esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Level Split Federated Learning for Large-Scale AIoT System Based on Smart Cities
## 5 Conclusions  
In this work, we proposed a novel multi-level split federated learning (SFL) framework for the enhancement of collaborative learning in large-scale AIoT systems. The multi-level SFL framework addresses the connectivity and data processing challenges that occur in a large-scale AIoT system with a multitude of clients. Through multiple levels of aggregation of model parameters, it significantly reduces the communication delay between the cloud server and the clients, enhancing the processing speed and entire performance of the central server. By integrating split learning into the framework, it balances the system heterogeneity among clients and boosts the system's scalability to incorporate more IoT devices for collaborative learning. It also mitigates the single point of failure risk of the central cloud server and ensures continuous model training even in the event of longer transmission distances. The use of the Message Queuing Telemetry Transport (MQTT) protocol and Docker containers in the experimental setup substantiates the practical feasibility of the proposed multi-level SFL framework. The resulting improvements in model accuracy under large-scale clients and faster convergence in non-IID scenarios, as evidenced by the simulation experiments, further validate the effectiveness of the proposed solution. Although our proposed multi-level SFL architecture has shown some advantages in model accuracy, it still has some shortcomings in terms of transmission overhead. For example, we have not further explored the effect of different sized datasets on the size of the information transferred and how to balance the overall communication overhead through MQTT generated by federated learning and split learning. Future work will further study these aspects.  
Conceptualization, H.X., K.P.S. and L.M.A.; methodology, H.X. and K.P.S.; resources, K.P.S.; data curation, H.X., K.P.S. and L.M.A.; writing--original draft preparation, H.X., K.P.S. and J.S.; writing--review and editing, K.P.S., J.S. and L.M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The Fashion MNIST dataset can be found in Kaggle: [[https://www.kaggle.com/datasets/zalando-research/fashionmnist](https://www.kaggle.com/datasets/zalando-research/fashionmnist)]([https://www.kaggle.com/datasets/zalando-research/fashionmnist](https://www.kaggle.com/datasets/zalando-research/fashionmnist)) (accessed on 15 January 2024). The HAM10000 dataset comes from the paper: \"The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions\", DOI: [[https://doi.org/10.1038/sdata.2018.161](https://doi.org/10.1038/sdata.2018.161)]([https://doi.org/10.1038/sdata.2018.161](https://doi.org/10.1038/sdata.2018.161)).  
The authors declare no conflicts of interest.
## 3 Results
### Relationship between SIF and MODIS at OCO-2 Footprint Level  
For forest, cropland, and grassland areas, scatter plots were drawn from SIF points for each 16-day period and the corresponding spatial values of EVI, FPAR, and LAI at a footprint level. Statistically significant correlations were obtained for SIF-EVI in cropland, forest, and grassland areas. The largest R\({}^{2}\) value was observed in cropland, followed by grassland, and finally forest areas (Table 2). In the case of SIF-FPAR, although significant correlations were obtained for three land cover types, the R\({}^{2}\) value corresponding to the forest areas was lower than 0.1. With regard to SIF-LAI, no R\({}^{2}\) value greater than 0.1 was found for any of the three land cover types; this indicates that if SIF was taken as a dependent variable, EVI could explain 30% of the observed SIF variation in cropland, forest, and grassland areas. The explanatory ability of LAI, however, is limited for all the three ecosystems. For forest areas, the lower capacity of EVI, FPAR, and LAI to describe changes in SIF is likely due to the comparatively complex canopy structure typical of forests [19; 42].
## 5 Key Technologies Supporting Deliberate Attack Defense for IECPS
### System Planning of IECPS
#### 5.2.1 Redundancy and Emergency Equipment Planning  
The planning of redundancy and emergency equipment aims to reduce the likelihood of attacks and minimize damage during an attack by strategically incorporating essential equipment. For cyber-related measures, this includes installing key data measurement devices to provide backups and enable verification. Additionally, fiber optic communication helps prevent transmission interruptions caused by power line damage. To restore networks disrupted by deliberate attacks, emergency communication vehicles are deployed. Strengthening system firewalls and intrusion detection systems further enhances the ability to resist and detect network attacks. Finally, data storage backup centers ensure the recovery of data and support traceability after an attack.  
