ \text{user}}(t)+P_{\text{WP}}^{\text{user}}(t)+P_{\text{CSP}}^{\text{user}}(t )+P_{\text{PEMFC}}^{\text{user}}(t)+Q_{\text{H2}}^{\text{user}}(t))+Q_{\text{HST }}^{\text{rest}}(8760)+Q_{\text{TES}}^{\text{rest}}(8760)}{\sum\limits_{t=1}^{8760 }(E_{\text{wind}}^{\text{power}}(t)+E_{\text{solar}}^{\text{power}}(t))} \tag{2}\]  
PRER is defined as the output \(E_{\text{useful}}\) of the system to the renewable energy input \(E_{\text{tot}}\) to the system. Among them, \(P_{\text{WP}}^{\text{user}}(t)\), \(P_{\text{PV}}^{\text{user}}(t)\), \(P_{\text{CSP}}^{\text{user}}(t)\), and \(P_{\text{FEMFC}}^{\text{user}}(t)\) are the electricity directly supplied to the user with WP, PV, CSP, and PEMFC, respectively. \(Q_{\text{H2}}^{\text{user}}(t)\) is the hydrogen energy output. \(Q_{\text{HES}}^{\text{rest}}(8760)\) and \(Q_{\text{TES}}^{\text{rest}}(8760)\) are rest energy in HES and TES, separately. \(E_{\text{wind}}^{\text{power}}(t)\) and \(E_{\text{solar}}^{\text{power}}(t)\) are the wind and solar radiation resources, respectively.  
This research aims to plan the system's yearly hourly power output, employing the LP technique for the secondary model's resolution. The final solution's parameters encompass LCOE and \(E_{\text{CO}_{2}}\). It is essential to establish boundaries for the secondary model, factoring in the power, TES system, and other modules, to guarantee that the system's scheduling remains within a logical scope. A detailed constraint model can be found in reference [37].
## 1 Introduction
This paper focuses on a fully optical measurement method utilizing the chord method, and integrates several chords in the same setup. A fully optical approach would allow a speed-independent measurement and reduce further dependencies, such as on the track properties. The lack of a fixed reference system due to the movements of the vehicle is a relevant disturbance for this concept. Displacements and tilting of the measurement setup can influence the measurement. A system with four triangulation laser sensors is built and tested for subsequent use on the train under laboratory conditions. While a single chord with non-contact measurement requires three sensors, four sensors can consequently be used to evaluate four chords and achieve greater robustness. This paper shows the extent to which a measurement method with four sensors based on the chord method is robust against expected external influences.
# Power Stability Analysis and Evaluation Criteria of Dual-Infeed HVDC with LCC-HVDC and VSC-HVDC
## 4 Evaluation Criteria for the Power Stability of Hybrid Dual-Infeed HVDC Systems
### Effective Short Circuit Ratio (ESCR)
#### 4.2.2 Case 2: VSC-HVDC Constant AC Voltage Control Mode  
According to the analysis in Section 3.4, when the VSC-HVDC is in constant AC voltage control mode, the power stability of the system is independent of the rated transmission power of VSC-HVDC. For the AC system, the VSC converter station is equivalent to a generator with reactive capacity constraints. The connection of VSC-HVDC shortens the electrical distance from the converter bus of LCC-HVDC to the constant voltage point. Therefore, the generator characteristics of VSC-HVDC should be considered when calculating the ESCR. In this case, the power stability of the system is mainly related to the parameter \(Z_{12}\). In the following, the relationship between PSI and ESCR is investigated under different \(Z_{12}\).  
Figure 9 shows the curves of PSI versus ESCR when \(P_{\text{dN2}}\) = 0.5 p.u., \(Z_{12}\) is 0.6 p.u., 0.8 p.u., 1.0 p.u. and 1.2 p.u., respectively. It can be seen that under the same \(Z_{12}\), PSI increases with the increase of ESCR. The value of CESCR is slightly affected by the parameter \(Z_{12}\). The variable range of CESCR is 1.56-1.65.  
