esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Accurate urban \(PM_{2.5}\) forecasting serves a crucial function in air pollution warning and human health monitoring. Recently, deep learning techniques have been widely employed for urban \(PM_{2.5}\) forecasting. Unfortunately, two problems exist: (1) Most techniques are focused on training and prediction on a central cloud. As the number of monitoring sites grows and the data explodes, handling a large amount of data on the central cloud can cause tremendous computational pressures and increase the risk of data leakages. (2) Existing methods lack an adaptive layer to capture the varying impacts of different external factors (e.g., weather conditions, temperature, and wind speed). In this paper, a federated deep learning network (FedDeep) is developed for edge-assisted multi-urban \(PM_{2.5}\) forecasting. First, we assign each urban region to an edge cloud server (ECS). An external spatio-temporal network (ESTNet) is then deployed on each ECS. Data from different urban regions are uploaded to the corresponding ECS for training, which avoids processing all the data on the central cloud and effectively alleviates computational pressure and data leakage issues. Second, in ESTNet, we develop a gating fusion layer to adaptively fuse external factors to improve prediction accuracy. Finally, we adopted \(PM_{2.5}\) data collected from air quality monitoring sites in 13 prefecture-level cities, Jiangsu Province for validation. The experimental results proved that FedDeep outperformed the advanced baselines in terms of prediction accuracy and model efficiency.  
federated learning; edge computing; deep learning; multi-urban \(PM_{2.5}\) forecasting
## 4 Discussion
### Effect of Different Land Use Patterns on SOC Distribution
Some studies have confirmed that afforestation significantly increases the surface SOC content of farmland [51], as the sustained generation of above-ground biomass post-afforestation enhances the quantity and quality of organic matter transferred to the soil, while the cessation of tillage effectively mitigates the decomposition of SOC [52]. However, some studies have indicated that SOC content decreases following aforestation. This decline may be associated with various factors, including climate and soil conditions, types of woodlands, the duration since land use change, and the methodologies employed in research [53]. This study indicates that, in contrast to farmland, the forest belt has deeper roots and exhibits more vigorous growth, which can provide a substantial amount of deep soil organic matter through metabolic activities such as shedding, mortality, and secretion [25]. The grass belt, rich in natural vegetation, also contributes significantly to soil organic matter through the accumulation of ground litter and root exudates following the decay of herbaceous plants [54].
# A Review of Electrochemical Sensors for the Detection of Glyated Hemoglobin
## 2 Direct Type Electrochemical HbA1c Sensors
### Potentiometric Sensors  
Potentiometric HbA1c biosensors mostly include immune sensors based on integrated chips or extended gate electrode arrays as sensitive elements [60], and they can simultaneously detect Hb and HbA1c without markers. The integrated chip is built based on standard complementary metal oxide semiconductor (CMOS) technology. Before detection, anti-HbA1c antibodies are coupled with ion field effect transistors [61]. The detection mechanism of the potentiometric HbA1c sensor is that on the gate of the field effect transistor, the biofilm is deposited to form a double electric layer, and its potential changes along with the concentration of HbA1c [62; 63].  
[PERSON] et al. built a potentiometric label-free immune microsensor using the CMOS process [63]. This sensor was composed of a CMOS with a micro-signal readout circuit and disposable test strip electrodes (Figure 4A). Using a self-assembled monolayer film coated with AuNPs, the authors fixed antibodies onto the electrode surface (Figure 4B,D) and detected HbA1c and Hb within linear ranges of 4-24 mg/L and 60-180 mg/L, respectively. Furthermore, an improved AuNPs sensor with SAMs eliminated nonspecific sites and interference (Figure 4B-E). Compared to an immunosensor fabricated by the mixed SAMs method and without gold nanofilm, this sensor had twofold higher sensitivity [64]. Another group built a disposable potentiometric immune sensor using screen-printed electrodes and PET [65]. To improve the sensitivity by exposing HbA1c to the antibodies, 0.2% ammonium dodecyl trimethyl bromide was added to denature HbA1c. This sensor was successfully applied in the direct determination of HbA1c, and the results showed a good correlation between the HbA1c standard and measured values.  