In terms of physical equipment, measures encompass deploying fault inspection drones and other mobile monitoring instruments to quickly locate faults; equipping fast isolation devices to provide equipment support for fault isolation; providing sufficient emergency repair vehicles to ensure the rapid arrival of repair personnel; setting up emergency power generation vehicles, fixed/mobile energy storage to provide short-term emergency power for system response and recovery; equipping uninterruptible power supplies and independent microgrids to ensure emergency power supply for key devices and loads; replacing critical lines with underground cables to enhance the ability to withstand extreme events.  
When planning redundancy and emergency equipment, it is crucial to focus on the system's coordination and risk resistance capabilities. Equipment configuration must take into account the interactions between different systems to enhance the system's flexibility in emergency response. For various potential deliberate attacks, equipment must be capable of resisting single-point and multi-point fault propagation. Additionally, emergency equip  
Figure 16: Application of multi-time scale simulation technology in deliberate attack defense.  
ment planning must support rapid deployment and scheduling to minimize the impact duration of deliberate attacks. Furthermore, within a limited budget, priority should be given to deploying the aforementioned equipment in critical locations, thereby optimizing emergency resource allocation to maximize the defense effectiveness against deliberate attacks.
# Multi-Sensor Fusion for Aerial Robots in Industrial GNSS-Denied Environments
###### Abstract  
The use of unmanned aerial robots has increased exponentially in recent years, and the relevance of industrial applications in environments with degraded satellite signals is rising. This article presents a solution for the 3D localization of aerial robots in such environments. In order to truly use these versatile platforms for added-value cases in these scenarios, a high level of reliability is required. Hence, the proposed solution is based on a probabilistic approach that makes use of a 3D laser scanner, radio sensors, a previously built map of the environment and input odometry, to obtain pose estimations that are computed onboard the aerial platform. Experimental results show the feasibility of the approach in terms of accuracy, robustness and computational efficiency.  
Monte Carlo localization; particle filter; UAV; 3D LIDAR; ultra-wide band +
Footnote †: journal: Applied Sciences
## 6 BER Performance Based on Measuring Data  
To evaluate the performance of coherent optical link with aperture averaging further, a group of channel gain data collected by the ground station of the LEO quantum communication satellite located Xinjiang of China is employed. The channel gain data of apertures with various diameters can be obtained by changing the statistical parameter of the data collected by the telescope with 1.2 m diameter.  
Figure 7 shows the channel gain curve collected by 1.2 m telescope within 2 s and the histogram with 2042 samples (1041 samples/s). The zenith angle is 61\({}^{\circ}\) and the rms wind speed is 76 m/s. The altitude of the ground station is 2080 m and the \(C_{n}^{2}\) at this altitude is estimated as \(9.86\times 10^{-17}\). The satellite altitude is assumed as 500 km. The theoretical scintillation index at the experience condition is 0.0136 and the realistic scintillation index is 0.0150.  
Changing the scintillation index of the data shown as Figure 7, according to Equation (1) and [23], we obtain the channel gain curves of different aperture diameters shown as Figure 8.  
Figure 6: Performance of average capacity with different diameters, turbulence conditions and zenith angles are: (**a**) \(A=1.7\times 10^{-14},w=10,\zeta=\pi/3\); (**b**) \(A=1.7\times 10^{-14},w=10,\zeta=\pi/6\); (**c**) \(A=3.0\times 10^{-13},w=21,\zeta=\pi/3\); (**d**) \(A=3.0\times 10^{-13},w=21,\zeta=\pi/6\).  
Figure 7: The curve and histogram of the channel gain collected by 1.2 m telescope.  