Figure 8: Curves of PSI versus ESCR for different \(Z_{12}\) under VSC-HVDC constant reactive power control mode.
## 3 Background and Methods
### Data Acquisition and Processing  
#### 3.1.1 Dataset Acquisition  
The data to perform the coconut yield estimation was acquired from the Trivandrum region. Here, every 15 min, the yield environment was monitored by IoT sensors, and  
Figure 1: Block diagram of BILSTM-LFSOA.  
the data was transformed into a daily variable for study. Here, the daily variables were collected from the year 2011 to 2021 (10 years of data). The following sensors were used in the monitoring: temperature sensors, humidity sensors, soil moisture sensors, rainfall sensors, wind speed, and direction sensors, solar radiation sensors, soil pH sensors, and nutrient sensors. The variables that they measured provide the input features of the model.
* The temperature sensor helped measure the existence of heat energy present in the soil; thus, the minimum and maximum temperatures were taken as 20.7 \({}^{\circ}\)C and 21.1 \({}^{\circ}\)C, respectively.
* The humidity sensor frequently monitored the humidity, which confirmed that the crop persisted in the perfect range (70% average).
* The soil moisture sensors measured the water content present in the soil (average 370 to 600).
* The rainfall sensor was used to determine whether there was a risk of flooding based on the rainfall range in various regions, which was taken as 7 mm on average.
* The wind speed and direction sensor measured the speed and direction of the wind in the crop field, which was taken as 2.4 m/s on average.
* The solar radiation sensors measured broadband and flux density irradiance with an average of 450, 600, and 470 W/m\({}^{2}\).
* The soil pH sensors measured the pH of the soil, which was essential since it affected the accessibility of plant nutrients with an average of 7 pH.
* The nutrient sensors precisely measured the nutrient levels in crop fields with an average of 1.67.  
The data generation is depicted in the following Figure 2. The overall process of the proposed model is mentioned as follows,  
Stage 1: Initially, the agricultural field was monitored using IoT devices (sensors) such as temperature, humidity, wind speed, rainfall, precipitation, and solar radiation.  
Stage 2: Depending on the obtained data from these IoT devices, the datasets were collected. In-depth information about the collected data is given in Section 3.1.1.  
Stage 3: The collected environmental data was pre-processed using min-max normalization, then the redundant features were removed in the feature selection through LASSO.  
Stage 4: Moreover, the prediction outcomes were improved by BILSTM where the hyperparameters were optimized by means of the LFSOA. These performances were measured in terms of recall, accuracy, f1-score, and precision.
# Detecting Deepfake Voice Using Explainable Deep Learning Techniques
## 1 Introduction
### Audiovisual Interpretability  
Traditionally, audio has been analyzed through spectrograms extracted from raw audio by Fourier transform [14]. For a human voice, the analysis is made using a mel spectrogram, which filters the spectrogram through a mel filterbank to emphasize low-frequency spectra in which most of the energy is concentrated [15]. Tasks, such as speaker verification, use x-vector or d-vector to make the process efficient [6; 7]. However, the requirement of domain knowledge and absence of interpretability made the use of spectrograms and embedded vector features, respectively, less suitable for non-experts.  
For human manual analysis, visualized audio data in the form of a spectrogram is traditionally used [16]. Characteristics, such as distribution of the energy, formant, frequency-wise characteristics, or general waveform are used for the analysis. However, recognizing these characteristics requires more than just intuitive human perception. The need for domain knowledge makes this method less user-friendly for non-experts compared to visual data-related tasks. Owing to the following two reasons, we achieved effectiveness of interpretability with the corresponding format: (1) lack of intuitive recognition based on visualized interpretability, and (2) tendency of the XAI methods observed on image classification. As a means of converting visualized interpretation back to audio, we used the Griffin-Lim algorithm.  