Additionally, alizarin red s (ARS) can be used as an indicator of the redox reaction in the potential detection of HbA1c [66]. A negative redox potential shift was produced by binding PBA to ARS and HbA1c after a complex reaction with dial alcohol-boric acid [67]. First, the potential of the ARS-PBA complex was negative. After competition between HbA1c and ARS to bind PBA, however, the potential shifted positively, and the shift value was related to the HbA1c concentration. The concentration of HbA1c measured according to the potential changes was in good agreement with the reference results.
# A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions
## 4 Challenge: Data Secrecy
### Cryptographic Approaches
#### 4.2.1 Homomorphic Encryption  
In order to operate on the encrypted data without decryption, the encryption methods mostly rely on homomorphic encryption (HE), in which the data are processed in the ciphertext domain, i.e., with encryption, and the decrypted results are equal to the directly computed results in plaintext domain, i.e., without encryption. This property is named consistency. For more details on HE, see [108].  
The raw data are firstly encrypted by the data owners before texting, and the machine learning models can then operate on the encrypted data. The encrypted inference results or trained models are then transmitted back to the data owners and decrypted locally. Therefore, only the data owners have access to the raw data. From the computational point of view, neural network training and inference can be mainly decomposed into addition, multiplication and comparison operations. The ideas for designing the encryption approaches for DNNs are mainly to apply encryption strategies to these operations. [PERSON] et al. propose back-propagation methods based on HE and gained comparable accuracy to training in a plaintext domain [109]. Often, addition and multiplication consistencies are preserved, and the activation functions are approximated for efficiency. Such function approximation brings errors.  
Microsoft provides a cloud service, allowing the users to conduct neural computing in the ciphertext domain [110]. _Microsoft Azur_ allows the users to upload the encrypted data to the cloud and run machine learning algorithms directly on the encrypted data [111]. _Amazon SageMaker_ from _Amazon Web Service_ (AWS) provides similar services, as introduced in [112]. [PERSON] et al. propose a machine learning training framework that supports homomorphic encryption in [113].
# A Magnetic-Balanced Inductive Link for the Simultaneous Uplink Data and Power Telemetry
## 2 Magnetic-Balanced Inductive Link System
### Application Requirements on Coils  
In our intraocular biomedical sensor device, the coil sizes are limited by the application requirements, and the size of the implant coil is decided by the doctors from our cooperative hospital. To avoid blocking the eyesight, the implant coil is designed to surround the periphery of the cornea hidden behind the eyelid. The external power coil will be embedded inside the glasses frame in front of and around one eye. Table 1 lists the parameters of the coils we used. The external power coil and the implant coil are optimized for the high PTE, and at the same time, they fulfill the application constraints on the size [21]. The radius of the external data coil is set to be 8 mm without blocking the eyesight. The turn number of the external data coil is optimized for higher intensity of the received uplink signal and at the same it fulfills the system bandwidth requirement. The optimization of the turn number will be introduced in Section 4. The coil separation (\(d\)) could vary from 15 mm to 40 mm due to the different facial forms and different ways of wearing glasses, which is considered as the operating range of our system.
# Early Fire Detection on Video Using LBP and Spread Ascending of Smoke
## 2 Proposed System
### Texture Analysis and Classification
#### 2.3.3 Texture Classification  
To determine which classification method provides better performance, several classification methods were evaluated, including Multilayer Perceptron (MLP) and Support Vector Machine (SVM). For texture classification, the regions that meet the movement and color requirements are divided into small blocks to analyze their texture using LBP. A block is a candidate for texture analysis if it has at least 50% of the specific color according to Equation (13). Figure 4 shows the results of texture detection, where the red blocks indicate regions that meet the motion and color characteristics and MLP classifies them as smoke texture. The third and forth ones in Figure 4 shows some noisy blocks, i.e., false positives. However, the size of these regions decrease or remain constant such that they disappear and reappear throughout the video. Smoke zones that are not classified as smoke texture are also observed; this is because the regions within the cloud are smooth and lack texture.