Using the channel gain data, the BER performance with various aperture diameters is shown as Figure 9. The coherent BPSK scheme is employed. This result has agreement to the theorical results of Figure 2 and the BER of the same aperture diameter is higher than Figure 2 as a result of stronger turbulence.  
Figure 8: The curves and histograms of the channel gain with various aperture diameters. The diameters are: (**a**) 0.8 m; (**b**) 0.6 m; (**c**) 0.4 m; (**d**) 0.2 m.  
Figure 9: The BER performance of various aperture diameters based on experience data.
# Theoretical Methane Emission Estimation from Volatile Fatty Acids in Bovine Rumen Fluid
## 3 Results and Discussion  
### VFA Concentrations in Rumen Fluid Using the Conventional Method  
All prepared samples were analyzed by GC-FID for the quantification of VFAs in the rumen fluid. The determination of acetic acid, propionic acid, and butyric acid is important because they account for about 95% of the total VFAs in rumen fluid [24]. In this study, isobutyric acid, valeric acid, and isovaleric acid were included for the analysis as well (Figure 1), as these less-common VFAs may be subject to greater measurement errors. The average measured concentrations of acetic acid, propionic acid, isobutyric acid, butyric acid, valeric acid, and isovaleric acid using the conventional method were 2833, 865, 188, 156, 103, and 93 ng \(\upmu\)L\({}^{-1}\), respectively. Among the six types of VFAs, acetic acid and propionic acid accounted for about 87% of the VFAs. The tendency of these results was confirmed by other previous studies. [PERSON] and [PERSON] [25] reported that the acetic acid, propionic acid, butyric acid, and valeric acid concentrations were determined to be 4002, 2013, 1271, and 366 ng \(\upmu\)L\({}^{-1}\) for the rumen liquid using gas chromatography. The specific concentration value was different, but the fraction of acetic acid and propionic acid was 80%, which accounted for the majority of the VFAs. [PERSON] and [PERSON] [26] collected and analyzed ruminal fluid from dairy cows, with their results showing the percentage of acetic acid, propionic, isobutyric acid, butyric acid, isovaleric acid, and valeric acid concentrations to be 63, 21, 0.6, 13, 1.2, and 1.1, respectively, also indicating that the main components of rumen fluid were acetic acid and propionic acid. In addition, [PERSON] et al. [27] also reported the molar proportions of acetic acid, propionic acid, butyric acid, and valeric acid from the rumen of dairy cows to be 67, 19, 12 and 18, respectively. Among these studies, there does not appear to be large differences in the composition ratios of rumen VFAs.
## 3 Results and Discussion
### Adsorption and Photocatalytic Removal of MB
#### 3.2.2 Effect of Illumination Time on the Decay of MB  
The activity of the prepared SA/PVP/TiO\({}_{2}\) nanocomposites was investigated in a dark environment to assess the level of MB dye adsorption in the beads. These data were used to evaluate the photocatalytic activity of SA/PVP/TiO\({}_{2}\) nanocomposites for eliminating MB dye in the presence of visible light. The experiments were carried out using 1 g L\({}^{-1}\) of  
Figure 3: (**a**) FTIR spectra and (**b**) XRD spectra of the TiO\({}_{2}\) nanotubes and SA/PVP/TiO\({}_{2}\) nanocomposite beads.  
one of the two studied concentrations of doping agent (1 and 3 wt.% of TiO\({}_{2}\), respectively for SA/PVP/TiO\({}_{2}\)-1 and SA/PVP/TiO\({}_{2}\)-3 nanocomposite beads) in a 500 mL solution containing 50 mg L\({}^{-1}\) of MB dye at pH 7. The analysis was done at different time intervals in the dark and under visible light. As illustrated in Table 1, the dark adsorption increased with time and stabilized after 40 min, indicating that the active site and porosity of the SA/PVP blended polymer were saturated with MB molecules. Furthermore, carboxylic groups are the prevalent functional groups in the SA polymer, aiding in the adsorption of the cationic dye molecules.  
