esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantifying Uncertainties in OC-SMART Ocean Color Retrievals: A Bayesian Inversion Algorithm
## 2 Oc-Smart
### Neural Network Training  
It has been demonstrated that multilayer, feed-forward neural networks with one or more hidden layers and a non-linear activation function can approximate non-linear functions [36; 37]. This capability is suitable for solving our inverse problem of deriving the water-leaving radiance \(L_{w}\) or the remote sensing reflectance (\(R_{\rm rs}\)) from the Rayleigh-corrected TOA radiance \(L_{rc}\). To make the MLNN training dataset representative of a variety of atmospheric and ocean conditions, a statistical study was performed on the 8-day averaged 4 km spatial resolution of global MODIS Aqua L3 data from 2011 to 2015. Radiative transfer simulations based on 100,000 combinations of parameter ranges consistent with available observational data [14] allowed us to generate a synthetic dataset of Rayleigh-corrected radiances and corresponding remote sensing reflectances. This synthetic dataset was then divided into two independent groups, a training group (90,000 data points) and a validation group (10,000 data points). The training group was used to optimize the weights and biases of the neural network and the validation group was used to validate the neural network after the training was finished.
# An Ordered Universal Accumulator Based on a Hash Chain
## 3 Hash Chain Accumulator Scheme
### Scheme Properties
#### 3.2.4 Anonymous Identity  
This anonymous identity scheme, leveraging a hash chain accumulator, offers a departure from traditional PKI certificate authentication by decentralizing identity verification. Initially, a device seeking an anonymous identity within the network submits its public key (pk). Subsequently, a decentralized consensus protocol, like a blockchain, aggregates all valid public keys into a set X. To initiate the identity scheme, an accumulator is initialized with a predefined value, \(\text{Acc}_{0}\). The core of the scheme lies in the sequential processing of the key set X by the hash chain accumulator. For each public key (pk\({}_{i}\)), the accumulator value (Acc) is iteratively updated through a hash function (H), where \(\text{Acc}_{i}=\text{H}(\text{Acc}_{i-1}\mid\mid\text{pk}_{i})\), creating a chain of cryptographic hashes. Upon successful inclusion of its public key, each device \(\text{D}_{i}\) receives a membership witness \(\text{w}_{i}\), composed of the accumulator value immediately preceding its key's inclusion, \(\text{Acc}_{i-1}\), and the updated accumulator value \(\text{Acc}_{i}\). This witness serves as proof of membership within the anonymous identity system.
## 2 Fully Coupled Traffic-Bridge-Wind (TBW) Simulation
### Modelling of Road Vehicles  
By pertaining all the relevant information, the vehicles can be simplified as several rigid bodies, suspension systems, and tires connected by a series of springs and dampers [22; 23]. The vehicle bodies and the tires are simulated as rigid bodies, whereas the elasticity and dissipation capacities of both the suspension system and the tires are idealized as springs and dampers, respectively. Taking a 3-axle six-wheel road vehicle, shown in Figure 1, as an example, the vehicle was simplified as a combination of 8 rigid bodies, 3 wheel-axle sets, and 24 sets of springs and dampers in vertical and lateral directions. In total, 19 degree-of-freedoms (DOFs) are assigned for the 3-axle vehicle, including 8 independent vertical, 8 lateral, and 3 rotational DOFs. The corresponding response vector of the 3-axle vehicle is given by the following:  
