esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Multi-Time Scale Dispatching Model of Active Power and Reactive Power Based on MPC
### Intra-Day Rolling Optimal Dispatching Model
\begin{split}\text{min}F=\rho_{T}\Delta t_{T}+\rho_{c}\Delta t_{ CBs}+C_{GT}+C_{VPSSH}+C_{BES}+\Gamma P_{loss}+\lambda_{v}\Delta V+\gamma\\ \gamma=\lambda_{VPSSH}(t)(V_{iV_{i}}^{\text{VPSSH}}-V_{i,pre}^{ \text{VPSSH}})+\lambda_{BES}(t)(E_{SOC,t,i}-E_{SOC,t,i,pre})\end{split}  \tag{25}\]  
Figure 2: Intra-day rolling optimal dispatch framework. where \(\lambda_{VPSSH}(t),\;\lambda_{BES}(t)\) are the capacity unbalance penalty coefficient of VPSSH and BES, which is related to time t; \(V_{t,i,pre}^{\text{VPSSH}},\;E_{SOC,t,i,pre}\) are the capacity reference value of VPSSH and BES, obtained from the day-ahead optimization results. 3. Constraint condition  
The constraints of the intra-day rolling optimization stage are the same as those of the day-ahead optimization stage, such as Equations (1)-(8) and (10)-(22). 4. Feedback correction  
Superior to the traditional optimization algorithm and intelligent optimization algorithm, the model predictive control has feedback correction which can update the real-time measurement state of the system at each rolling time, correct the forecast error, and exhibit high anti-interference performance and robustness [21]. The input value of the predictive model in this paper is the ultra-short-term forecast data of the wind, PV, and load. However, due to certain errors between the ultra-short-term forecast value and the actual value or external interference, the reference value of the active power and reactive power output of controllable resources issued in advance is not completely equal to the actual output, and the actual state of the system is not consistent with the predictive state. In order to eliminate the influence of deviation as possible, this paper uses the actual active power and reactive power output of controllable resources, sampled after previous optimization, as the initial values for the new rolling optimization. Thus, it forms a closed-loop control system and enhances control reliability. Specifically  
\[ \begin{array}{l}\mathbf{P}_{0}(k+1)=\mathbf{P}_{real}(k+1)\\ \mathbf{Q}_{0}(k+1)=\mathbf{Q}_{real}(k+1)\end{array}  \tag{26}\]  
\(\mathbf{P}_{0}(k+1),\;\mathbf{Q}_{0}(k+1)\) are the initial value of active power and reactive power output at \(k+1\) time; \(\mathbf{P}_{real}(k+1),\;\mathbf{Q}_{real}(k+1)\) are the actual active power and reactive power output values of controllable resources sampled at \(k+1\) time.
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 3 Detailed Description of the Proposed Localization Scheme
### Determining the Projection of a Node on the Trajectory
#### 3.2.2 Extending Vectors  
Since the trained regression function by SVR is a black box, it can transform the input into the output, but the inner working is difficult to interpret because the underlying models are based on complex mathematical systems [42]. The derivative method cannot be used to find the point with the maximum value on the predicted RSSI curve. We extend the location vector \(\mathbf{X}\) and predicted RSSI vector \(\widehat{\mathbf{R}}\) to determine a point closer to the actual projection.  
Figure 4: Determining the projection of a node on the trajectory by the SVR-based interpolation method.  
