esa-sceva/satcom-chunk-collection huggingface dataset:
# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
## 4 Methods
### System Architecture
A node selection smart contract chooses computing nodes based on reputation data from an audit verification contract. A data distribution smart contract assigns shares and masks, creating an on-chain index table. Computing nodes download data from IPFS, perform local computations, and upload results to IPFS. The audit verification smart contract inspects results, updates reputation, and syncs permission states. Participants reconstruct the Hamming distance as the outcome. Blockchain records metadata, IPFS stores data, and smart contracts orchestrate the process, ensuring privacy, efficiency, and auditability.
## 2 Relative Humidity
### Evaluation of Shelf-Life Prediction Model  
According to Section 2.2, 20,160 sets of data were obtained by a variable temperature data collection experiment. The training set data were at 14,112, and the test set data were at 6048. The proposed BP neural network prediction model based on MATLAB R2019a used temperature, humidity, O\({}_{2}\) and CO\({}_{2}\) level as the input, and shelf-life as the output.  
The BP neural network stopped when the Validity Checks reached 6, the number of epochs was 343, the best validation performance was 0.008954 at epoch 337 (Figure 8(a)), and the R-value was close to 1 (Figure 8(b)). According to Figure 8(c), most of the predicted values fall on the actual value. The error was in the range of 0.002-0.267 d. It can be seen that the proposed model was in high prediction accuracy and could meet the requirements of shelf-life prediction.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Quality Parameter**} & \multicolumn{3}{c}{**Relative Humidity**} & \multicolumn{3}{c}{**Oxygen Concentration**} & \multicolumn{3}{c}{**Carbon Dioxide Concentration**} \\ \cline{2-10}  & **0**\({}^{\circ}\)**C** & **5**\({}^{\circ}\)**C** & **20**\({}^{\circ}\)**C** & **0**\({}^{\circ}\)**C** & **5**\({}^{\circ}\)**C** & **20**\({}^{\circ}\)**C** & **0**\({}^{\circ}\)**C** & **5**\({}^{\circ}\)**C** & **20**\({}^{\circ}\)**C** \\ \hline Sensory Evaluation & \(-0.82\) & \(-0.80\) & \(-0.48\) & \(0.92\) & \(0.83\) & \(0.86\) & \(-0.85\) & \(-0.86\) & \(-0.82\) \\ \hline Hardness & \(-0.91\) & \(-0.84\) & \(-0.39\) & \(0.99\) & \(0.87\) & \(0.86\) & \(-0.80\) & \(0.85\) & \(-0.83\) \\ \hline Weightlessness & \(0.85\) & \(0.80\) & \(0.46\) & \(-0.95\) & \(-0.82\) & \(-0.84\) & \(0.80\) & \(-0.90\) & \(0.90\) \\ \hline Relative Conductivity & \(0.62\) & \(0.65\) & \(0.42\) & \(-0.73\) & \(-0.80\) & \(0.75\) & \(0.55\) & \(0.72\) & \(0.70\) \\ \hline Sugar-acid Ratio & \(-0.48\) & \(-0.28\) & \(-0.61\) & \(0.61\) & \(0.30\) & \(0.84\) & \(-0.23\) & \(-0.33\) & \(-0.81\) \\ \hline VC Content & \(-0.98\) & \(-0.90\) & \(-0.45\) & \(0.99\) & \(0.93\) & \(0.93\) & \(-0.80\) & \(-0.96\) & \(-0.89\) \\ \hline L* Value & \(-0.71\) & \(-0.78\) & \(-0.51\) & \(0.82\) & \(0.82\) & \(0.86\) & \(-0.74\) & \(-0.85\) & \(-0 In addition, this paper also compared the prediction performance of the proposed model with the Support Vector Regression (SVR) and Recurrent Neural Network (RNN) models, which were widely used in the field of shelf-life prediction. Table 5 shows that the maximum error of SVR could reach 0.620 d, and the maximum error of RNN reached 0.716. It can be seen that the proposed BP neural network model had better performance in shelf-life prediction.
# Regional Manufacturing Industry Demand Forecasting: A Deep Learning Approach
## 4 Experimental Results
### Results
#### 4.3.2 Comparative Prediction Model  
To verify the accuracy of LSTM, this paper compared it with four models, namely, SVM, BP, RF, and AR. The input indexes of all models were consistent. Because the data units of each index are different, to get more accurate prediction results, the original data of each index were normalized and mapped to [0,1][57].