However, under visible light, as can be seen in Figure 5, the results reveal that the photocatalytic activity increased with the duration of the illumination period, with the SA/PVP/TiO\({}_{2}\)-1 and SA/PVP/TiO\({}_{2}\)-3 nanocomposite beads reaching 92% and 98.9% of MB dye decay, respectively, after 120 min of illumination.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Dye Removal \%** & **Dye Removal \%** & **Dye Removal \%** & **Dye Removal \%** \\
**Time** & **with** & **with** & **with** & **with** \\
**(min)** & **SA/PVP/TiO\({}_{2}\)-1 in** & **SA/PVP/TiO\({}_{2}\)-1 in** & **SA/PVP/TiO\({}_{2}\)-1 in** & **SA/PVP/TiO\({}_{2}\)-3 in** \\  & **Dark** & **Dark**
## 3 Related Works
### Security in Wireless Interconnect  
The exploration of communication security is crucial due to the significant potential of Terahertz electromagnetic (EM) waves leaking into space through intra- and inter-chip channels. This not only impacts efficiency but also poses security risks such as eavesdropping (ED) or external interference disrupting signals. To address these concerns, Ref. [24] proposes a burst-error correction code to detect jamming attacks in wireless interconnects and suggests alternative routes. Additionally, in the case of eavesdropping (ED), a lightweight, low-latency data scrambling method is proposed. Another approach, known as Built-In Self-Test (BIST) as reported in [36], monitors wireless interconnects and can accurately detect jamming attacks with 99.87% accuracy while causing less than 3% bandwidth degradation. In [37], the authors introduce a novel detection and defense mechanism for WNoC against Denial of Service (DoS) attacks and Hardware Trojans (HT). This involves reconfigurable MAC and communication protocols, along with deploying an adversarial machine learning approach to detect attacks in the wireless network, achieving a detection accuracy of 99.87%.
# Isothermal Drying Process and its Effect on Compressive Strength of Concrete in Multiscale
## 3 Results
### Microstructure Analysis
#### 3.4.1 TGA/DSC Analysis  
The results of relative mass change and heat flow of hardened cement paste during the heating process are shown in Figure 7a-c. TGA curves of three water/cement ratios exhibited a continuous mass loss during heating up to 200 \({}^{\circ}\)C, which indicates the thermal stability of hardened cement paste within the temperature range of this test, and nothing else is lost except water. Two endothermic peaks appeared in the DSC curve, one at \(\sim\)75 \({}^{\circ}\)C and the other at \(\sim\)100 \({}^{\circ}\)C. The first endothermic peak may be attributed to the dehydration of ettringite [26], and the second may be caused by the removal of adsorbed water in C-S-H [27]. That is, the dehydration of ettringite and C-S-H occurs under the drying condition used in this study (105 \({}^{\circ}\)C), which changes the composition of the hydration products. As a result, this change would influence the shrinkage, creep, and mechanical properties of cement-based materials.  
Figure 6: Deformations in drying process of concrete at three scales, (**a**) cement paste (**b**) mortar (**c**) concrete.
# A Typology Model of Temporary Facility Constraints for Automated Construction Site Layout Planning
## 4 A Constraint Typology Model for CSLP
### Visibility Constraint  
Visibility constraints ensure visibility from temporary facilities to other objects or specific points on the site for management, safety, and security reasons. The visibility constraint instances identified in this study are summarized in Table 6.  