As shown in Figure 4, the predicted RSSI curve has first and second maximum RSSI values at points \(x_{i}\) and \(x_{j}\), respectively. Before extending vectors, \(x_{i}\) is determined as the projection, but has a large error with the actual projection. Although shortening the distance between broadcast points can reduce the error, it is difficult to reduce this distance to a small value in an actual sea environment. To determine a point closer to the actual projection position, we evenly insert \(k\) points between \(x_{i}\) and \(x_{j}\), as shown in Figure 5. The extended location vector is  
\[\hat{\mathbf{X}}=[\hat{x}_{1},\dots,\hat{x}_{i},\hat{x}_{i1},\hat{x}_{i2}, \dots,\hat{x}_{ik},\hat{x}_{j},\dots,\hat{x}_{m}] \tag{23}\]  
After using the regression function to predict all the elements in \(\hat{\mathbf{X}}\), the extended RSSI vector can be obtained  
\[\hat{\mathbf{R}}=[\hat{r}_{1},\dots,\hat{r}_{i},\hat{r}_{i1},\hat{r}_{i2}, \dots,\hat{r}_{ik},\hat{r}_{j},\dots,\hat{r}_{m}] \tag{24}\]
## 3 Results
### Meta-Analysis  
Quantitative data were systematically extracted from the results sections of the reviewed studies using a structured extraction tool (see Supplementary Material: SM S7) to minimize bias and ensure consistency. The extracted data were entered into Microsoft Excel and then imported into R version 4.4.1 (2024-06-14 curt) for data cleaning and analysis. Extracted data included age-related statistics (mean, median, interquartile range, and standard deviation), sample sizes (including gender-based breakdowns), metric measures used to evaluate AI/ML approaches, and the countries and subregions of SSA where studies were conducted. Data cleaning involved checking for inconsistencies and missing values and ensuring accuracy and completeness. Descriptive analysis, including summary statistics and frequency analysis, was performed to prepare the data for meta-analysis. Studies with incomplete data, missing metrics, or insufficient study characteristics were excluded. Variables selected for meta-analysis were informed by their availability, relevance, and potential to address study objectives, resulting in the selection of five variables: study, sample size, use, AI/ML approach, and F1-score (used as the effect size). Only five studies reported age-related statistics, revealing a significant demographic data gap crucial for understanding population-level risks. Furthermore, some studies presented unique metrics or descriptive outcomes that, while valuable, were omitted from the meta-analysis because they were either qualitative or non-standardized. For the evaluation of AI/ML approaches, the F1-score was selected, as it was reported in nine studies, sometimes disaggregated by gender or across different methods, thereby providing more records. Also, the F1-score was selected because of its characteristic of representing the harmonic mean of precision and recall, combining these two metrics into a single measure to provide a comprehensive assessment of model performance [57]. Several metrics were used in the 20 reviewed studies. Though F1-scores and accuracy appeared in the same number of studies, F1-scores had more recordings than accuracy. Table 4 highlights various metrics and their corresponding frequencies, used to evaluate the performance of AI/ML approaches in the reviewed studies.  
Based on availability, consistency, accuracy, completeness, and relevancy, Supplementary Material: SM S9 was generated to show the data that were captured for meta-analysis. The F1-scores from the nine studies considered for meta-analysis and the different AI/ML algorithms and models employed in each study resulted in 69 observations being generated, as recorded in Supplementary Material: SM S9.
## 3 Results  
### Seasonal Patterns of SIF and GPP across PFTs  
From Figure 1, we can see that the spatial variation in SIF was consistent with that of GPP, indicating that SIF has the ability to track GPP at the global scale. In addition, the seasonal patterns of SIF and GPP for different PFTs are important for our understanding of the responses of SIF and GPP to temperature.  
It can be seen that SIF had a similar seasonal pattern to GPP for each PFT in the Northern Hemisphere (Figure 4). Although SIF tracked the GPP well for different PFTs, the seasonal patterns of SIF and GPP were largely influenced by environmental conditions. For tropical regions, although TrEBF, TrDBF, and TrC3 GRA grew under relatively comfortable temperature conditions (Figure 5), SIF and GPP of TrEBF did not exhibit an obvious seasonal change, compared to that of TrDBF and TrC3 GRA. The seasonal variations in SIF and GPP for TrDBF and TrC3 GRA may have been largely influenced by the VPD, which varied with the change from dry to wet seasons (Figure 5). Compared to TeENF and TeDBF, SIF and GPP for TeEBF did not show a more intense seasonal pattern, as air temperature and PAR for TeEBF did not significantly change during the growing period (Figure 5). For Boreal regions, BoENF, BoDBF, BoDNF, and BoC3 GRA generally experienced low-temperature stress (less than 10 \({}^{\circ}\)C; Table 1). We can see that the magnitude of SIF and GPP for BoENF, BoDBF, BoDNF, and BoC3 GRA decreased when the temperature decreased during the over-wintering period. As different PFTs have different growth temperature conditions, the seasonal variations in SIF and GPP for different PFTs responded differently to temperature.  
Figure 4: Seasonal patterns of the SIF and GPP for each plant functional type in the Northern Hemisphere (2018–2020). The lines correspond to the means of the corresponding data, with a different color for SIF (red) and GPP (dark). The shaded area indicates the standard deviation.  