## 2 System Model
Vehicles in our system have limited caching capacity, which is divided into two parts: Caching space and relay space. The former is used to cache the content which is of high popularity or is attractive to the vehicle user, and it can accommodate content with the maximum number of \(N(N<L)\). The latter is used to temporarily cache content chunks that will be delivered to the request vehicle when the vehicle meets the request vehicle. The vehicles selected to temporarily cache content are called as the relay vehicles. Because the relay space only caches part of the content, the amount of content that the relay space can cache was set to 2. The common symbols are showed in Table 1.
## 3 Results and Discussion
### Ftir  
The infrared spectra for the untreated and OTS-treated SiO\({}_{2}\) NPs, as well as for the OTS reference and clay substrate, are shown in Figure 6. The band detected around 1076 cm\({}^{-1}\) in the spectrum for the SiO\({}_{2}\) NPs is attributed to the siloxane functional group (Si-O-Si), corresponding to the asymmetric stretching and bending vibrations. The band detected at 800 cm\({}^{-1}\) is associated with the symmetrical stretching vibration of Si-O, which corresponds to the silicate ion transmittance band. Similarly, the band around 500-450 cm\({}^{-1}\) is associated with the siloxane bending modes. However, the double band observed at 564 and 588 cm\({}^{-1}\) for the OTS reference corresponds to the Si-Cl stretching mode, which vanishes in the preparation of the OTS-treated SiO\({}_{2}\) NPs [45]. The band detected at 1468 cm\({}^{-1}\) is associated with asymmetric CH\({}_{3}\) bending, and the bands around 2852 and 2924 cm\({}^{-1}\) are associated with the OTS CH\({}_{3}\) and CH\({}_{2}\) long-chain alkyl groups, respectively, which allow us to confirm an interaction between the OTS and SiO\({}_{2}\) NPs. The obtained spectrum for the clay substrate shows the contribution from the different materials as it is addressed in refs. [46; 47; 48]. The broad band 1300-800 cm\({}^{-1}\) is attributed to phyllosilicates and tectosilicates, which are characteristic in silica tetrahedron-alumina octahedron-silica tetrahedron (T-O-T) structures. The band around 700-450 cm\({}^{-1}\) can be attributed to the presence of quartz, hematite and kaolinite structures. The FTIR spectrum of the OTS-treated SiO\({}_{2}\) NPs/clay shows the characteristic bands previously mentioned, such as the long-chain alkyl groups of OTS; it also shows the asymmetric and bending vibrations  
Figure 4: The 3D mapping of the surface roughness for (**a**) clay substrate and samples of OTS-treated SiO\({}_{2}\) NPs: (**b**) S1 one-layer deposition, (**c**) S2 two-layer deposition and (**d**) S3 three-layer deposition.  
Figure 5: Average roughness values (Ra) calculated for clay substrate and samples S1–S3.  
of the siloxane functional groups (Si-O-Si) and the symmetrical stretching vibration of Si-O, both corresponding to SiO\({}_{2}\) NPs, as well as showing the broad band attributed to the T-O-T structures of the clay. With all this information, it is possible to consider that the interaction between the SiO\({}_{2}\) NPs and the substrate is due to the rough surface of the clay and to the electrostatic forces between the surface and treated nanoparticles.
# Design and Implementation of a Smart Traffic Signal Control System for Smart City Applications
## 2 Related Works on ITS Applications  
### Emergency Vehicle Signal Preemption (EVSP)  
More than 98% of respondents have encountered ambulances on public roads in Dearborn Heights, but 82.9% of the respondents had at least one experience of failing to respond appropriately when emergency vehicles (EV) approached [6]. The paper pointed out that, in the United States between 2004 and 2008, there were 3708 accidents involving emergency vehicles. After the in-vehicle information system (such as on-board unit, smart phone, radio), the interference to the general driving is more serious, which will cause the driver to have a slower response to the emergency vehicle. Emergency vehicles need to respond quickly to save the lives and property. In some emergency situations, early response is very important to affect life and death in a few seconds. Every one minute of delay in the patient's first aid, the chance of survival would be reduced by 7-10%. For a fire accident, half a minute for each delay will double the fire. For traffic accidents, one third of the deaths associated with vehicle accidents can be prevented by ensuring faster rescue of emergency vehicles, so rapid rescue is already a very urgent and important matter [7].  