\[\mathbf{d}_{v}= \left\{\begin{array}{l}Z_{vr}^{1},\ Y_{vr}^{1},\ \theta_{vr}^{1},\ \theta_{vr}^{1},\ Z_{vr}^{2},\ Z_{vr}^{2},\ \beta_{vr}^{2},\ Z_{ul}^{1},\ Z_{ul}^{2},\ Z_{ul}^{3},\\ [PERSON]_{ul}^{1},\ [PERSON]_{ul}^{2},\ [PERSON]_{ul}^{2},\ [PERSON]_{ul}^{3},\ [PERSON]_{ul}^{1},\ [PERSON]_{ul}^{2},\ [PERSON]_{ul}^{3},\ [PERSON]_{ul}^{1},\ [PERSON]_{ul}^{3},\ [PERSON]_{ul}^{2},\ [PERSON]_{ul}^{3},\ [PERSON]_{ul}^{3},\ [PERSON]_{ul}^{3},\end{array}\right\}, \tag{1}\]where \(Z^{i}_{vr}\) and \(Y^{i}_{vr}\) = vehicle vertical and lateral displacements of \(i\)th (\(i\) = 1, 2) rigid body; \(\theta^{i}_{vr}\) and \(\theta^{i}_{vr}\) = rotational displacements of \(i\)th (\(i\) = 1, 2) rigid body in the \(X\)-\(Z\) plane (pitching) and the \(Y\)-\(Z\) plane (rolling), respectively; \(Z^{j}_{aL(K)}\) = vertical displacement of \(j\)th (\(j\) = 1, 2, 3) wheel axle on the left (right) side; \(Y^{j}_{aL(K)}\) = lateral displacement of \(j\)th (\(j\) = 1, 2, 3) wheel axle on the left (right) side. Each DOF in the vector is independent, as shown in Figure 1a,b. The \(\theta^{2}_{vr}\) is dependent on other DOFs, which is defined as \(\theta^{2}_{vr}=\left(Z^{2}_{vr}-Z^{1}_{vr}-L_{5}\,\theta^{1}_{vr}\right)/L_{6}\).
# A Microservices-Based Control Plane for Time-Sensitive Networking
## 3 \(\mu\)TSN-CP Architecture
### Topology Discovery Microservice  
The Topology Discovery microservice is in charge of collecting the topology of the TSN network and providing it to the other microservices represented as a network matrix. For this task, this microservice uses LLDP [20], a data-link layer protocol used for advertising the network elements' identity, capabilities, and adjacency. In a nutshell, the Topology Discovery microservice generates a list of all the available devices in the network by accessing them through SSH and retrieving their LLDP information. We combined bash scripts and Python3 to create this microservice, specifically using the Paramiko [21] library, which implements the SSHv2 protocol. Figure 4 shows the general architecture of this microservice and its parts.  
Figure 4: Overview of the Topology Discovery microservice.  
Figure 3: Overview of the Jetconf microservice.  
What follows is a summary of the steps followed from the moment we start the communication to the moment the information is retrieved from the devices and parsed to the Preprocessing microservice:  
1. The Topology Generator module activates the retrieval of the topology information using the Paramiko module. It uses Paramiko to send commands to every device in the network to get the adjacency information of each device. Raw data is collected and stored as files in the microservice.
2. The Topology Generator module gets all the files, cleans the information and translates it into a Network Topology Matrix.
3. Once the network information is ready, the Topology Discovery microservice generates a JSON message in the RabbitMQ queue and sends it to the Preprocessing microservice.
## 3 Materials and Methods  
### Reagents  
Benzylpiperazine (**1**) was purchased from Fluka and used as received. Azobisisobutyronitrile (AIBN) was obtained from DuPont and was recrystallised from acetone prior to use. Acrylic acid (**6**), methacrylic acid (**7**), itaconic acid (**12**), divinylbenzene (DVB), ethylene glycol dimethacrylate (EGDMA), trimethylolpropane trimethacrylate (TRIM), sodium hydroxide, glacial acetic acid, hydrochloric acid, potassium hydroxide, (1\(R\), 25)-(-)-ephedrine (**19**), 1-phenylpiperazine (**20**), p-acetoxystrene (**21**), thiophosgene and 7-hydroxy-4-methylcoumarin were purchased from Sigma-Aldrich and used as received, unless otherwise stated. Monomers **6** and **7** and the crosslinkers DVB and EGDMA were distilled under reduced pressure prior to use. TRIM was purified by washing with aqueous sodium hydroxide (0.1 M, 2 \(\times\) 50 mL), water (50 mL) and saturated brine solution (50 mL) and then dried over MgSO\({}_{4}\). _N,O_-bismethacryloyl ethanolamine (NOBE, **14**), 7-hydroxy-4-methylcoumarin acrylate (**15**) and benzylpiperazine (4-vinylphenyl) carbamate (**16**) were synthesized in-house, according to the synthetic procedures outlined below. Cocaine base (**17**) and morphine (**18**) was provided by the Australian Federal Police Forensic Services and was used as received. Methacryloyl chloride and acryloyl chloride were purchased from Fluka and Lancaster, respectively, and used as received.  
