esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Work  
In this paper, we propose a novel FRS framework, FedRecI2C, which aims to accelerate the convergence of FRSs. To achieve this, we integrate communication and computation resources in the system to utilize not only those communication-capable clients to perform federated training, but also those communication-constrained clients to contribute computation and limited communication resources for further local training. Experiments conducted on four public datasets, seven FRSs and various communication scenarios validate the effectiveness of our FedRecI2C framework. Specifically, our works yield several conclusions:Firstly, I2 CG can significantly expedite the convergence of FRSs, with the direction of its impact on the model performance dependent on the specific dataset. When paired with the DPM mechanism, I2 CL not only accelerates the convergence of FRSs but also enhances their performance, with the degree of improvement inversely correlated with the dataset's sparsity. And then, I2C demonstrates robustness under varying communication conditions, including differing I2C periods and ratios of I2C clients. Finally, integrating the early-stop strategy with I2 CG reduces the additional computation overhead and can mitigate potential performance degradation. Conversely, incorporating the early-stop strategy into I2 CL leads to a decline in the model performance, comparable to FRSs without I2 CL.  
FedRecI2C provides simplicity and flexibility, furnishing a plug-and-play architecture that enhances convergence in various FRSs under diverse communication conditions, while also inspiring novel insights into FRSs' training patterns.  
In future work, we aim to investigate the combined use of I2 CG and I2 CL to assess the effectiveness of FedRecI2C under more complex communication conditions. Specifically, the FRS will include clients with reliable communication channels and those in the communication-constrained scenarios Scenario I and Scenario II. Additionally, we plan to replace random client sampling with client selection methods that consider network bandwidth, computational resources or historical activity to improve realism.  
Conceptualization, Q.Z.; methodology, Q.Z.; software, Q.Z.; validation, Q.Z.; formal analysis, Q.Z.; investigation, Q.Z.; resources, Q.Z. and X.H.; data curation, Q.Z.; writing--original draft preparation, Q.Z.; writing--review and editing, Q.Z. and X.H.; visualization, Q.Z.; supervision, X.H.; project administration, X.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Teaching Reform Research Projects of Jinan University (JG2024060).  
All models, or code, that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
# New Approaches of Stochastic Models to Examine the Vibration Features in Roller Bearings
## 2 Experimental Setup  
The bearing datasets utilized in this research were acquired through experiments conducted on a bearing failure test rig, illustrated in Figure 2.  
This setup is relatively straightforward, driven by a two-horsepower (HP) variable-speed induction motor. A specially designed shaft was then supported by two bearings. Notably, the bearing on the right side of the shaft represents a healthy state, while the left-supported bearing serves as the experimental bearing, specifically the SKF 6209. This test bearing underwent both radial and axial loading under defect conditions. The experimental conditions at various loadings with different bearing defects are listed in Table 1. To refine feature extraction and streamline the creation of robust prognostic models, we selected the peak values of defect frequencies. This strategy was employed to mitigate the impact of noise that might obscure pertinent frequencies within the broader spectral energy. By refining the peak values, we aimed for a more precise comprehension of the distinct defect propagation. In this study, our focus was specifically directed towards the analysis on the peak values linked to inner and outer race defects.  
Figure 2: Experimental setup.
# Preamble Design and Collision Resolution in a Massive Access IoT System
###### Abstract  
How to support massive access efficiently is one of the challenges in the future Internet of Things (IoT) systems. To address such challenge, this paper proposes an effective preamble collision resolution scheme to sustain massive random access (RA) for an IoT system. Specifically, a new sub-preamble structure is first proposed to reduce the preamble collision probability. To identify different devices that send the same preamble to the gNB on the same physical random access channel (PRACH), a multiple timing advance (TA) capturing scheme is then proposed. Thereafter, an RA scheme is designed to sustain massive access and the performance of the scheme is studied analytically. Finally, the effectiveness of the proposed RA scheme is validated by extensive simulation experiments in terms of preamble detection probability, preamble collision probability, RA success probability, resource efficiency and TA capturing.  