In order to reduce EV rescue delay, [PERSON] et al. use LED equipment to display the signs of emergency vehicles [6]. When an EV approaches the intersection, the OBU in EV will communicate with the signal controller. The traffic signal controller then turns on the emergency vehicle LED flashing, the passersby can react early to the emergency vehicle approach and then prepare for the early arrival. However, EV drivers still have to slow down when driving through the intersection due to the signal controller not supporting emergency vehicle signal preemption.  
In [7], an EVSP system was proposed to solve this problem by extending the green period or cutting off the red period to facilitate the EV passing through the intersection. It assigns different weights to different types of EV, such as the ambulance, fire engine, and police vehicles, so that the signal controller can serve different types of EVs by their priorities. However, the issue of maximum and minimum green period in traffic engineering is not discussed in this article. [PERSON] et al. proposed an improved algorithm that reduces response time and minimizes the effects of general traffic by using two control strategies [8]. The system changes the traffic state to EVSP transition 1 to reduce the traffic impact time, after transition 1, the system switches to EVSP transition 2 for traffic flow compensation.
## 5 Generation-Reserve Co-Optimization Model
### Constraints  
(1) Power balance constraints.  
At any time period \(t\), the generation should cater for the demand, and the power balance constraints can be expressed as  
\[\sum_{i\in G}P_{i,t}^{G}+\sum_{j\in R}P_{j,t}^{R}=\sum_{k\in D}P_{k,t}^{D} \tag{21}\]  
where \(G\), \(R\), and \(D\) are the set of SGs, RG, and load, respectively.  
(2) Output margin constraints.  
At any time period \(t\), the load of generators cannot cross their upper and lower output limits, which can be expressed as  
\[P_{\min,i}^{G}\leq P_{i,t}^{G}\leq P_{\max,i}^{G} \tag{22}\]  
where \(P_{\min,i}^{G}\) and \(P_{\max,i}^{G}\) are the minimum and maximum capacities of generator \(i\). Furthermore, considering its provided reserve, the load of each generator cannot cross its upper and lower limits, either, which can be expressed as  
\[P_{\min,i}^{G}\leq P_{i,t}^{G}+r_{i,t}^{u}\leq P_{\max,i}^{G} \tag{23}\]  
\[P_{\min,i}^{G}\leq P_{i,t}^{G}-r_{i,t}^{d}\leq P_{\max,i}^{G} \tag{24}\]  
The output of the grid-supporting IBG units also satisfies these constraints.  
(3) Ramp constraints.  
For the transition of power output in adjacent periods, the ramp constraints can be expressed as  
\[-P_{ramp,i}^{d}\Delta T\leq P_{i,t+1}^{G}-P_{i,t}^{G}\leq P_{ramp,i}^{u}\Delta T \tag{25}\]  
where \(P_{ramp,i}^{u}\) and \(P_{ramp,i}^{d}\) are the upward and downward ramp rate of generator \(i\), respectively. \(\Delta T\) is the dispatch interval. For the reserve, it needs to be dispatched within a preset time. Therefore, the ramp constraints for reserves can be expressed as  
\[r_{i,t}^{u}\leq P_{ramp,i}^{u}T_{r} \tag{26}\]  
\[r_{i,t}^{d}\leq P_{ramp,i}^{d}T_{r} \tag{27}\]  
where \(T_{r}\) is the reserve dispatch interval. For the IBG units, since the control of PEIs is fast and accurate, the ramp rate is set to be infinite.  
(4) Reserve constraints.  
