esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Low-Cost Control System for Fish Farm Facilities
## 2 Identification of Critical Factors for Control Systems for Aquaculture
### Factors Affecting Fish Wellbeing and Performance  
First of all, the general factors that affect the fish's well-being and performance are divided into five domains:  
* Production Domain: Factors derived from the way in which fish are produced in the facilities. Most of those factors are fixed and depend on the design and location of the production plant. Other factors are more flexible, such as feeding-related ones, and might be changed or improved.
* Biotic Domain: Factors dependent on the biotic part of the environment in which the facilities are located. All these factors are independent of the facilities and include water, atmosphere, and light factors. If facilities are located in open skies, barely any control or modification can be performed on these factors. Nonetheless, it is possible to modify these factors when facilities are indoors. More information is provided in the section titled Control Domain.
* Species Biotic Domain: Factors that depend on the grown species, including the growing stage and physiological and social factors. Even though most of them cannot be controlled or modified, some factors can be altered.  
* External Biotic Domain: Factors depending on the presence and effects of other living beings found in the surroundings of the aquaculture facilities. It includes the effects of animals, plants, and bacteria, among others, on the fish. In general terms, the presence of the effect of external biotic factors can be controlled, minimized, or avoided.
* Control Domain: Factors referring to the control actions that can be conducted on fish farms, including monitoring and modification of biotic, abiotic, and production domains. This is the core of aquaculture facilities' smart, low-cost control systems. In the Domain, only the aspects related to the control of other domains are listed since the rest of the factors do not affect fish performance or well-being. These factors, such as monitoring, telecommunication, or computing technology, are defined in subsequent sections.  
The factors included in the listed domains affect the fish grown; their effect can be measured in many different ways, such as growth, meat quality, survival, reproduction success, and welfare. Figure 1 represents the summary of the included Domains.
## 2 Materials and Methods
### Video Spectral Comparator (VSC) Studies  
The VSC studies were conducted using a VSC 6000 workstation (Foster & Freeman Ltd., Evesham, UK) featuring an advanced digital system suitable for comprehensive forensic examination of a variety of questioned documents. The sophisticated VSC 6000 workstation incorporates a digital imaging system, variable light sources, and multi-spectral illumination spanning different regions of ultraviolet, visible, and infrared light. Notably, the VSC 6000 includes a high-resolution micro-spectrometer, which facilitates the non-destructive spectral analysis helpful for studying distinct features of ink and paper compositions. The VSC 6000 system allows the acquisition of absorption, reflectance, fluorescence, and transmitted spectra from the documents under scrutiny. Such advanced optical and spectroscopic capabilities enable using the VSC 6000 for a wide range of document examinations, encompassing both ink and paper analysis. The VSC 6000 features spot luminescence examination capability, allowing images to be illuminated with visible light of different wavelengths, and then the luminescence is recorded in the infrared region. VSC 6000 features ten preset optical excitation filters for sample illumination, allowing varying the excitation waveband over different bandwidth ranges from 400-485 nm to 645-800 nm band-pass regions in addition to the wide band-pass filter in the 300-800 nm region. The VSC then can adjust the emission filters of the camera according to the excitation waveband, and this determines the range of the infrared emitted wavelengths that will be recorded. In our experiments, the ten preset filters of VSC 600 were used to vary the excitation waveband used for sample illumination. The different ink samples were examined using the same magnification with slight adjustments to the iris, integration, and exposure time to obtain optimal image clarity using different transmission filters.  
The handwritten ink samples were classified according to their luminescence behavior under the first spot luminescence 400-485 nm band-pass filter. Upon examination of a large set of selected samples, the examined samples were grouped into 4 distinct groups (groups I-IV). To gain a thorough understanding of the luminescent properties of the examined samples, the luminescence spectra of the handwritten ink samples were acquired along with those for the crystal violet dye standard and bands of other constituents separated using the TLC plates.