random access; preamble collision resolution; timing advance capturing; Internet of Things +
Footnote †: journal: Sensors
# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
## 3 Data and Methods
### Accuracy Assessment  
A reference dataset including two classes--aquaculture ponds (AP) and non-aquaculture ponds (Non-AP)--was created for the entire coastal study region. We randomly distributed a total of 7500 sample points per class along the coastal buffer using a stratified sampling scheme [55] to adequately represent the amount of detected pond aquaculture area between different coastal regions. In order to include a statistically sound data basis for the validation, the proportion of the AP class for each of the 261 coastal parcels (see definition of parcels in Section 2) was calculated. The 7500 AP sample points and 7500 Non-AP sample points were distributed according to the parcels' shares of the total aquaculture area. Reference data was derived from several remote sensing experts who assigned a class to each sample point (AP or Non-AP) for the entire coastal zone based on optical satellite imagery. The acquisition of very high-resolution optical data is clearly limited, since continuous coverage of the large areal extent of the continental-scale study region with a total coastline length of approximately 300,000 km (see Table 2) is too cost-intensive. For this reason, we used very high-resolution  
Figure 8: **(A)** Self-contacting polygon edges; (**B1**) Holes in polygons and (**B2**) Patched holes; (**C1,C2**) Compactness parameters: yellow—water polygon; grey—convex hull of the polygon; light blue—oriented minimum bounding box; (**D**) Result of the minimum oriented bounding box calculated for the aquaculture pond mapping result.  
optical data provided via Google Earth imagery. Since these data are globally available, a comprehensive validation database could be provided in order to assess the accuracy of the mapping results even for very remote coastal sites and areas. Finally, we calculated a confusion matrix including performance measures such as the overall, producer's, and user's accuracy for the entire study region as well as for the five macro-regions (see Table 4).
## 3 Results: Network Performance
### Fairness
Moreover, the packet generation rate in (14) defines the maximum traffic load that a node can offer to the network, and fulfils the condition of queue stability:  
\[\sum_{j=2}^{N}\lambda_{j}\leq\frac{1}{2} \tag{20}\]  
It is interesting to note that Equation (20) lets us adjust the traffic per node in the network to maintain a stable behavior in the long-term, and it is not imperative that if node \(j\) is farther than node \(\ell\) from the Gateway, \(\lambda_{j}\leq\lambda_{\ell}\) should happen to avoid bottlenecks. In order to measure the differences between the average packet delays of the different nodes, a commonly used figure of merit of inequality is the Gini index [32]. Originally used in economics to show imbalances of income distribution [33], the Gini index has spread to many disciplines because of its simplicity, e.g., in demography (population studies) [34], medicinal chemistry [35], or even for improving the resources distribution in packet networks [36]. The Gini index \(G\) can be calculated from [37]:  
\[G(N)=\frac{1}{2\,N^{2}\,\overline{x}}\,\sum_{j=2}^{N}\,\sum_{\ell=2}^{N}|x_{j} -x_{\ell}|, \tag{21}\]  
with \(N\) being the number of nodes, \(x_{j}\) the average delay of the packets generated at node \(j\) and delivered to the Gateway, and \(\overline{x}\) the arithmetic mean of \(x_{j}\) (\(j=2..N\)). Index \(G\) takes values in [0,1]. Zero means a homogenous distribution or fairness, which means no difference, i.e., the average delays that the packets suffer from in terms of the different nodes would be the same. On the other hand, a value \(1\) for \(G\) means that there is a node with such a high average delay that the delays of the rest of nodes are negligible (usually called greedy behavior). The simulation results for all possible \(\mathbf{W}^{ini}\) (permutations) are presented in Figure 12 for a network with six nodes. In order to look for the bounds of the Gini index of the delay, Table 5 shows the minimum (fairest case) and maximum values for different network sizes under the same generated traffic conditions as those in Table 4.  