At any time period \(t\), the total reserves in the system must meet the reserve requirement according to the rated LOLP and RSP criteria. Therefore, the accumulation of the upward and downward reserves satisfies  
\[R_{t}^{u}\leq\sum_{i=1}^{N}r_{i,t}^{u} \tag{28}\]\[R_{t}^{d}\leq\sum\limits_{i=1}^{N}{r_{i,t}^{d}} \tag{29}\]  
where \(R_{t}^{u}\) and \(R_{t}^{d}\) are reserve requirements under preset LOLP and RSP reliability criteria. Furthermore, considering the outage of a single unit, the reserve constraints satisfying the _N-1_ scenario can be expressed as  
\[\forall i\in N,\text{ it satisfies}\left\{\begin{array}{ll}R_{t}^{u}\leq \sum\limits_{i=1}^{N}&r_{j,t}^{u}\\ &j=1\\ &j\
eq i\\ R_{t}^{d}\leq&\sum\limits_{j=1}^{N}&r_{j,t}^{d}\\ &j\
eq i\\ \end{array}\right. \tag{30}\]
# A Location Routing Problem with Time Windows Consideration: A Metaheuristics Approach
###### Abstract
The distribution system of chain stores, cold-chain, and relief goods distribution can be represented as LRP with time windows problem, where each vehicle has a service level agreement within the distribution center and customers (retailers, shelters, stores, etc.). Each customer has to be served before a certain desired time. Each driver also has a certain working time which is considered as a constraint as well. Other practices include the flood evacuation network and distribution with electric vehicles. This research extends the LRP model into the location routing problem with time windows (LRPTW) with goals to determine: (i) the depots to open, (ii) the customers, and (iii) the vehicles to be assigned to each open depot and the routes to be created to satisfy the demand within restricted time windows of the customers with the smallest cost. This paper tackles both location and routing problems together with consideration of time windows and capacity restrictions at depots and vehicles for different size instances. This study is also the first attempt to create an instance of an LRPTW problem from LRP instances. The heuristic variable neighborhood search with path relinking (VNSPR) is presented as a method to solve the LRPTW. One key contribution of this paper is initiating a basis for evaluating the performance of VNSPR which demonstrates capability of solving CLRP and its extensions. This paper is organized as follows: Section 1 provide introduction and reviews previous studies on the CLRP, VRPTW and the related problems, Section 2 consist of two part, first presents the problem definition and mathematical formulation of the LRPTW, second gives an overview of the VNS and PR algorithm and introduces the proposed VNSPR heuristic for solving the LRPTW, while Section 3 presents the benchmark instances and algorithm verification, Section 4 provide discussion on the propose VNSPR to solve LRPTW, Section 5 draws conclusions and points out possible future research directions.
# nmODE-Unet: A Novel Network for Semantic Segmentation of Medical Images
## 4 Experiments and Results
### Discussion  
Due to the small size of DR lesions and the high interclass similarity in terms of location, color, and shape among different lesions, the segmentation task is highly challenging. To further enhance the accuracy of DR lesion segmentation, we proposed a novel framework, nmODE-Unet, and conducted comparative experiments with commonly used image segmentation networks and other state-of-the-art methods. The experimental results indicate that our approach is competitive. Particularly, the segmentation of SE and MA lesions on the IDRiD dataset has achieved the highest values we are aware of.  
Due to the poor image quality and noise interference in the e-ophtha dataset, all methods experienced a significant decrease in segmentation accuracy, particularly in the case of MA lesions. MA lesions are extremely small in size, share similarities with retinal blood vessels, and are susceptible to information loss during the downsampling process, exacerbated by noise, leading to mis-segmentation issues. However, while our method also exhibited reduced accuracy, it remained significantly superior to other approaches. As evident from Tables 3 and 4, the segmentation performance of nmODE-Unet surpasses that of other methods by a wide margin, primarily because nmODE possesses a unique global attractor. The presence of this global attractor enhances the network's resistance to noise.  
We have measured the FLOPs, parameters, memory usage, and inference time for nmODE-Unet and the baseline model. The results are presented in Table 8. Unet++ and DeepLab v3+ exhibited the highest computational complexity. Unet++ recorded the highest FLOPs, while DeepLab v3+ had the highest parameter count and memory usage. In comparison to the U-Net backbone, nmODE-Unet does not increase FLOPs and parameters. However, both inference time and memory usage are increased due to the iterative process required by the nmODE block for solving ordinary differential equations. Despite the absence of increased FLOPs and parameters, nmODE-Unet exhibits an improved segmentation performance. Nonetheless, the drawback lies in the longer inference time, indicating the need for further improvement in this regard.