In addition, due to their varying geographical distribution, some PFTs were not only distributed at places where relatively warm conditions prevailed throughout the year, but also in places where low-temperature stress could occur. For example, C3 CRO distributed at low latitudes generally grew under relatively warm conditions, while those distributed at high latitudes experienced low-temperature stress (Figure 3). These data provide us with a chance to study the variations in SIF and GPP in response to temperature.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AC & Analysis Center \\ ASC & Advanced Star Camera \\ CB & Conservative Bound \\ CDAAC & COSMIC Data Analysis and Archive Center \\ CHAMP & CHAllenging Miniastellite Payload \\ CODE & Center for Orbit Determination in Europe \\ DSM & Daily System Modeling \\ DLR & German Aerospace Center \\ DTU & Technical University of Denmark \\ ECOM & Empirical CODE Orbit Model \\ ECV & Essential Climate Variable \\ EOP & Earth Orientation Parameter \\ ESA & European Space Agency \\ ESOC & European Space Operations Centre \\ EUMETSAT & European Organization for the Exploitation of Meteorological Satellites \\ FCDR & Fundamental Climate Data Record \\ GNSS & Global Navigation Satellite System \\ GFZ & German Research Centre for Geosciences \\ GPS & Global Positioning System \\ GRACE & Gravity And Climate Experiment \\ GRAS & GNSS Receiver for Atmospheric Sounding \\ IERS & International Earth Rotation and Reference Systems Service \\ ILRS & International Laser Ranging Service \\ IGS & International GNSS Service \\ IR & Infrared \\ ITRF & International Terrestrial Reference Frame \\ JCGM & Joint Committee for Guides in Metrology \\ JPL & Jet Propulsion Laboratory \\ LEO & Low Earth Orbit \\ LRR & Laser Retroreflector \\ Metop & Meteorological Operational \\ NASA & National Aeronautics and Space Administration \\ ODP & Occultation Data Processing \\ PCO & Phase Center Offset \\ PCV & Phase Center Variation \\ POD & Precise Orbit Determination \\ RCB & Receiver Clock Bias file format \\ RINEX & Receiver Independent Exchange Format \\ RO & Radio Occultation \\ rOPS & Reference Occultation Processing System \\ RTN & Radial-Transverse-Normal \\ SLR & Satellite Laser Ranging \\ SP3 & Standard Product 3 orbit file format \\ UCAR & University Corporation for Atmospheric Research \\ \end{tabular}
# A Brief Overview of Energy Efficiency Resources in Emerging Wireless Communication Systems
## 3 Energy Efficiency Techniques in Wireless Communication Systems
### Energy Efficient Hardware Component Designs  
Power amplifiers are used to increase the signal level of wireless communication for effective performance over the communication channel [4]. In [33], it was opined that using power amplifiers in base stations required certain conditions to be met. Requirements include the efficiency, linearity and expected output. The authors considered the Doherty power amplifier, envelope tracking (ET) power amplifier and linear amplification using non-linear components (LINC) power amplifiers. Advancements are still ongoing to improve the efficiency of power amplifiers in using sequential power amplifiers, good RF switching techniques, and smart antennas, among others [34; 35].
## 3 Proposed Scheme: FRBP
### Local Real-Time Failure Avoidance and Recovery
As discussed above, since network-wide broadcast by DBP reporting causes severe energy consumption, the reporting process should be kept to a minimum. In this paper, we assume that the report condition of node \(i\) is \(DBP_{i}<[-m/2]\). Algorithm 2 shows the entire procedure for failure avoidance at each node. At this point, each intermediate node also determines whether it uploads received DBP-Based Fault Reporting (DBFR) messages through upstream by policies of a parent. In this paper, we assume that intermediate nodes upload DBFR even if one DBFR is received. In order to prevent reporting excessive DBFR messages, each intermediate node uploads aggregated DBFR toward a source. Through the described procedure, the source could be aware of how many DBP faults occur. The source also expects flexible recovery through the avoidance because of the high cost of tree reconstruction. In the meantime, when the number of DBFR message exceeds a predefined threshold, broadcasting tree reconstruction might be conducted according to administration policies. Namely, the source regards such a situation as unsatisfactory for the goal of real-time broadcasting and performs the tree reconstruction. Additionally, if the reconstruction is frequently performed, it is considered that the entire network could not provide real-time broadcasting capacity; though in such bad circumstances, data delivery is needed, it temporarily runs the flooding method. ```
1:\(PCSRB_{i}\): parent candidate set for real-time broadcasting
2:\(DBP_{i}\): DBP value of node \(i\)
3:\(DDS\): desired delivery speed
4:\(DS\): delivery speed