HPLC-grade acetonitrile and chloroform were obtained from Merck and were used as received. All other solvents were distilled prior to use, unless otherwise stated.  
Deuterated chloroform (99.8%) and dimethylsulfoxide (99.8%) for NMR analysis were obtained from Cambridge Isotope Laboratories Incorporated.
## 2 Data and Methods
### NDVI\({}_{max}\) and GPP\({}_{max}\) Extraction Methods  
For the NDVI and GPP data, taking the NDVI as an example, we extracted the maximum value for each pixel from all NDVI data over the course of a year, which is designated as NDVI\({}_{\max}\). In this way, each pixel value in the composite image represents the maximum NDVI value for that location across all images throughout the year.
## 4 Database Used  
In the current research, we utilized data (comprising 226 entries) sourced from the study by [61], which span various soil categories such as fat clay (CH), silty clay (CL-ML), lean clay (CL), clay gravel (GC), silty gravel (GM), elastic silt (MH), well-graded sand with clay (SW-SC), poorly graded sand with clay (SP-SC), silt (ML), clayey sand (SC), silty sand (SM), poorly graded gravel with clay (GP-GC), and well-graded gravel with clay (GW-GC). The physical attributes of these soil samples are presented in Table 1. This dataset encompasses a diverse array of soils, highlighted by the extensive variety of index properties. Within this dataset, it is posited that the compaction metrics are influenced by factors like liquid limit (LL), plastic limit (PL), compaction energy (E), sand content (C\({}_{\text{S}}\)), fines content (C\({}_{\text{F}}\)), gravel content (C\({}_{\text{G}}\)), optimum water content \(w_{opt}\), and maximum dry density \(\rho_{\text{dmax}}\), all of which are detailed in Table 1. Figure 4 presents frequency histograms for each variable, inclusive of soil classification. Analyzing Figure 4 reveals that within this database, a significant portion of soils possess gravel and sand contents ranging from 0% to 20% (as seen in Figure 4a,b). Conversely, the content of fines in the soils predominantly falls within 80% to 100% (illustrated in Figure 4c). A large portion of soils register a liquid limit between 0 and 50 (as displayed in Figure 4g), and 32 soils exhibit a liquid limit exceeding 300, indicative of high plasticity. The plastic limit is chiefly found between 10 and 30 (Figure 4d). Notably, this dataset features a comparably larger quantity of clayey sand, fat clay, lean clay, and clayey gravel in contrast to other soil types (depicted in Figure 4f). A significant number of compaction tests were executed under standard Proctor conditions or with diminished compaction energy (not surpassing 600 kJ/m\({}^{3}\) as shown in Figure 4e). Around 30 modified Proctor compaction tests are also part of the study. In terms of maximum dry density and optimum water content, peak frequencies are predominantly within 1.6 Mg/m\({}^{3}\) to 1.8 Mg/m\({}^{3}\) and 10% to 20%, respectively (as captured in Figure 4h,i).