Figure 12: Gini index (\(G\)) for average end-to-end delay for two service policies: (**a**) Round-Robin and (**b**) FIFO. The bounds are shown in both graphs. Case data: \(N\)=6 nodes, simulation time: 1000 slots. When we observe Figure 12 and the results in Table 5, we realize that in, general, the Round Robin service policy is fairer, i.e., has a smaller Gini index. In all cases, however, a task-force analysis has to be carried out to find the best \(\mathbf{W}^{ini}\) vector that achieves the sought objective, e.g., minimize the average delay, the maximum delay, or the Gini index.
# Performance of Sensor Data Process Offloading on 5G-Enabled UAVs
## 2 UAV as UE  
Many parameters distinguish a UAV from a common smartphone UE in a cellular network. High mobility, not only on the ground level but also at high altitudes, the requirement for higher data rates, acceptable delays on the transmitted packets, and the stringent requirement on the reliability of the transmitted packets are some factors that characterize UAVs as UEs. One can dive deep into subjects such as channel models (free space model or not), interference to/from base stations (BSs), BS handover frequencies, and others, which are important parameters that affect the communication performance of 5G-UAVs [15]. However, the purpose of this study is to demonstrate the capabilities of a 5G-UAV as a sensing platform and to highlight the significant elements and challenges that should be considered when designing and operating applications for 5G-UAVs. The remainder of this section will cover the data flows that occur throughout a UAV's mission, followed by a description of the capabilities of a 5G-UAV UE.
## 2 Results and Discussion
### Validation of BA\(\mu\)E(PS-DVB)-LD/HPLC-DAD Methodology  
After the optimization study we proceed to the validation of the proposed methodology. From the data obtained, excellent linearity (1.0-25.0 \(\upmu\)g/L) was obtained with remarkable determination coefficients (\(r^{2}>0.9904\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Phenol Compounds**} & **Recovery a** & \multirow{2}{*}{\(r^{2}\)b  } & **LOD** & **LOQ** \\  & **(\% \(\pm\) RSD; \(n\) = 3)** & & **(\(\upmu\)g/L)** & **(\(\upmu\)g/L)** \\ \hline
4 NOP & 102.2 \(\pm\) 5.2 & 0.9995 & 0.3 & 0.8 \\
3 NOP & 88.0 \(\pm\) 5.7 & 0.9954 & 0.3 & 0.8 \\ BPA & 96.7 \(\pm\) 5.3 & 0.9904 & 0.3 & 0.8 \\ OP & 99.8 \(\pm\) 4.0 & 0.9946 & 0.3 & 0.8 \\ NP & 96.2 \(\pm\) 4.5 & 0.9940 & 0.3 & 0.8 \\ \hline \end{tabular} a
[FOOTNOTE:a]Footnote a: footnotetext: Method efficiency after extraction and back-extraction with PS-DVB in water sample spiked at the 10.0 \(\upmu\)g/L level under optimized conditions (extraction: 16 h (1,250 rpm), pH 5, 5% NaCl; back-extraction: 45 min with ACN under sonification); b  
\end{table}
Table 1: Recovery yields, determination coefficients (\(r^{2}\)), LODs and LOQs achieved for five phenol compounds in ultrapure water samples using BA\(\mu\)E(PS-DVB)-LD/HPLC-DAD analysis, under optimized experimental conditions.  
Figure 2: **(a)** Effect of desorption solvent, **(b)** agitation speed, **(c)** equilibrium time and **(d)** pH on the recovery yields.  
It is also noteworthy that the precision achieved for the present methodology, using within- and between-day repeatability assays calculated as relative standard deviation (RSD) on five replicates, gave rise to variations lower than 15.0%. It must be noted that, according to the requirements of Directive 98/83/EC for organic compounds, the present methodology may be considered acceptable since it presented a RSD below 25%.  
The sensitivity of the actual methodology was also checked through the LODs and LOQs for all five compounds and calculated with a signal-to-noise ratio (S/N) of 3/1 and 10/1, respectively, where values of 0.3 \(\mathrm{\SIUnitSymbolMicro g}\)/L for LODs and 0.8 \(\mathrm{\SIUnitSymbolMicro g}\)/L for LOQs were achieved. The carry-over was also checked by a series of replicates, for which the background was always below the LODs achieved. Table 1 summarizes the experimental recovery yields, the determination coefficients, LODs and LOQs achieved for five phenols in ultra-pure water matrices by the present methodology.