## 3 Results
### Hydrometeorological Conditions in the Catchment of the River Swierszcz
#### 3.1.3 Water Flows in the River Swierszcz  
To analyse the changes in the amount of surface water in the catchment area of the River Swierszcz in the years 2021-2022, the water levels and river flows were measured at the Painted Bridge measuring point (P3) at the outlet of the 18.15 km\({}^{2}\) catchment. The seasonal outflow rhythm of the River Swierszcz during the study period was characteristic of rain-fed rivers. In the hydrological year 2020/2021, the lowest average monthly flows were recorded from November to January, and the highest ones were measured in July (Figure 7). The substantial differences in average monthly flows were the result of extremely high precipitation in the summer. In 2021, during the 10 days between 23 June and 3 July, the area received 253 mm of rain, which was 35% of the average annual precipitation. After these heavy rainfalls, there was a clear increase in supply from the main groundwater aquifer, and outflow from the upper peat bog part of the catchment of the River Swierszcz was renewed. In the warm season of 2021, river flow was similar to the long-term average at 41.10 L/s. In the cold season, it was 31.03 L/s. In the hydrological year 2021/2022, the water flow distribution for the River Swierszcz was completely different. In the warm season, the river flow was 31.3 L/s and was lower than the flow in the cold season when it was 46.7 L/s. This was due to the more even distribution of precipitation throughout the year, as mentioned earlier. In the hydrological year 2021/2022, high water flows were observed in winter and spring. Flows in February and April were 60.42 and 62.11 L/s, respectively (Figure 7). Despite high rainfall in the summer, the average flow of the River Swierszcz in the hydrological year 2020/2021 was 27.99 L/s. In the next hydrological year 2021/2022, the river's average annual flow increased to 40.03 L/s. This value was only slightly lower than the river's average flow for the 20 years between 2012-2020, which was 42 L/s.
# Stability Analysis of Multi-Agent Tracking Systems with Quasi-Cyclic Switching Topologies
## 2 Preliminaries and Problem Formulation
### Problem Formulation
As shown in Table 2, \(\phi_{i}(i=1,2,...,m;m<N_{T})\) belongs to a quasi-cyclic switching topology set of stable subsystems, \(\phi_{j}(j=m+1,m+2,...,N_{T})\) belongs to a quasi-cyclic switching topology set of unstable subsystems. Let \(N_{s}\), \(N_{us}\) be the length of the period of stable subsystems and unstable subsystems respectively, so that \(N_{s}=m,N_{us}=N_{T}-N_{s}\). Figure 1 displays a possible case of Table 2, where the quasi-switching follows Assumption 1. As shown in Figure 1, the switching times are represented by \(t_{\kappa}(\kappa\in\mathbb{N})\), which consist of the switching not only among consecutive stable subsystems or consecutive unstable subsystems but also between stable and unstable subsystems. The time series \(p_{k}(p_{k}\in t_{\kappa},k\in\mathbb{N})\) is introduced to denote the switching times between stable and unstable subsystems. When \(p_{0}=t_{0},p_{1}=t_{s_{1}},\ldots,p_{k}=t_{s_{k}},\ldots p_{n}=t_{s_{n}}(s_{ \zeta}\in\kappa,\zeta\in\mathbb{N})\), the systems are switched between stable and unstable systems. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{5}{c}{**Stable Subsystems**} & \multicolumn{3}{c}{**Unstable Subsystems**} \\ \hline \(\phi_{1}\) & \(\phi_{2}\) &... & \(\phi_{m}\) & \(\phi_{m+1}\) &... & \(\phi_{N_{T}}\) \\ \hline \(\phi(1)\) & \(\phi(d_{1}+1)\) & \(\phi(d_{m-1}+1)\) & \(\phi(d_{m}+1)\) & \(\phi(d_{s-1}+1)\) \\ \(\phi(2)\) & \(\phi(d_{1}+2)\) & \(\phi(d_{m-1}+2)\) & \(\phi(d_{m}+2)\) & \(\phi(d_{s-1}+2)\) \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\ \(\phi(d_{1})\) & \(\phi(d_{2})\) & \(\phi(d_{m})\) & \(\phi(d_{m+1})\) & \(\phi(d_{s})\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The schematic table for Assumption 1.
## 4 Hardware Design of Dual-Phase Sensitive Lock-in Amplifier Circuits
### Low-Pass Filter Circuit  
A low-pass filter is a type of filter that permits signals below a specific frequency to pass through. In this circuit design, the low-pass filter's primary function is to remove the AC component resulting from the multiplication operation performed by the phase-sensitive detector, ensuring that only the DC component is preserved. This paper presents the design of a fourth-order low-pass filter using the Butterworth filter characteristic with a flat region and the Butterworth filter normalization table. The transfer function \(T_{N}\)(s) of the \(N\)th order low-pass filter is of the form (19), as indicated in the table.  