On CNN-based simple models, regardless of the XAI methods used, the attribution scores show energy dependency on the spectrogram. Given the tendency of the scores, the attribution-score-based recomposed audio is audible. When an LSTM layer is added, Deep Taylor loses its energy dependency and makes the transcript of the recomposed audio unrecognizable but shows significant differences between human and deepfake voices on rhythm and pitch variances. In general, a deepfake voice shows relative monotonousness.  
The major contribution of this paper is that it suggests two novel ideas on deepfake detection, i.e., adaptation of XAI for spoken language and multi-modal interpretability at a human perception level. Visual and audio interpretation, respectively, enabled interpretation of model tendency on general spoken language data and intuitive recognition of characteristics that are not effortlessly recognized when only visual interpretation is available.  
The remainder of the paper is organized as follows. In Section 2, we introduce related works, i.e., explainable deepfake image detection and explainable speech recognition. In Section 3, preliminaries and datasets are described. In Section 4, methods and motivations are introduced along with the experimental environments. In Section 5, we suggest inter pretation based on the attribution score of the model. Finally, in Section 6, the study is summarized along with its limitations and future research directions.
## 4 Dynamic Analysis of Debris Flows in Niumiangou Valley
### Debris Flow Velocity  
As the debris flow disaster in Niumiangou valley is a typical viscous debris flow, this paper adopts the calculation method of viscous debris flow velocity proposed by the Ministry of Land and Resources of the People's Republic of China and Lanzhou Institute of Glacial Permafrost, Chinese Academy of Sciences, respectively, to determine the debris flow velocity of this watershed outbreak.  
1. The calculation method of viscous debris flow velocity proposed by the Ministry of Land and Resources of the People's Republic of China is shown in Equation (3) [23]: \[V_{\rm C}=H_{\rm C}^{2/3}I_{\rm C}^{1/2}/n_{\rm C}\] (3) where \(V_{\rm C}\) is the debris flow cross-section average flow velocity, m/s; \(H_{\rm C}\) is the average mud depth of the calculated cross-section, m; \(I_{\rm C}\) is the hydraulic gradient of debris flow, %, determined by the weighted average gradient from the formation area to the circulation area of the induced watershed; and \(n_{\rm C}\) is the roughness of gully bed inducing debris flow.  
A field measurement of the Niumiangou valley \(8\cdot 14\) debris flow determined the average mud depth of the section to be \(H_{\rm C}=3.8\) m and the weighted average slope from the formation area to the circulation area to be \(I_{\rm C}=427.9\)%. The gully bed roughness can be selected as \(n_{\rm C}=0.067\), according to reference [23]. Therefore, according to Equation (3), the average flow velocity of the Niumiangou valley \(8\cdot 14\) debris flow is calculated as \(V_{\rm C}=23.77\) m/s.  
2. The calculation method of viscous debris flow velocity proposed by the Lanzhou Glacier and Frozen Soil Research Institute of Chinese Academy of Sciences is shown in Equation (4) [24]: \[V_{\rm C}=M_{\rm C}H_{\rm C}^{2/3}I_{\rm C}^{1/2}\] (4) where \(M_{\rm C}\) is the roughness coefficient of the debris flow gully bed; the other symbols have the same meaning as above.  
Combined with the field investigation of the Niumiangou valley \(8\cdot 14\) debris flow and the references [23;24], the roughness coefficient \(M_{\rm C}=14\) was selected. Similarly, the average velocity of the Niumiangou valley \(8\cdot 14\) debris flow can be calculated as \(V_{\rm C}=22.30\) m/s.  
The average velocity of the debris flow obtained by the above two methods is averaged to determine the average velocity of the Niumiangou valley \(8\cdot 14\) debris flow. Therefore, the average velocity of the Niumiangou valley \(8\cdot 14\) debris flow is \(V_{\rm C}=23.04\) m/s.