## 3 Modeling Approach
### Load Profiles
#### 3.1.2 Thermal Load Curves  
RAMP is specifically designed to generate not only electrical demand profiles but thermal demand profiles as well--DHW needs in this specific case. In order to improve the characterization of the power needed for heating the water for domestic purposes, a modification to RAMP is proposed: according to [PERSON] et al. [42] the daily groundwater temperature was derived from the daily average ambient temperature through Equation (1).  
\[T_{gw}\Big{(}n_{day}\Big{)}=\overline{T_{amb}}-3\cos\Big{(}\frac{2\pi}{365} \Big{(}n_{day}-n_{day}^{min}\Big{)}\Big{)}\left[K\right] \tag{1}\]  
where:  
* \(T_{gw}[K]\) is the groundwater temperature;
* \(n_{day}\left[-\right]\) is the progressive number of the considered day of the year;
* \(\overline{T_{amb}}\)\(\left[K\right]\) is the yearly average ambient temperature;
* \(n_{day}^{min}\left[-\right]\) is the progressive number of the day with the lowest temperature of the year.  
Once the daily groundwater temperature is computed, it is possible to calculate the power consumed by the three main tasks identified: showers, handwashing, and dishwashing:  
\[P_{task}=\dot{m_{w}}cp_{w}\Big{(}\mathrm{T_{task}}-T_{gw}\Big{)} \tag{2}\]  
where:  
* \(P_{task}[W]\) is the power required to heat the water for the operation of the task;
* \(\dot{m_{w}}\)\(\left[1/s\right]\) is the water mass flow required by the task, retrieved from references [43; 44];
* \(cp_{w}\left[\frac{kJ}{kg}\right]\) is the water isobaric specific heat;
* \(\mathrm{T_{task}}\)\(\left[K\right]\) is the temperature of the water needed for the specific task, retrieved from references [43; 44];
* \(T_{gw}[K]\) is the groundwater temperature.  
The time series with the powers of the DHW-related tasks are then fed to the RAMP model. The modifications to RAMP are available with the repository of the model [45].  
Furthermore, differently from electric loads, thermal loads related to different user classes are not aggregated in a single total load profile. In fact, considering the absence of a centralized heat distribution network, thermal loads need to be spatially bound to the site of consumption. Accordingly, we model a separate thermal load profile for each user class, namely:  
* commercial activities,
* domestic,3. public offices,
4. school.  
No seasonality is considered for thermal loads, which are stochastically generated for 365 days based on the same set of input data. Further details about the data and the assumptions made for the creation of electric and thermal loads are provided in Supplementary Information.
# Application of Microgel as a Sorbent for Bisphenol Analysis in Liquid Food Samples
## 3 Results  
### Characterization of Microgel Sorbent  
Polymer particles were characterized by transmission (TEM) and scanning electron microscopy (SEM) measurements and, in the dried state, they had regular spherical morphology, with an average particle diameter of approximately 100 nm. The influence of various parameters affecting the sorption of bisphenol's on the microgel sorbent (pH, contact time, temperature and adsorption isotherm) was investigated and carefully discussed in the previous paper [28]. The results showed that the temperature and pH of the sample did not affect the sorption in a rather wide range (20-60 \({}^{\circ}\)C) and had a pH lower than 9. An analysis of experimental sorption data indicated that the sorption of bisphenol's was characterized by a pseudo-second-order kinetic model and the data fitted well the Langmuir adsorption model. The maximum adsorption capacity obtained for biphenyl was in the range from 55 mg:g\({}^{-1}\) for BPF to 160 mg:g\({}^{-1}\) for BPB. The microgel was stable for up to five adsorption recycles without an obvious decrease in removal efficiency. What is more is that the possible generation of secondary pollutants, which may leach from the microgel, was also monitored by using LC-FLD [28].