\[T_{N}(s)=\frac{1}{\prod\limits_{i=1}^{N}(s-s_{i})}=\frac{1}{1+a_{1}s+a_{2}s^{ 2}+\cdots a_{N-1}s^{N-1}+a_{N}s^{N}} \tag{19}\]  
Therefore, the transfer function \(T_{4}\)(s) of the fourth-order low-pass filter is Equation (20).  
\[T_{4}(s)=\frac{1}{\prod\limits_{i=1}^{4}(s-s_{i})}=\frac{1}{1+a_{1}s+a_{2}s^{2 }+a_{3}s^{3}+s^{4}}=\frac{1}{(1+b_{1}s+b_{2}s^{2})(1+b_{3}s+b_{4}s^{2})} \tag{20}\]  
The given circuit can be interpreted as two second-order low-pass filters connected in series, as shown in Figure 8. The filters have different resistance and capacitance values.  
Based on its sketch, the second-order low-pass filter transfer function \(T(s)\) can be calculated as Equation (21).  
\[T(s)=\frac{1}{R_{1}R_{2}C_{1}C_{2}s^{2}+(R_{1}+R_{2})\times C_{2}\times s+1} \tag{21}\]  
Figure 8: Schematic diagram of second-order low-pass filter.  
Figure 7: Internal schematic and pins of AD835.  
When \(R_{1}=R_{2}=R_{f}\), the relationship between the cutoff frequency \(f_{c}\) and the capacitor resistance impedance is Equation (22).  
\[f_{c}=\frac{1}{2\pi\sqrt{\mathbb{C}_{1}\mathbb{C}_{2}R_{f}}} \tag{22}\]  
The two signals that were multiplied are passed through a fourth-order low-pass filter module to retain the DC component. The amplitude and phase values of the original target signal are then obtained by digital processing of Equations (11) and (12). Combining the above analysis of the modules of the designed circuit, we constructed a circuit suitable for processing weak signals at a target frequency of 1 MHz. In addition, the transfer function and the circuit system were thoroughly studied and analyzed in this paper.
## 2 Experimental Set Up and Test Procedure
### Experimental Procedure  
The static strain measurements are carried out in two consecutive stages, (1) the confining pressure (oil pressure in the high-pressure vessel) is increased incrementally from 0.5 to 12 MPa. (2) the pore pressure is increased incrementally from 0 to 10 MPa by injection of pure water into the sample. Two syringe pumps are employed for applying the confining pressure and water injection respectively. The temperature of the fluid in the two syringe pumps is controlled at 40 \({}^{\circ}\)C. by a  
Figure 3: A schematic diagram of the experimental setup.  
Figure 2: A schematic showing the position of the four strain gauges in relation to the optic fiber (blue line): (**a**) Berea sandstone core; (**b**) Tako sandstone core. Also shown is the position of the selected points where the frequency shifts are monitored.  
circulator. The high-pressure vessel is covered with a heating rubber heater and maintained at 40 \({}^{\circ}\)C The syringe pump for water injection is connected to the bottom end of the sample.  
The experimental procedure at each stage is as follows.  
1. Measurements while increasing confining (oil) pressure * After placing the sandstone sample into the high-pressure vessel, the system is vacuumed to remove residual air from the sample and the pure water injection line. * Increase the oil pressure inside the high-pressure vessel to 0.5 MPa and monitor the strain readings until they stabilize (no change for 30 minutes). Then set the strain readings to zero (initialization). * Perform Brillouin and Rayleigh frequency shift measurement respectively. * Increase the oil pressure to 2 MPa, then incrementally up by 2 MPa to 12 MPa. After stabilization at each step, perform the frequency shift measurement respectively.
2. Measurements while increasing pore pressure * The oil pressure in the high-pressure vessel is maintained at 12 MPa. * Set the flow rate of pure water syringe pump to 0.05 mL/min and a target pressure of 0.5 MPa, then put it in run state and open the water valve. When the water injection pressure is stabilized at 0.5 MPa, the strain readings are initialized (set to zero), and Brillouin and Rayleigh frequency shift measurements are performed respectively. * Increase the water injection pressure to 2 MPa, then incrementally up by 2 MPa to 10 MPa at injection rate of is 0.05 mL/min. At each pore pressure step, take strain readings and perform Brillouin and Rayleigh frequency shift measurements when the injection pressure is stabilized.