## 2 Materials and Methods
### Analytical Methods Used as Reponses in Experimental Design of Extraction Optimization (EO)
#### 2.2.3 Determination of Antioxidant Activity by Using FRAP Assay  
The FRAP assay was determined according to the procedure described by [PERSON] and [PERSON] [49] with some modifications. The working FRAP reagent was prepared by mixing 10 mM TPTZ (2,4,6-tris (1-pyridyl)-5-triazine) solution in 40 mM HCl with a 20 mM FeCl\({}_{3}\) solution and 0.3 M acetate buffer (pH 3.6) in a proportion 1:1:10 (\(v/v/v\)). A total of 50 \(\upmu\)L of the CS extract was mixed with 450 \(\upmu\)L of distilled water and 4.5 mL of the FRAP reagent. The obtained mixture was incubated at 37 \({}^{\circ}\)C for 30 min. After an incubation, the absorbance of the tested sample was measured at 593 nm. The distilled water was used as a blank control sample. The standard curve was constructed using an aqueous solution of ferrous sulfate (FeSO\({}_{4}\).7H\({}_{2}\)O) at concentrations of 100-1000 \(\upmu\)M. The FRAP results were expressed as \(\upmu\)mol of ferrous equivalent per gram of coffee silverskin dry matter (\(\upmu\)mol Fe(II)/g CS d.m.).
## 3 Hybrid Computing Architecture of MSD Algorithm
### Correlation Calculation Based on SC  
In the realm of SC, it is imperative to maintain probability values strictly within the range of 0 to 1 throughout the entire computation process. During multiplication operations, if the input data fall within the range of 0-1, the output will inherently lie within the same range.  
For adders employed in SC, it is necessary to scale the summation result to prevent overflow. Excessively small scaling factors may lead to the summation results exceeding  
Figure 4: Stochastic multiplier based on AND gate.  
the permissible range, while excessively large scaling factors result in small outcomes, thereby reducing the discriminability between different summation results. Therefore, it is necessary to design the adder with an appropriate scaling factor. Additionally, considering that the input number of the adder will vary with the observation window length in the MSD algorithm, the adder must also accommodate different numbers of input ports. Thus, high requirements are imposed on the scalability and flexibility of the SC-based adders.  
In summary, the entire correlation calculation based on SC requires three fundamental computational units: an inverter, a multiplier, and an adder with flexible scaling factors. The circuit structures of each basic module based on SC will be introduced individually in the following.
# Smart Contract-Based Right-of-Way Trade with Game Theory in Blockchain-Enabled Internet of Vehicles
## 4 Right-of-Way Trade Strategy
### Meaningful Circumstances for Right-of-Way Trade  
A and B are two neighbor-order convoys such as \(C_{(y)}\) and \(C_{(y+1)}\), respectively, in two conflicting road directions. First, some assumptions and definitions are made for the derivation.  
* \(\alpha_{A}\) and \(\alpha_{B}\) are the time cost factors for convoys A and B, respectively. The larger the parameter, the more sensitive it is to time.
* Exchanging the right-of-way helps convoy B ahead of schedule for \(\Delta t_{BA}\), while leading A's delay for \(\Delta t_{AB}\).
* The time cost represented as \(C\) is assumed to be linearly related to the time \(t\) and time cost factor, i.e., \(C=\alpha t\). The time cost profit reduction of B associated with its early passing is \(\Delta C_{B}\), and the increased time cost profit of A is denoted as \(\Delta C_{A}\).  
Figure 3: An IoTA and its smart contract including right-of-way trade for a newly arrived convoy to obtain a passing order in a crossroad.  
Secondly, we analyze and give a conclusion on meaningful circumstances suitable for A and B to make a right-of-way deal. Two conditions must be met before trading.  
* Condition 1: Early passing only makes sense if A and B have a time conflict in passing the crossroad, i.e., Equation (10) is satisfied. \[T_{(B),arrive}-T_{(A)}<\Delta T_{guard}.\] (10) Equation (10) is equal to \(\Delta C_{B}>0\).
* Condition 2: Based on satisfying Equation (10), if and only if \[F=\Delta C_{B}-\Delta C_{A}>0.\] (11) There is a Pareto optimization after swapping the passing order. Such an exchange makes sense.  
Transaction Price: However, the right-of-way is not free. Convoy B has to negotiate with A by gaming and finally reach a concession price \(S\). That is, B pays A for \(S\) traffic tokens in exchange for the right-of-way.  