## 4 RF Energy-Harvesting Techniques
### Single-Band vs. Multi-Band RF Energy Harvesting
Each added frequency band increases the challenge of minimizing cross-band interference and achieving compact form factors, particularly when integrating tunable matching circuits or broadband rectifiers. This complexity can impact size, cost, and integration with existing wireless sensor networks. The integration of broadband antennas and tunable matching networks is a promising approach to address these challenges. A comparison of the two approaches reveals key trade-offs. Single-band RFEH systems offer high efficiency within the targeted frequency band and a simpler circuit design, making them suitable for dedicated power transfer applications such as RFID and wireless sensors operating in a fixed-frequency environment. However, they are ineffective in scenarios where the targeted RF source is unavailable. Multi-band RFEH, on the other hand, enables energy collection from multiple sources, making it more adaptable to dynamic RF environments. Nevertheless, the trade-off lies in the difficulty of maintaining consistent performance across a wide range of frequencies, often requiring multi-stage rectification circuits or switchable matching networks, which can increase power losses and reduce net efficiency. This advantage comes at the cost of increased design complexity, as multi-band rectennas require sophisticated impedance matching networks to maintain efficiency across different frequencies.
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 3 Background in Programmable Data Plane and 5G and Beyond
### 5G and Beyond Technology and Architecture
#### 3.2.2 5G System Architecture  
In the first stage of Release 15, 3 GPP has defined the complete architecture of a 5G system (5 GS) [31]. It describes a set of characteristics and specifications necessary for the deployment of an operational infrastructure for mobile networks based on a 5 GS. During the second stage, the mechanisms for data and service connectivity, as well as the general 5G architecture, were established. Figure 4 illustrates a simplified 5 GS architecture based on a service-based representation [32]. In this description, every network function (NF) (e.g., NSSF) provides a service which can be consumed by any other NF (e.g., AMF). NFs interact with each other using either dedicated protocol point-to-point interfaces (e.g., N1) or through API-based interfaces (e.g., Nnssf). As can be observed, there is a separation of the control plane and the user plane.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**KPI** & **Key Use Case** & **Values** \\ \hline Peak Data Rate & eMBB & DL: 20 Gbps, UL: 10 Gbps \\ \hline Peak Spectral Efficiency & eMBB & DL: 30 bps/Hz, UL: 15 bps/Hz \\ \hline User Experienced Data Rate & eMBB & DL: 100 Mbps, UL: 50 Mbps (Dense Urban) \\ \hline
5\% User Spectral Efficiency & eMBB & DL: 0.3 bps/Hz, UL: 0.21 bps/Hz (Indoor Hotspot); \\  & eMBB & DL: 0.225 bps/Hz, UL: 0.15 bps/Hz (Dense Urban); \\  & & DL: 0.12 bps/Hz, UL: 0.045 bps/Hz (Rural) \\ \hline Average Spectral Efficiency & eMBB & DL: 9 bps/Hz/TRxP, UL: 6.75 bps/Hz/TRxP (Indoor Hotspot); \\  & & DL: 7.8 bps/Hz/TRxP, UL: 5.4 bps/Hz/TRxP (Dense Urban); \\  & & DL: 3.3 bps/Hz/TRxP, UL: 1.6 bps/Hz/TRxP (Rural) \\ \hline Area Traffic Capacity & eMBB & DL: 10 Mbps/m\({}^{2}\) (Indoor Hotspot) \\ \hline User Plane Latency & eMBB, uRLLC & 4 ms for eMBB and 1 ms for uRLLC \\ \hline Control Plane Latency & eMBB, uRLLC & 20 ms for eMBB and uRLLC \\ \hline Connection Density & mMTC & 1,000,000 devices/km\({}^{2}\) \\ \hline Energy Efficiency & eMBB & Capability to support high sleep ratio and long sleep duration to allow low energy consumption when there are no data (e.g., above 6 GHz) \\ \hline Reliability & uRLLC & 1–10\({}^{-5}\) success probability of transmitting a layer 2 protocol data unit of 32 bytes within 1 ms in channel quality of coverage edge \\ \hline Mobility & eMBB & Up to 500 km/h \\ \hline Mobility Interruption Time & eMBB, uRLLC & 0 ms \\ \hline Bandwidth & eMBB & At least 100 MHz; up to 1 Gbps for operation in higher frequency bands \\ \hline \end{tabular}
\end{table}
Table 1: Minimum technical performance requirements of IMT-2020 [10].