## 4 Materials and Methods
### CCCP Induction of P. aeruginosa Planktonic Persisters and Evaluation of LRM-CFS Activity
After 24 h of incubation, the medium was changed with fresh ASM and incubated for an additional period of 24 h, resulting in a total incubation period of 48 h. Following incubation, wells were gently washed thrice with PBS to remove non-biofilm-embedded bacteria and resuspended in ASM with ciprofloxacin concentrations of 16 ug/mL (64 x MIC), 32 ug/mL (128 x MIC), and 64 ug/mL (256 x MIC) and incubated at 37\({}^{\circ}\)C for 8 h and 24 h.  
Following incubation, wells were washed thrice with PBS to remove non-biofilm-embedded bacteria. A sterile tip was used to detach biofilms from the surface of each well, and biofilm-derived cells were resuspended in 1 mL of PBS. To obtain single-cell suspensions, biofilm cells were vortexed for 30 s, sonicated for 30 s in a water bath sonicator (Ultrasonic cleaner, VWR), and vortexed for a further 30 s. The CFU count of biofilm-associated bacteria was performed at time 0, after 8 h and 24 h incubation with ciprofloxacin. To this aim, bacterial suspensions were serially diluted, plated on TSA agar plates, and incubated for 24-72 h for CFU enumeration. The persister cells obtained after exposure of biofilm with ciprofloxacin at the concentration of 32 ug/mL were treated with LRM-CFS diluted at 1:4, 1:6, and 1:8 for 24 h.  
To confirm the antibiotic persistence of _P. aeruginosa_ in the treated biofilm, all the experimental samples were simultaneously treated with ciprofloxacin a second time at the same concentration (32 \(\mathrm{\SIUnitSymbolMicro g}\)/mL). Biofilm persister bacteria were washed thrice with PBS to remove ciprofloxacin and were then resuspended in ASM in the presence of LRM-CFS and incubated in static conditions at the temperature of 37 \({}^{\circ}\)C. Following incubation, wells were gently washed thrice with PBS to remove non-biofilm-embedded bacteria. Biofilm-associated bacteria in wells were disaggregated as previously described in this subsection, and bacterial suspensions were serially diluted on TSA agar plates and incubated for 24-72 h for CFU enumeration. A control condition was prepared by adding the same amount of MRSB at pH = 4 and incubating in the same conditions.
## The Ocean
### SARAL/AltiKa a Data Into Operational Systems  
Altimetry is one of the most important data sources for assimilation into operational models. It provides global, real time, all-weather SSH measurements with high space and time resolution. Sea level is directly related to ocean circulation through the geostrophic approximation but sea level is also an integral of the ocean interior and is a strong constraint for inferring the 4D ocean circulation through data assimilation. Altimeters also measure SWH, which is essential for operational wave forecasting. High resolution from multiple altimeters is required to adequately represent ocean eddies and associated currents in models. Only altimetry can constrain the 4D mesoscale circulation in ocean models which is required for most operational oceanography applications.  
There are very strong links between satellite oceanography and operational oceanography. The development of operational oceanography has been mainly driven by the development of satellite oceanography capabilities. The ability to observe the global ocean in near real time at high space and time resolution is indeed a prerequisite to the development of global operational oceanography and its applications. The first ocean parameter to be globally monitored from space was the SST on board meteorological satellites in the late 1970s. It is, however, the advent of satellite altimetry in the late 1980s that led the development of ocean data assimilation and global operational oceanography.  
Figure 5: (**a**) Area of study in the Western Mediterranean. Colour contours represent bottom bathymetry and the white box corresponds to the area covered by the Ibiza HF radar shown in (**b**). (**b**) Coverage of SOCIB HF radar in the Ibiza Channel. The vectors correspond to surface velocities derived from HF radar (17 April 2013) field (three-day average). The colours are currents magnitude (in m/s). SARAL/AltiKa track 16 and Jason-2 track 187 are overlaid. The dots correspond to the position of the geostrophic velocity vectors derived from the altimeter missions. Note that the along-track delayed-time data provided at CMEMS is filtered with a 42 km filter and sub-sampled every 14 km (see details in [13]). (**c**) Comparison of the temporal variability of SARAL/AltiKa and HF radar mean cross-track velocity. X-axis is the number of cycles. (**d**) The same as (**c**) but for Jason-2.  
The satellite altimetry community was also keen to develop further the use of altimetry and this required an integrated approach merging satellite and in-situ observations with models.