# Improving Localization of Deep Inclusions in Time-Resolved Diffuse Optical Tomography
## Appendix A Windows Selectivity Calculations
### Mellin-Laplace Moments  
\[\int_{-\infty}^{\infty}|g(t)|^{2}\,\mathrm{d}t =\int_{-\infty}^{\infty}\left|t^{n}e^{-pt}H(t)\right|^{2}\,\mathrm{d}t\] \[=\int_{0}^{\infty}t^{2n}e^{-2 pt}\,\mathrm{d}t=\frac{(2n)!}{(2p)^{ 2n+1}}.\]  
\[\int_{-\infty}^{\infty}t^{2}|g(t)|^{2}\,\mathrm{d}t =\int_{-\infty}^{\infty}t^{2}\left|t^{n}e^{-pt}H(t)\right|^{2}\, \mathrm{d}t\] \[=\int_{0}^{\infty}t^{2n+2}e^{-2 pt}\,\mathrm{d}t=\frac{(2n+2)!}{(2 p)^{2n+3}}.\]  
\[\int_{-\infty}^{\infty}|G(f)|^{2}\,\mathrm{d}f =\int_{-\infty}^{\infty}\left|\frac{n!}{(p+i2\pi f)^{n+1}}\right| ^{2}\,\mathrm{d}f\] \[=\frac{n!^{2}}{2\pi|p|^{2n+1}}\int_{-\pi/2}^{\pi/2}\frac{\sec^{2 }x}{\left|(p+i\tan x)\right|^{2n+2}}\,\mathrm{d}x\qquad(2\pi f=p\tan x)\] \[=\frac{n!^{2}}{2\pi|p|^{2n+1}}\int_{-\pi/2}^{\pi/2}\cos^{2n}x\, \mathrm{d}x\] \[=\frac{n!^{2}}{\pi|p|^{2n+1}}\int_{0}^{\pi/2}\cos^{2n}x\,\mathrm{ d}x\] \[=\frac{n!^{2}}{\pi|p|^{2n+1}}W_{2n}.\]  
\[\int_{-\infty}^{\infty}f^{2}|G(f)|^{2}\,\mathrm{d}f =\int_{-\infty}^{\infty}f^{2}\left|\frac{n!}{(p+i2\pi f)^{n+1}} \right|^{2}\,\mathrm{d}f\] \[=\frac{(n!\,p)^{2}}{(2\pi)^{3}|p|^{2n+1}}\int_{-\pi/2}^{\pi/2} \frac{\tan^{2}x\,\sec^{2}x}{\left|(p+i\tan x)\right|^{2n+2}}\,\mathrm{d}x\qquad (2\pi f=p\tan x,\quad n\geq 1)\] \[=\frac{(n!\,p)^{2}}{(2\pi)^{3}|p|^{2n+1}}\int_{-\pi/2}^{\pi/2} \tan^{2}x\,\cos^{n}x\,\mathrm{d}x\] \[=\frac{(n!\,p)^{2}}{(2\pi)^{3}|p|^{2n+1}}\int_{-\pi/2}^{\pi/2} \sin^{2}x\,\cos^{2n-2}x\,\mathrm{d}x\] \[=\frac{(n!\,p)^{2}}{(2\pi)^{3}|p|^{2n+1}}\left(\int_{-\pi/2}^{ \pi/2}\cos^{2n-2}x\,\mathrm{d}x-\int_{-\pi/2}^{\pi/2}\cos^{2n}x\,\mathrm{d}x\right)\] \[=\frac{(n!\,p)^{2}}{(2\pi)^{3}|p|^{2n+1}}\left(2W_{2n-2}-2W_{2n} \right)\qquad(n\geq 1).\]  
where \(W_{n}=\int_{0}^{\pi/2}\cos^{n}x\,\mathrm{d}x\) is the Wallis integral.  
\[D_{0}(g) =\frac{(2n+2)!}{(2p)^{2n+3}}\cdot\frac{(2p)^{2n+1}}{(2n)!}=\frac{(2n+2)!} {(2n)!}\cdot\frac{1}{(2p)^{2}}.\]  
\[D_{0}(G) =\frac{(n!\,p)^{2}}{(2\pi)^{3}|p|^{2n+1}}\left(2W_{2n-2}-2W_{2n} \right)\cdot\frac{\pi|p|^{2n+1}}{n!^{2}W_{2n}}\] \[=\frac{p^{2}}{4\pi^{2}}\left(\frac{W_{2n-2}}{W_{2n}}-1\right)\] \[=\frac{p^{2}}{4\pi^{2}}\left(\frac\[\boxed{D_{0}(g)D_{0}(G)=\frac{1}{16\pi^{2}}\frac{(2n+2)!}{(2n)!}\left(\frac{2n}{2n -1}-1\right),\qquad(n\geq 1).}\]  
Therefore,  
\[\frac{1}{16\pi^{2}}\frac{(2n+2)!}{(2n)!}\left(\frac{2n}{2n-1}-1\right)>\frac{1} {16\pi^{2}},\qquad(n\geq 1).\]
###### Abstract  
Among the 5G and anticipated 6G technologies, non-orthogonal multiple access (NOMA) has attracted considerable attention due to its notable advantages in data throughput. Nevertheless, it is challenging to find the near-optimal allocation of the channel and power resources to maximize the performance of the multi-cell NOMA system. In addition, due to the complex and dynamically changing wireless communication environment and the lack of the near-optimal labels, conventional supervised learning methods cannot be directly applied. To address these challenges, this paper proposes a framework of MDRL-UL that integrates the multi-agent deep reinforcement learning with the unsupervised learning to allocate the channel and power resources in a near-optimal manner. In the framework, a multi-agent deep reinforcement learning neural network (MDRLNN) is proposed for channel allocation, while an attention-based unsupervised learning neural network (ULNN) is proposed for power allocation. Furthermore, the joint action (JA) derived from the MDRLNN for channel allocation is used as a representation to be fed into the ULNN for power allocation. In order to maximize the energy efficiency of the multi-cell NOMA system, the expectation of the energy efficiency is used to train both the MDRLNN and the ULNN. Simulation results indicate that the proposed MDRL-UL can achieve higher energy efficiency and transmission rates than other algorithms.  