# Low-Light Image Enhancement Method Based on Retinex Theory by Improving Illumination Map
## 4 Experiments and Results
### Subjective Evaluation of Experimental Results
#### 4.1.1 Testing the Effectiveness of the Proposed Method in Dark Regions  
The overall pixel values of shimmering images were small, and there were often completely dark areas in the images, such as the bridge in Figure 10, the wooden stakes in the corner of the house in Figure 11, the eave in Figure 12, and so on. As shown in Figures 10-15, all methods improved the brightness of the image to a certain extent, but the red box areas of the image were enhanced differently.  
For instances, for the red frame region in Figure 11, some methods (i.e., MF, LIME, FFM, and LECARM) had less visible enhancement, while some methods (i.e., BIMEF, SR, and SDD) had little or no effect. The dark region enhanced by Ret-Net could be clearly seen, but its details were also blurred. Our proposed method had a significant effect for recognition of the wooden stake. This was because addition of GC to the BEF function improved the enhancement of dark areas. Similarly for Figure 15, for the roof in the red frame, some methods (e.g., BIMEF, SR, Ret-Net, FFM, SDD) blurred details, whereas LIME and LECARM led to different degrees of over-enhancement. The enhancement effects of MF and the proposed method were obviously better than those of the other algorithms. However, in the area of the square box, the proposed method showed relatively clear content.
# Enhanced Fully Generalized Spatial Modulation for the Internet of Underwater Things
###### Abstract  
A full design of the Internet of Underwater Things (IoUT) with a high data rate is one of the greatest underwater communication difficulties due to the unavailability of a sustainable power source for the battery supplies of sensor nodes, electromagnetic spread weakness, and limited acoustic waves channel bandwidth. This paper presents a new energy-efficient communication scheme named Enhanced Fully Generalized Spatial Modulation (EFGSM) for the underwater acoustic channel, where the different number of active antennas used in Fully Generalized Spatial Modulation (FGSM) is combined with multiple signal constellations. The proposed EFGSM enhances energy efficiency over conventional schemes such as spatial modulation, generalized spatial modulation, and FGSM. In order to increase energy and spectral performance, the proposed technique conveys data bits not just by the number of active antenna's index as in the existing traditional FGSM, but also using the type of signal constellation to increase the data bit rate and improve power saving without increasing the receiver's complexity. The proposed EFGSM uses primary and secondary constellations as indexes to carry information, they are derived from others by geometric interpolation signal space. The performance of the suggested EFGSM is estimated and demonstrated through Monte Carlo simulation over an underwater acoustic channel. The simulation results confirm the advantage of the suggested EFGSM scheme not just regarding energy and spectral efficiency but also concerning the average bit error rate (ABER).  
IoUT; spectral efficiency; energy efficiency; spatial modulation; FGSM +
Footnote †: journal: Sensors
# New Satellite Selection Approach for GPS/BDS/GLONASS Kinematic Precise Point Positioning
###### Abstract  
With the development of global satellite navigation systems, kinematic Precise Point Positioning (PPP) is facing the increasing computational load of instantaneous (single-epoch) processing due to more and more visible satellites. At this time, the satellite selection algorithm that can effectively reduce the computational complexity causes us to consider its application in GPS/BDS/GLONASS kinematic PPP. Considering the characteristics of different systems and satellite selection algorithms, we proposed a new satellite selection approach (NSS model) which includes three different satellite selection algorithms (maximum volume algorithm, fast-rotating partition satellite selection algorithm, and elevation partition satellite selection algorithm). Additionally, the inheritance of ambiguity was also proposed to solve the situation of constantly re-estimated integer ambiguity when the satellite selection algorithm is used in PPP. The results show that the NSS model had a centimeter-level positioning accuracy when the original PPP and optimal dilution of precision (DOP) algorithm solution were compared in kinematic PPP based on the data at five multi-GNSS Experiment (MGEX) stations. It can also reduce a huge amount of computation at the same time. Thus, the application of the NSS model is an effective method to reduce the computational complexity and guarantee the final positioning accuracy in GPS/BDS/GLONASS kinematic PPP.  