# Research on Wi-Fi Fingerprint Database Construction Method Based on Environmental Feature Awareness
## 3 Environmental Feature Sensing Model
### Data Preprocessing  
The environment feature-aware regression model uses the feature parameters of the indoor environment listed in Table 1 as inputs, with the Wi-Fi fingerprints of all the points in the room as outputs, forming a comprehensive fingerprint library. Assuming that the fingerprint at a grid point is denoted as \(RSSI_{i}\), it can be expressed as follows:  
\[RSSI_{i}=\left[\textit{rssi}_{i}^{\textbf{1}},\textit{rssi}_{i}^{\textbf{2}}, \ldots\textit{rssi}_{i}^{\textbf{m}},\textit{X}_{i},\textit{Y}_{i}\right], \tag{2}\]  
where \(i\) denotes the first \(i\) sampling point; \(n\) denotes the first \(n\) AP; \(\textit{rssi}_{i}^{\textbf{m}}\) denotes the first AP at the \(i\) RSSI value of the first sampling point at the \(n\) RSSI value of the AP; and \((\textit{X}_{i},\textit{Y}_{i})\) are the coordinates of the sampling point. Since the Wi-Fi fingerprint consists of the signal strengths of multiple APs, if directly put into training, it may be difficult for the model to  
Figure 1: General view of the environmental feature perception model.  
distinguish the effects of different environmental features on the RSSI values, resulting in the model learning inaccurate patterns, thus reducing the prediction accuracy.  
To enhance the model's ability to learn the relationship between feature parameters and each RSSI value, we segment the fingerprints after data loading, ensuring that each RSSI value occupies a separate column. This allows the model to better capture the RSSI values from each access point (AP) across different environments during the training process. By doing so, the model can more accurately learn the relationship between RSSI values and environmental features, thereby improving both learning efficiency and accuracy. After segmentation, the data is merged with the original features, as illustrated in Figure 2.  
To optimize the processing and transformation of raw data for feature extraction, we standardize the features by converting their values to a distribution with a mean of 0 and a standard deviation of 1. This normalization step eliminates the differences in magnitude between features, allowing the model to better learn and recognize patterns in the data, ultimately improving its performance and accuracy.  
Standardizing the features also accelerates the convergence of model training and prevents certain features from disproportionately influencing the training process due to excessively large or small value ranges. After standardization, the features and labels are transformed into a format suitable for subsequent deep learning model training.
## 3 Intrinsic Thermal Conductivity of Graphene
The reported value is larger than thermal conductivity of bulk graphite at RT, which is about 2000 W/mK. Other optothermal studies with suspended graphene found thermal conductivity in the range from 1500 to 5000 W/mK [33]. The experimental data for suspended or partially suspended graphene is closer to the intrinsic thermal conductivity because suspension reduces thermal coupling to the substrate and scattering on the substrate defects and impurities. The measurements for exfoliated graphene supported on SiO\({}_{2}\)/Si substrate revealed in-plane \(K\approx 600\) W/mK near RT [37]. This value is below those reported for suspended graphene because of the phonon scattering at the interface and  
Figure 1: **(a)** Schematic of the suspended graphene structure used for measurements of the thermal conductivity of graphene. Graphene was heated with the laser light focused in the middle of the suspended part. The temperature rise was determined from the shift of the G peak position in graphene Raman spectrum; **(b)** Scanning electron image of the bilayer graphene ribbon suspended across the 3-\(\upmu\)m trench in Si/SiO\({}_{2}\) wafer for optothermal measurements. The image is reproduced with permission from [1]. Copyright 2010 Nature Publishing Group. diffusion to the substrate. The thermal conductivity of supported graphene, limited by the extrinsic effects, it is still very high and it exceeds that of silicon and copper. In Ref. [37], the authors calculated the theoretical thermal conductivity of free graphene to be \(\sim\)3000 W/mK near RT. Detailed review of thermal conductivity measurements and comparison of the results among different groups was given in Ref. [1].