This concession price is set so that both convoys A and B benefit from the concession and achieve a Pareto improvement.  
\[\Delta C_{A}<S<\Delta C_{B} \tag{12}\]
## 2 Background Overview
### Vehicular Ad Hoc Networks  
Given that vehicular networks are a subset of wireless networks, some challenges are common to other existing wireless networks, including limited transmission ranges, the limited number of communication channels, and interference. However, there are many other challenges that come from the unique characteristics of vehicular environments. Large and dynamic topologies, variable capacity wireless links, bandwidth and hard delay constraints, and short contact durations are some of the characteristics of these networks. These challenges are caused by the high mobility and high speed of vehicles, and frequent changes in density happening even in the same area. For example, the vehicle density is higher on main streets than on secondary ones and it changes sharply over time (the same streets are busier during peak hours as opposed to night hours or other parts of the day).  
In addition, as the number of smart vehicles and sensors incorporated in vehicles keeps increasing, a huge amount of data will be generated in VANETs. Conventional VANETs, which are based on vehicle-to-vehicle communication and depend only on self-contained resource capabilities, lack the needed resources for dealing with such massive amounts of data. Moreover, the lack of a centralized management entity makes it hard for such dynamic networks to have an equitable share of resources and therefore results in collision of transmitted packets and less efficient use of channel resources [20]. With the integration of cloud-fog-edge computing and the SDN approach within VANETs, prospective solutions in managing the aforementioned problems can be achieved. The main components of this architecture together with the content flow are illustrated in Figure 1.  
The vehicles are equipped with limited computing and storage resources and after collecting information from the data gathering module, the vehicles can perform real-time data analysis for non-complex data. In this way, the information is processed locally and there is no delay in case immediate action is needed. However, the limitations of edge computing make it necessary for VANETs to rely upon fog and cloud computing. Fog computing in VANETs is the extension of the cloud paradigm brought in proximity of the vehicles, which offers predictable latency and seamless resource management by supporting mobility and geo-distribution [33].  
The computation of complex data, which are beyond the capability of edge and fog layer and are not time-sensitive, can be sent to the cloud layer for further advanced analysis that takes a long time and requires abundant storage. They are also used as a repository for control policies, software updates, and so on. The integration of SDN in VANETs handles the issues of such large-scale, heterogeneous, and dynamic networks by providing a robust mechanism for data traffic control and resource management of all the components of this novel architecture of VANETs. The implementation of cloud-fog-edge and SDN in VANETs will enhance VANETs services and will pave the way for future applications.
## 3 Proposed Method
### Encoder for Extracting Features  
#### 3.1.1 Residual Learning  
As shown in Figure 4, the EAR-Net is an encoder-decoder structure. The EAR-Net adopts ResNet with residual learning applied from the encoder to the backbone network. Residual learning prevents the feature loss that occurs during feature extraction and compression of continuous convolutions. Further, it prevents the vanishing gradient problem, which tends to occur as the network becomes deeper. There are several types of ResNets, such as ResNet-18, ResNet-50, and ResNet-101. In ResNet-n, n is the number of layers in the network, and as n increases, the computation cost increases and the performance improves. In this study, we adopted ResNet-50, considering the trade-off between the segmentation performance and speed.  
In Figure 4, the backbone network was divided into four blocks (encoding block1, encoding block2, encoding block3, and encoding block4), according to the resolution of the feature map. The blocks have \(\frac{1}{2}\), \(\frac{1}{4}\), \(\frac{1}{8}\), \(\frac{1}{16}\) resolution of the input image, respectively. The structure of each encoding block is the same, and Equation (2) and Figure 5 show the encoding block.  
\[H(x)=F(x)+x \tag{2}\]  
A typical CNN proceeds with stem blocks before proceeding with encoding blocks. A stem block reduces the resolution of the input image and extracts features, such as contours, to reduce the amount of computation of the subsequent convolution. A conventional ResNet reduces the resolution of the feature map to \(\frac{1}{4}\) by performing a \(7\times 7\) traditional convolution with stride 2 and max-pooling. In addition, several semantic segmentation methods perform this down-sampling process while using the ResNet as a backbone.  