5:if\(parentNode_{i}==\)then\(\triangleright\) if a current parent node of \(i\) is in the case of node or link failure
6:\(T\leftarrow\{t\in PCSRB_{i}|min(HopCount(t))\}\)
7:\(parentNode_{i}\leftarrow\underset{j\in T}{\operatorname*{arg\,max}}\{Dist(src,j)\}\)
8:elseif\(DeliverySpeed(i,parentNode_{i})<DDS\)then\(\triangleright\) if a current parent node of \(i\) is in the case of bad DS
9:if\(numChildNode_{i}>0\)then\(\triangleright\) if node \(i\) is an intermediate node
10:\(T\leftarrow\{t\in PCSRB_{i}|DBP_{i}\geq 0\}\)
11:if\(T==\)then
12:\(T\leftarrow\{t\in PCSRB_{i}\}\)
13:if\(T==\)then
14:\(T\leftarrow\{t\in PCS_{i}|DBP_{i}\geq 0\}\)
15:endif
16:endif
17:\(parentNode_{i}\leftarrow\underset{j\in T,j\
eq parentNode_{i}}{\operatorname*{ arg\,max}}\{DeliverySpeed(i,j)\}\)\(\triangleright\) the second best node
18:else\(T\leftarrow\{t\in PCSRB_{i}|DBP_{i}\geq 0\}\)
19:if\(T==\)then
20:\(T\leftarrow\{t\in PCSRB_{i}\}\)
21:endif
22:endif
23:\(parentNode_{i}\leftarrow\underset{j\in T,j\
eq parentNode_{i}}{\operatorname*{ arg\,max}}\{DeliverySpeed(i,j)\}\)\(\triangleright\) the second best node
24:endif
25:elseif\(DBP_{i}<\lfloor\frac{k-m}{2}\rfloor\)then\(\triangleright\) if the DBP value of node \(i\) is poor
26:\(T\leftarrow\{t\in PCSRB_{i}|DBP_{i}\geq 0\}\)
27:\(parentNode_{i}\leftarrow\underset{j\in T,j\
eq parentNode_{i}}{\operatorname*{ arg\,max}}\{DeliverySpeed(i,j)\}\)\(\triangleright\) the second best node
28:endif
```  
**Algorithm 2** Real-time failure avoidance
# A TinyML Approach to Real-Time Snoring Detection in Resource-Constrained Wearables Devices +
## 2 Related Work
The model achieved a high detection accuracy of 96.7% regarding OSA-related snoring. Based on the analysis of the research review, it is concluded that there is a need for the development of an efficient, real-time wearable device and system capable of accurately detecting snoring. The system should address the limitations of resource-constrained devices and eliminate dependency on cloud servers for inferencing. Through the creation of a wearable device that can accurately detect snoring in real-time without relying on external servers, people with OSA can benefit from continuous monitoring and timely intervention.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 5 Security Analysis of the Proposed Protocols
### Security Analysis of the PQC-AES Hybrid PKE Protocol
#### 5.2.1 Security of the Key Exchange (PQC KEM)  
The key exchange phase is identical to that described for the PQC standalone PKE protocol (Section 5.1.1). The same security assumptions and formal statements apply.
## 1 Introduction  
In the modern world, natural disasters occur frequently, posing significant challenges for post-disaster rescue efforts that have garnered substantial societal attention [1, 2]. Recently, numerous scholars have investigated the post-disaster rescue problem across various contexts and scenarios [3, 4, 5]. Disasters often cause severe road damage, impeding ground transportation, and delaying rescue operations in affected areas. In contrast, helicopters can rapidly transport rescuers and materials to disaster zones [6, 7, 8], making helicopter scheduling a critical yet complex problem in post-disaster rescues.  
Rescue tasks in post-disaster situations typically involve transporting rescue materials and evacuating survivors. A major challenge is that the survival chances of victims decrease over time. Thus, efficient helicopter scheduling for rescue missions has become an urgent issue [9]. The helicopter scheduling problem is a variant of the vehicle routing problem with time windows (VRPTW). However, existing research on rescue routing rarely incorporates the dual constraints of time windows and survivors' diminishing life strength. Therefore, we extended the VRPTW problem to consider the limited survival time of victims and established a new problem model. Currently, the iterative greedy (IG) algorithm has gained popularity due to its simplicity, strong exploitation capability, and fast convergence,attracting significant attention from researchers in various fields [10; 11; 12; 13]. In addition, it has shown excellent performance in solving many other complex problems, which has laid a solid foundation for addressing helicopter rescue routing challenges. Thus, this paper proposes an IIG algorithm to solve the collaborative helicopter rescue routing problem. The main contributions of the paper are as follows:  
(1) A heuristic initialization strategy was designed to generate an efficient and feasible initial solution;  
(2) A feasible-first destruction-construction strategy was applied to enhance the exploration ability of IIG;  
(3) Three problem-specific local search operators were developed to improve the algorithm's local search performance;  
(4) An improved acceptance criterion for the evolutionary solution was investigated to prevent the algorithm from getting trapped in local optima.  