GPS/BDS/GLONASS kinematic Precise Point Positioning; satellite selection; GDOP
## 3 Off-Design Model of SASCHCS
### Modeling of Compression Subsystem  
The compression subsystem is described by the lumped parameter method. Its condenser and evaporator are modeled by mass and energy conservations:  
\[\sum m_{he,cs,i,if}=\sum m_{he,cs,o,if} \tag{11}\]  
\[Q_{he,cs}=\sum m_{he,cs,i,if}h_{he,cs,i,if}-\sum m_{he,cs,o,if}h_{he,cs,o,if} \tag{12}\]  
\[Q_{he,cs}=m_{he,cs,ef}c_{p,he,cs,ef}\Delta T_{he,cs,ef} \tag{13}\]  
\[Q_{he,cs}=(UA)_{he,cs}LMTD_{he,cs} \tag{14}\]  
The energy balance in the subcooler is controlled as follows:  
\[m_{11}(h_{11}-h_{12})=m_{3}(h_{4}-h_{3})=(UA)_{sub,cs}LMTD_{sub,cs}=Q_{e,as} \tag{15}\]  
Here, the subcooling power equals the cooling capacity of the absorption subsystem and is controlled by the absorption subsystem model.  
The flow rate of the refrigerant in the compression subsystem is as follows:  
\[m_{cs,if}=m_{11}=\eta_{v}\rho_{suc}V_{sync}n \tag{16}\]  
The expression of the compressor work is derived as follows:  
\[W=m_{cs,if}(h_{dis,s}-h_{suc})/\eta_{s} \tag{17}\]  
The dependence of volumetric efficiency and isentropic efficiency on the compressor speed is considered using the following formula [25]:  
\[\eta_{v}=\eta_{v,d}\Bigg{[}d_{1}+d_{2}\bigg{(}\frac{n}{n_{d}}\bigg{)}+d_{3} \bigg{(}\frac{n}{n_{d}}\bigg{)}^{2}\Bigg{]} \tag{18}\]\[\eta_{s}=\eta_{s,d}\Bigg{[}e_{1}+e_{2}\bigg{(}\frac{n}{n_{d}}\bigg{)}+e_{3}\bigg{(} \frac{n}{n_{d}}\bigg{)}^{2}\Bigg{]} \tag{19}\]  
Here, \(d_{1}\), \(d_{2}\), \(d_{3}\), \(e_{1}\), \(e_{2}\), and \(e_{3}\) for the scroll compressor are 0.693, 0.543, \(-\)0.236, 1.599, \(-\)1.06, and 0.461, respectively. Further, the volumetric efficiency and isentropic efficiency in the design condition are 0.9369 and 0.6996, respectively, from the operational data offered by the manufacturer.  
In terms of the definition, the COP of the compression subsystem is evaluated:  
\[COP_{cs}=Q_{e,cs}/W \tag{20}\]  
It should be noticed that the above-mentioned COP is also the COP of the hybrid system in respect that the subcooling power produced by the absorption chiller is considered.