Keywords:non-orthogonal multiple access (NOMA); channel allocation; power allocation; multi-agent deep reinforcement learning; unsupervised learning; attention mechanism _Article_
## 2 Data Description
### Swarm B Langmuir Probe Observations  
Swarm is a constellation of three LEO satellites launched on the 22 nd of November 2013 and is still in operation [16]. Among the three satellites, Swarm B has a circular near-polar orbit with an inclination of 87.75\({}^{\circ}\) and an initial altitude of around 500-510 km, which is similar to that of CSES--01. In contrast to CSES--01, Swarm B spans all the LTs in about 130-140 days. Swarm B provides in situ \(N_{\text{e}}\) observations through two different instruments of the Electric Field Instrument (EFI) payload [35]: spherical LPs and the FP of the Thermal Ion Imager (TII). LPs provide continuous \(N_{\text{e}}\) observations at 2-Hz rate, while the FP has a 16-Hz rate; however, corresponding observations are not continuous in time [35; 36; 37; 38]. Swarm data are freely downloadable at [[https://earth.esa.int/eogateway/missions/swarm/data](https://earth.esa.int/eogateway/missions/swarm/data)]([https://earth.esa.int/eogateway/missions/swarm/data](https://earth.esa.int/eogateway/missions/swarm/data)) (accessed on 7 April 2023).  
In this work, we consider Swarm B LP \(N_{\text{e}}\) observations in the same time window as the CSES--01 dataset, i.e., from the 1 st of January 2019 to the 30 th of September 2021. To be consistent with the CSES--01 dataset, we also considered only Swarm B observations in the range between 70\({}^{\circ}\) S and 70\({}^{\circ}\) N in terms of geographic latitude, and for the two LT sectors sounded by CSES--01. Specifically, we selected Swarm B observations in the range 01:00 \(\leq\) LT < 03:00 for the nighttime sector and in the range 13:00 \(\leq\) LT < 15:00 for the daytime sector [34].  
In a similar manner to CSES--01, Swarm B LP \(N_{\text{e}}\) observations also underwent a calibration procedure based on the results of [34]. In fact, the authors of [34; 39; 40] independently highlighted how Swarm B LP \(N_{\text{e}}\) observations are characterized by a nighttime overestimation under low solar activity conditions. To overcome this issue, the authors of [34] calibrated Swarm B LP \(N_{\text{e}}\) observations on the basis of corresponding values from the FP onboard the same satellite, which showed a much better agreement with ISRs observations. Additionally, original Swarm B LP \(N_{\text{e}}\) measured values (in cm\({}^{-3}\)) have been calibrated by applying Equation (1), where \(x\equiv\ \log_{10}(\text{Swarm B LP original }N_{\text{e}})\) and \(y\equiv\ \log_{10}(\text{Swarm B LP calibrated }N_{\text{e}})\), with calibration coefficients:  
\[\text{daytime}\Rightarrow\left\{\begin{array}{l}m=0.978\pm 0.009;\\ q=0.161\pm 0.044;\\ \end{array}\right. \tag{3}\] \[\text{nighttime}\Rightarrow\left\{\begin{array}{l}m=1.374\pm 0.017 ;\\ q=-1.254\pm 0.069.\end{array}\right.\]  
The application of the calibration procedure to Swarm B LP \(N_{\text{e}}\) values allows one to fix the nighttime overestimation for low solar activity conditions [34]. For daytime conditions,its application has only marginal effects, meaning that the agreement between LP and FP data is already good.