Figure 4: Efficient atrous residual network (EAR-Net) architecture. DSConv and ASPP denote depthwise separable convolution and atrous spatial pyramid pooling.  
Figure 5: Encoding block architecture. Conv, BN and ReLU denote traditional convolution, batch normalization and rectified linear unit.  
However, this process causes loss of the spatial information included in the feature map. Therefore, the EAR-Net reduces the resolution of the feature map by half by removing max-pooling. Moreover, a \(7\times 7\) traditional convolution requires much computation due to the large kernel. Therefore, the proposed method replaces the \(7\times 7\) traditional convolution with two \(3\times 3\) traditional convolutions. Unless noted otherwise, batch normalization and rectified linear unit (ReLU) layers are included after the convolution layer.
# Design of Multi-Chain Traceability Model for Pepper Products Based on Traceability Code
## 4 Construction of Multi-Chain Traceability Model for Pepper Products
### Smart Contract Design under Multi-Chain Traceability Modeling
#### 4.4.1 Uplinked Smart Contract  
Data uplinking is realized through the uplinking contract, and the uplinking smart contract mainly has the following functions: 1. Judging the heavy metal content in the traceability information of pepper products to be uplinked by using the smart contract, and if it does not meet the national standard, the traceability information will fail to be uplinked, and the system will return the reasons for the failure of the uplinking, limiting value data from \"GB/T 30391-2013 Peppercorn Standard\" [24]. 2. Judging whether the traceability data are private traceability data; if they are privately traceable, then the data  
Figure 3: Tracing data query process for multi-chain models.  
are entered into an encryption module, and the public traceability data will be directly uploaded to the chain after they meet the conditions of the smart contract execution. 3. Judging data encryption by encrypting private traceability data through the RSA algorithm to ensure the data security of the private data. Taking the processing of traceability data as an example, the pseudo-code of the uplinking contract is shown in Algorithm 1.  
```
Input: Pepper for total arsenic X, lead Y, cadmium Z, total mercury G Output: successful transaction hash address TxID, failure to return error reason 1: message = Stub. GetState(TraceabilityCode) //Checking for TraceabilityCode records in the sidechain 2: when (ID = 1)//At this time the product is fresh or frozen peppercorns 3: if (X > 0.07 or Y > 0.42 or Z > 0.11 or G > 0.01) 4: return \"Excessive heavy metals\"//fail to upload 5: when (ID = 2)//At this time the product is dried peppercorns or powdered peppercorns 6: if (X > 0.30 or Y > 1.86 or Z > 0.50 or G > 0.01) 7: return \"Excessive heavy metals\"//fail to upload 8: message.encryption()://Calling the encryption module to encrypt 9: Stub.PutState (message) //uplink ciphertext data 10: return TxID//Upload successfully, return transaction hash address