The remainder of this paper is structured as follows: Section 2 presents a literature review of VRPTW; Section 3 provides a concise overview of the problem description and formulation; Section 4 outlines the various components of the proposed algorithm; Section 5 discusses the simulation environment and the parameter settings, along with a comparative analysis with several effective algorithms. Finally, we summarize the conclusions of this study and present the future work in Section 6.
# Metal-Organic Frameworks Toward Electrocatalytic Applications
## 2 Charge Transport in Metal-Organic Frameworks (MOFs)
### Band Transport
Recently, the incorporation of guest molecules to engender the intra-framework donor-acceptor charge transfer has been demonstrated in cooper-based, cobalt-based, and manganese-based MOFs to facilitate the electron conduction within those frameworks [61; 62; 63]. The donor-acceptor strategy was also utilized to render the water-stable Zr-MOFs electrically conductive very recently. [PERSON] et al. demonstrated the incorporation of nickel(IV) bis(dicarbollide) as the electron acceptor into NU-1000, a mesoporous Zr-MOF containing electron-donating pyrene-based linkers (Figure 3c); much improved electrical conductivity and electrochemical performance were achieved with the help of the electron-accepting guest molecules [64]. [PERSON] et al. also utilized a similar strategy to render another Zr-MOF (NU-901) electrically conductive by incorporating fullerene as the electron-accepting guest [38]. Besides the aforementioned approaches, other strategies may be used to design the electrically conductive MOF materials. For example, [PERSON] et al. demonstrated the polymerization of conducting polymer within the channels of the MOF to prepare the electrically conductive MOF-polymer composite materials (Figure 3d) [65], and [PERSON] et al. utilized self-limiting techniques to construct conducting and continuous molecular bridges within the MOF channels to rendering the framework electrically conductive [66; 67]. The design of electrically conductive MOFs is not only beneficial for the use in electrocatalysis and relevant applications, but also helpful for developing MOF-based resistive gas sensors [68]. In addition to the strategies that render charge transport in MOFs mentioned above, another common approach to prepare electrically conductive MOF-based materials is transforming MOFs into MOF-derived porous carbons and metal oxides at high temperature [69]. Due to the remarkable electrical conductivity of carbon materials, these MOF-derived materials usually perform well in electrochemical applications. However, such MOF-derived materials lose the regular pore structures and chemical functionality of the parent MOFs. Since MOF-derived materials are no longer crystalline  
Figure 3: Examples of electrically conductive metal–organic frameworks (MOFs) designed by utilizing (**a**) 2D \(\pi\)-conjugation [49]. Reproduced with permission from [49], Copyright American Chemical Society, 2014. (**b,c**) incorporation of guest molecules [60; 64]. Reproduced with permission from [60; 64], Copyright The American Association for the Advancement of Science, 2014 and Copyright American Chemical Society, 2018. And (**d**) MOF-polymer composite [65]. Reproduced with permission from [65], Copyright American Chemical Society, 2016. MOFs, the use of MOF-derived materials for electrocatalytic applications is beyond the scope of this review.
# On Improving 5G Internet of Radio Light Security Based on LED Fingerprint Identification Method
## 2 LED Fingerprint Verification Mechanism
### Extraction and Identification Mechanisms  
In this section, introduced LED fingerprint extraction and identification will be explained. The extraction and identification mechanisms are illustrated in Figure 2.  
The example of the LED fingerprint extraction of the \(i\)-th device will be described below in order to illustrate the proposed method's functioning. The signal transmitted from the \(i\)-th device will be sampled by performing a Fast Fourier Transform (FFT) to calculate the power spectrum at the receiver. By fitting this measured power spectrum with its theoretical function, the feature vector becomes the LED's fingerprint.  
Before the identification, the registration of the authorized devices should be processed first. The feature vector of each authorized device will be extracted and put into a machine-learning-based classifier to train an identification model. Once the model has been established, it will be stored into a database which is integrated on the judgment module.  
When a device \(k\) requests accessing, its feature vector \(\lambda k\) will be extracted by the receiver. Meanwhile, the classification module will invoke the model stored in the database and perform identification. As the device has been recognized, the judgment module will decide if it permits it to access, based on the demands of the user.