# Replicas Strategy and Cache Optimization of Video Surveillance Systems Based on Cloud Storage
## 4 Dynamic Redundant Replicas Mechanism Based on Security Levels
As \(T_{x}\) plays a negative role in the retention time increasing, so \(\partial_{3}<0\). Using the linear relationship can directly reflect the impact weights of the influencing factors under different scenarios. \[\left\{ \begin{array}{l}S_{x}=\lceil\partial_{1}S_{x-init}+\partial_{2}H_{x} +\partial_{3}T_{x}\rceil\\ \partial_{1}>0,\;\partial_{2}>0,\;\partial_{3}<0\end{array} \right. \tag{4}\]  
Assume there are w video streams writing data into the storage system. Each stream will write block files, file collection is set as \(rm-x=\{x_{1},x_{2},\ldots,x_{w}\}\), \(I_{x}\) for each stream is \(rm-I_{x}=\{I_{1},I_{2},\ldots,I_{w}\}\). Though the intelligent video analysis results, we can get \(rm-S_{x-init}=\{S_{1-init},S_{2-init},\ldots,S_{w-init}\}\) and determine the number of initial redundant replicas. Then with the changes in retention time and access hit, redefine the number of redundant replicas, set \(NRR=\{n_{1},n_{2},\ldots,n_{w}\}\). The security levels algorithm is proposed as Algorithm 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Level** & **Security Definition** \\ \hline
1 & 0 & lower \\
2 & 1 & normal \\
3 & 2 & important \\ \hline \hline \end{tabular}
\end{table} 
Table 3: User-specify security levels definition. ```
1 Information:\(rm-x=\{x_{1},x_{2},\ldots,x_{w}\}\)
2 Input:\(rm-S_{x-init}=\{S_{1-init},S_{2-init},\ldots,S_{w-init}\}\)
3 Output: number of redundant replicas
4 foreach \(x_{i}\) in \(rm-x\) do
5 get retention time from storage module
6 calculate \(T_{x}\) by Formula (3)
7 get number of accesses from storage module
8 calculate \(H_{x}\) by Formula (2)
9 get \(S_{x-init}\) from \(rm-S_{x-init}=\{S_{1-init},S_{2-init},\ldots,S_{w-init}\}\)
10 calculate \(S_{x}\) by Formula (4)
11 compare \(S_{x}\) with Table 2
12 put the number of redundant replicas into \(NRR=\{n_{1},n_{2},\ldots,n_{w}\}\). 13 endfor
14 return\(NRR=\{n_{1},n_{2},\ldots,n_{w}\}\). ```  
**Algorithm 1** Security levels algorithm.
# An Energy Efficient MAC Protocol for Wireless Passive Sensor Networks
## 4 Experiments and Analysis
### Network Model and Parameters Configuring  
#### 4.1.1 Network Model Selection  
A network model is set up before evaluation. The network model includes the number of nodes, the communication range of each node, the size of data packets, and other ingredients. In order to choose a suitable network model, indicators named energy rate and throughput are introduced. Energy rate refers to the energy consumption of transmitting one bit data of all nodes. Throughput is the total amounts of data packets received by nodes in unit time. Time interval refers to the transmission interval of per data packet. For example, the time interval is 1 s means that when accomplishing sending a data packet, the node will send the next data packet after 1 s. When the time interval is 1 s, the energy rate and throughput of different numbers of nodes are compared. The time interval is chosen as 1 s because at this time the channel competition is very intense and the superiority of the IS-MAC protocol is verified strongly. Figure 2 shows the curves of energy rate and throughput. The horizontal ordinate represents the number of nodes.  
It can be concluded from (a) that with the increase of nodes' numbers, the gap of energy rate between IS-MAC protocol, and S-MAC protocol become larger and larger. This means that the network saves more and more energy and the IS-MAC protocol performs better than the S-MAC protocol when the number of nodes is large. In (b), the throughput of both protocols declines. With the increase of the number of nodes, channel competition will become more intense, and data collision will happen more frequently. The throughput of nodes will reduce. The throughput of the IS-MAC protocol declines relatively gentle and the throughput of the S-MAC protocol declines rapidly. This means that the IS-MAC protocol performs better than the S-MAC protocol, especially when the number of nodes is large. In conclusion, the IS-MAC protocol performs better than the S-MAC protocol when the number of nodes is large.  
Figure 1: Flow chart for the improved backoff algorithm.  
Limited to the complexity of large number of nodes, the network model is set up with five nodes. If a conclusion that IS-MAC protocol is better than S-MAC protocol can be drawn when the number of nodes is small, it is obvious that the conclusion will remain when the number of nodes is large.