The visibility checking, that is, whether a subject is visible to viewers in space, can be performed using the raycasting approach. Specifically, visibility regions can be generated by three steps: first, sweep 360 degrees from the viewer's point, projecting a light ray toward to start point and end point of all segments (site boundaries or site objects' walls). Second, while sweeping, each time the start or end points of the segments closest to the viewer's point are captured, a triangle consisting of vertices (the viewer's point, start point, and end point) is filled as in Triangle P in Figure 8a. On the other hand, if the nearest segment is changed to another object's segment before the end point of the original segment closest to the viewer's point is captured, a triangle consisting of vertices (the viewer's point, start point, and another object's start point) is filled as in Triangle Q in Figure 8a. Third, iterate the second step until sweeping is completed. The set of these triangles is the visibility regions from the viewer's point where the segment hidden from the viewer due to the presence of other objects is considered [46]. Furthermore, when a closed-circuit television (CCTV) is installed to cover the visibility, it should also be deemed a viewer that creates a visibility region. Once all visibility regions are set, actual checking of the visibility constraints can be made by implementing a sight query that returns a true value when at least two segments of the object are entirely visible without being blocked (Figure 8b).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Subtype** & **Facility** & **Constraints** \\ \hline VI1 & N/A & Access road & Drivers should ensure visibility of at least x(m) on single-lane roads and at least y(m) on two-lane roads. \\ \hline VI2 & N/A & Site office & The office should be located in a place where the entire site can be viewed. \\ \hline VI3 & N/A & Storage & The storage should be in a location visible from the security office. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Visibility constraints identified in this study.  
Figure 7: Linear and travel path distance algorithms for inter-facility distance constraint.
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 2 Overview of the Existing Data Acquisition Techniques for Road Extraction
### High-Resolution Imaging Technology
#### 2.1.2 Typical Sensors  
Spaceborne high-resolution imaging is still the main technical approach for earth observation. As shown in Table 1, an increasing number of high-resolution satellites have been developed; some of these satellites have been developed in series and are constantly being upgraded [82; 83; 84; 85; 86]. It can be seen from Table 1 that most high-resolution satellites were developed and launched by the USA, while the number of high-resolution satellites in China has increased in recent years. With the development of related technologies, the performance of spaceborne cameras continues to improve, and images with a spatial resolution better than 1 m can now be obtained.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Satellite** & **Launch (Year)** & **Swath (km)** & **PAN (m)** & **R (m)** & **G (m)** & **B (m)** & **NIR (m)** \\ \hline Gaofen 1 (CN) & 2013 [87] & & 2 & 8 & 8 & 8 & 8 \\ Gaofen 2 (CN) & 2014 [88] & 70 & 0.8 & 3.2 & 3.2 & 3.2 & 3.2 \\ Gaofen 6 (CN) & 2015 [89] & & 2 & 8 & 8 & 8 & 8 \\ SuperView (CN) & 2016 [90] & 12 & 0.5 & 2 & 2 & 2 & 2 \\ GeoEye 1 (US) & 2008 [91] & 15.2 & 0.41 & 1.65 & 1.65 & 1.65 & 1.65 \\ IKONCS (US) & 1999 [85] & 11.3 & 1 & 4 & 4 & 4 & 4 \\ PlanetScope (US) & 2018 [92] & 24.6 & / & 3 & 3 & 3 & 3 \\ QuickBirds (US) & 2001 [93] & 16.5 & 0.6 & 2.4 & 2.4 & 2.4 & 2.4 \\ WorldView 1 (US) & 2007 [94] & 17 & 0.5 & / & / & / & / \\ WorldView 2 (US) & 2009 [91] & 17 & 0.5 & 2 & 2 & 2 & 2 \\ WorldView 3 (US) & 2014 [95] & 13.1 & 0.31 & 1.24 & 1.24 & 1.24 & 1.24 \\ WorldView 4 (US) & 2016 [96] & 13.1 & 0.31 & 1.24 & 1.24 & 1.24 & 1.24 \\ OrbView 3 (US) & 2003 [97] & 8 & 1 & 4 & 4 & 4 & 4 \\ RapidEye (DE) & 2008 [98] & 77 & / & 6.5 & 6.5 & 6.5 & 6.5 \\ KOMMSAT 2 (KR) & 2006 [99] & 15 & 1 & 4 & 4 & 4 & 4 \\ KOMMSAT 3 (KR) & 2012 [100] & 16 & 0.7 & 2.8 & 2.8 & 2.8 & 2.8 \\ KOMMSAT 3A (KR) & 2015 [101] & 12 & 0.55 & 2.2 & 2.2 & 2.2 & 2.2 \\ Pleiades 1A (FR) & 2011 [102] & 20 & 0.7 & 2.8 & 2.8 & 2.8 & 2.8 \\ Pleiades 1B (FR) & 2012 [103] & 20 & 0.7 & 2.8 & 2.8 & 2.8 & 2.8 \\ STOT 6 (FR) & 2012 [104] & 60 & 1.5 & 6 & 6 & 6 & 6 \\ SPOT 7 (FR) & 2014 [105] & 60 & 1.5 & 6 & 6 & 6 & 6 \\ DubaiSat 1 (AE) & 2009 [106] & 12 & 2.5 & 5 & 5 & 5 & 5 \\ DubaiSat 2 (AE) & 2013 [107] & 12 & 1 & 4 & 4 & 4 & 4 \\ \hline \end{tabular} PAN: panchromatic. R: red. G: green. B: blue. NIR: near-infrared. WorldView 2 [91] and WorldView 3 [95] have four other multispectral bands (red edge, coastal, yellow and NIR), and RapidEye has another multispectral band (red edge) [98].  
\end{table}
Table 1: Main parameters of typical high-resolution satellites.
## 3 System Model and Problem Formulation  
### System Model  
As shown in Figure 1, the residual energy maximization problem is studied in an environment of MEC system. The system includes an Access Point (AP) integrated with the MEC server and \(K\) mobile devices. The mobile devices are denoted by \(MD_{i}\), \(i\in K=\{1,2\ldots\ldots,K\}\). The AP adopts RF signal-based radio to charge \(K\) mobile devices, then receives offloaded tasks from users and transmits them to MEC for computing. AP provides energy to mobile devices by transferring RF power. The mobile devices receive and convert RF power into energy in their batteries and utilize the stored energy to execute task computation. Assume that the downlink energy transmission from AP to users and the uplink computation offloading from users to AP are implemented in the same frequency band, a block system based on FDMA is applied. Besides, the channel state remains constant in one time block and varies between different time blocks. \(t\) denotes the time block index, and \(t\in\mathcal{T}=\{0,1,2,\ldots,N\}\). \(T\) denotes one time block length. Each wireless device includes two states: active or invalid. It is assumed that the device state of each wireless device \(MD_{i}\) at each time block \(t\in\mathcal{T}\) is denoted by \(s_{i}(t)\in\{0,1\}\), (0 means invalid, 1 means active). Define the device status of all mobile devices \(MD_{i}\) during the \(t\)-th time block as \(S(t)=\{s_{1}(t),s_{2}(t),\ldots,s_{K}(t)\}\). Furthermore, we assume that the channel state information (CSI) from/to \(K\) mobile devices can be perfectly known by the AP [44]. Denote the uplink channel power gain vector in the \(t\)-th time block as \(H(t)=\{h_{1,u}(t),h_{2,u}(t),\ldots,h_{K,u}(t)\}\). Similarly, the downlink channel power gain vector is denoted by \(G(t)=\{g_{1,d}(t),g_{2,d}(t),\ldots,g_{K,d}(t)\}\). Where \(h_{i,u}^{max}<\infty\), \(g_{i,d}^{max}<\infty\), \(i\in K\).  
Figure 2 shows the harvesting and offloading model. The mobile devices offload data in the FDMA manner. Assume that the time block length is \(T\). In the \(t\)-th time block, the first \(T_{i,h}(t)\) denotes the time for each mobile device to harvest energy from the AP, and the second \(T_{i,o}(t)\) denotes the time to offload tasks from mobile devices to the AP, the execution of the offloading tasks and the download of the computing results are accomplished within the remaining time of the time block. Assume that downlink RF signals transfer and local computing are executed simultaneously.