## 5 The Effects of RF Exposure on Humans
### Modelling  
The exposure reference levels are derived using simplified anatomical models to conservatively relate the occurred field levels with the basic restrictions. Although it is assumed that the basic restrictions are satisfied if the electromagnetic field exposure is below the reference levels, further measures need to be taken to indicate the compliance with safety limits if the occurred fields violate the reference levels. These measures can be, for instance, direct measurements of the SAR or simulations of the SAR or of the induced currents using anatomical high-resolution models of the human body. Indeed, to better describe the risk of exposure, increasingly sophisticated algorithms and mathematical simulation must take place [112, 113, 114, 115]. Recently, SAR for RF was modelled on a 3D anatomical eye model. Eyes seem to be the most threatened by such exposure and most sensitive to temperature increase that often cause or worsen cataracts. Similarly, head phantoms are used to estimate the 3-D SAR distribution of four realistic models of mobile phones [116]. This kind of information might be exploited for implementing safer mobile phone design [117]. Whole-body phantom was used [118] to experimentally assess the whole-body average SAR in a complex indoor environment. All of these predictive methods are validated through numerical simulations and may provide tools to determine compliance with the safety guidelines for SAR levels.
## 4 Performance Evaluation
### Result Evaluation  
CAPC-MAC, which is the basic strategy of this wireless network, is developed to minimize collisions and manage the priority of access to channels efficiently. This makes it possible to obtain better results, especially in conditions where many nodes want signal space at the same time. The CSMA-MAC (Carrier Sense Multiple Access with Collision Avoidance) protocol is one of the most popular techniques used in wireless communications. It has features that make it listen to the channel before it begins to transmit, working towards there being no other node transmitting at the same time. SFMA-MAC mainly aims at the spatial fairness of the access to the networks based on the location of the nodes. It assists in making a provision that nodes in distinct regions receive fair access to the network regardless of the positioning of the nodes. Lastly, the T-Lohi-MAC protocol is intended to cut battery consumption in wireless networks through a tone-based reservation scheme. It enables nodes to control use of the channel in an efficient way, making minimal use of system resources while at the same time offering optimum network performance.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline No. of clusters & 4 \\ \hline Underwater sound speed & 1500 m/s \\ \hline Communicating data packet & 1000 bits \\ \hline Control data packet & 60 bits \\ \hline Rx node range & 6.5 km \\ \hline Channel range & 6.5 km \\ \hline Bit error rate & 2200 bps \\ \hline Radius of CH & 6.5 km \\ \hline Size of RTS/CTS & 220 bits \\ \hline \(SINR_{dTH}\) value & 22 dB \\ \hline Total simulation time & 23,000 s \\ \hline Power of transmission & 2 watts \\ \hline Power of receiver & 0.75 watts \\ \hline Gross power & 8.5 mW \\ \hline Bandwidth & 10 kb/s \\ \hline Traffic rate & 0.05 to 0.4 packets per s \\ \hline Running rounds & 20 \\ \hline Simulation area & 600 \(\times\) 600 m \\ \hline No. of nodes & 50 to 200 \\ \hline No. of buoys & Uniform: 14 \(\times\) 14 grid (approx.) \\ \hline Learning rate & 0.01 \\ \hline Offset time step & 6 ms \\ \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.