```  
**Algorithm 1** Up chain Smart contract pseudo code.
## 3 Proposed Algorithm
### Extending Lifetime and QSSM-ML Algorithm  
In this ELQSSM-ML algorithm, the network lifetime is extended by minimizing the transfer power. This is accomplished by adjusting the transfer energy as follows:  
\[E=\min\left\{E_{S}-E_{t}^{S}T,E_{r_{1}}-E_{t}^{r_{1}}T,\ldots,E_{r_{n}}-E_{t}^ {r_{N}}T\right\} \tag{1}\]  
In Equation (1), \(E\) refers to the node's least remaining power, which would remain after probably contributing in collaboration, \(E_{S}\) refers to the origin's present remaining power, \(E_{t}^{S}\) refers to the predicted transfer of energy at the origin, \(\left\{E_{t}^{t},\ldots,E_{t}^{r_{N}}\right\}\) denote the predicted transfer energy subgroup of the amount of contributing relay nodes for minimizing the power used at transfer time \(t\), \(E_{r_{n}}\) denotes the present power of individual nodes, and \(T\) refers to the predicted overall transfer period. The predicted power used \(\left(E_{S}^{D}\right)\) between origin \(\left(S\right)\) and target \(\left(D\right)\) for direct transfer is represented by  
\[E_{S}^{D}=\left(E_{t-max}+E_{rp}+E_{c}\right)T_{Con}+\left(E_{t}^{S}+E_{rp}+E_ {c}\right)T_{data\_S,D} \tag{2}\]  
In Equation (2), \(E_{t-max}\), \(E_{rp}\) and \(E_{c}\) are the highest transfer energy for control frames at 1 Mbps, the reception energy and the processing energy, correspondingly, \(T_{Con}\) is the time required to transfer all control frames and \(T_{data\_S,D}\) is transfer time between source and destination. Moreover, the predicted power used \(\left(E_{r_{n}}^{C}\right)\) if the relay node collaborates is provided as follows:  
\[E_{r_{n}}^{C}=\left(E_{t-max}+E_{rp}+E_{c}\right)\left(T_{Con}+T_{stt}\right) \ldots+\left(E_{t}^{S}+E_{rp}+E_{c}\right)T_{data\_S,r_{n}}\ldots+\left(E_{t} ^{r_{n}}+E_{rp}+E_{c}\right)T_{data\_S,D} \tag{3}\]  
\[T_{Con}=T_{RTS}+T_{CTS}+T_{ACK}\text{ and }T_{i,j}=\frac{8(L+L_{H})}{R_{i,j}} \tag{4}\]  
In Equations (3) and (4), \(T_{stt}\), \(T_{RTS}\), \(T_{CTS}\), and \(T_{ACK}\) are the time required to transfer the Supporter-Ready-to-Transmit (SRT), \(RTS\), \(CTS\), and \(ACK\) frames, correspondingly, \(T_{data\_S,r_{n}}\), and \(T_{data\_S,r_{n}}\),\(D\) are the time needed to transfer the data between origin and relay, as well as relay and target, accordingly, \(R_{i,j}\) is the transfer rate from the path \(i\) to \(j\),\(L_{H}\) refers to the header data in bytes, and \(L\) is the total number of bytes to be forwarded by the origin. Additionally, the power use of the direct transfer is evaluated with that of collaborative transfer prior to the relay node is served as a supporter node. Evaluating (3) and (4), obtain \(E_{S}^{D}-E_{r_{n}}^{C}\
eq 0\), which should be a non-zero range for realizing the power gain. It defines the power used by any of the collaborator nodes, which should be lower when compared to a direct transfer owing to the distance between the origin and its corresponding target.
## 2 Technical Backgrounds
### Security Considerations and Requirements  
Authentication mechanisms used in 5G environments, especially secondary authentication, must satisfy additional considerations in addition to those of the general authentication protocol. This section addresses four security considerations that should be considered by the secondary authentication in the 5G environment: Availability, Flexibility, Auditability and Independence of Authentication Authority.
# A Dynamic Service Reconfiguration Method for Satellite-Terrestrial Integrated Networks
###### Abstract  
Satellite-terrestrial integrated networks (STINs) are regarded as a promising solution to meeting the demands of global high-speed seamless network access in the future. Software-defined networking and network function virtualization (SDN/NFV) are two complementary technologies that can be used to ensure that the heterogeneous resources in STINs can be easily managed and deployed. Considering the dual mobility of satellites and ubiquitous users, along with the dynamic requirements of user requests and network resource states, it is challenging to maintain service continuity and high QoE performance in STINs. Thus, we investigate the service migration and reconfiguration scheme, which are of great significance to the guarantee of continuous service provisioning. Specifically, this paper proposes a dynamic service reconfiguration method that can support flexible service configurations on integrated networks, including LEO satellites and ground nodes. We first model the migration cost as an extra delay incurred by service migration and reconfiguration and then formulate the selection processes of the location and migration paths of virtual network functions (VNFs) as an integer linear programming (ILP) optimization problem. Then, we propose a fuzzy logic and quantum genetic algorithm (FQGA) to obtain an approximate optimal solution that can accelerate the solving process efficiently with the benefits of the high-performance computing capacity of QGA. The simulation results validate the effectiveness and improved performance of the scheme proposed in this paper.  
satellite-terrestrial integrated networks; quantum genetic algorithm; service migration; SDN/NFV +
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