esa-sceva/satcom-chunk-collection huggingface dataset:
# QoSComm: A Data Flow Allocation Strategy among SDN-Based Data Centers for IoT Big Data Analytics
## 6 Conclusions and Future Work  
The IoT decision-making approach gets benefits when using Big Data Analytics through the intelligent processing of massive data collected from IoT devices and an analysis of the actionable data in the shortest possible time in order to make quick decisions.  
Our proposal, QoSComm, provides network QoS for IoT Big Data Analytics, specifically in their job submission process without the need to modify the application and network controller schedulers. QoSComm improves BDA completion time when transferring data flows among SDN-based distributed data centers, as was shown in experiment results described in the previous section.  
Evaluations results showed that, for time-sensitive applications, it is necessary to consider other network parameters to provide QoS such as delay, jitter, and packet loss rather than just considering higher bandwidth to minimize their completion time. QoSComm allows BDA to program software-defined networks dynamically according to their needs and can also communicate with multiple SDN controllers, providing an inter-domain communication approach.  
As future work, the design of a REST API is proposed for a data flow distribution model enabling the BDA application scheduler to communicate its additional requirements for job execution (security, computing resources, among others). In addition, we will work on a combination of QoS based network paths and secure a forwarding scheme for sensitive-data IoT Big Data Analytics.  
All the authors were involved in research design and conceptualization; Writing--original draft, J.E.L.-R., J.I.N.-H., and R.R.-R.; Methodology, M.V.-B.; Formal analysis, J.C.C.-E.; Writing--review and editing, M.A.C.-L., J.I.N.-H., and R.R.-R. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Centro de Investigacion Cientifica y de Educacion Superior de Ensenada, Baja California, Mexico (CICESE) and the Universidad Autonoma de Baja California, Campus Ensenada, Mexico (UABC).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} IoT & Internet of Things \\ BDA & Big Data Analytics \\ SDN & Software Defined Network \\ QoS & Quality of Service \\ HPC & High Performance Computing \\ MPI & Message Passing Interface \\ \end{tabular}
# Research on Optimization of Multi-Objective Regional Public Transportation Scheduling
## 3 The Regional Public Transportation Scheduling Model
### Notations  
1. Parameters \(L=\{1,2,\ldots,N\}\) is the bus line, \(N\) is the number of lines; \(s\) is the station set, \(s_{i,q}\) represents station \(i\) of line \(q\), and \(m_{i}\) is the number of stations on line \(i\); \(S_{t}=\left\{(s_{i,q},s_{i,p})|s_{i,q},s_{i,p}\in S,i,j\in L\right\}\) is the set of pairs for transfer stations; \(x_{i}\in X\) implies the bus \(x\) uses line \(i\), where \(X\) is the bus set; \(V\) is the passenger capacity of the bus; \(p_{x,s_{i,q}}^{a},p_{x,s_{i,q}}^{b}\) denote the arrival number and alighting rate of passengers on the bus \(x_{i}\) at station \(s_{i,q}\), respectively; \(f_{s_{i,q}s_{i,p}}\) is the proportion of passenger transfer from line \(i\) to \(j\) at transfer-station \(s_{i,q},s_{j,p}\); \(Q_{x,s_{i,q}}^{a}\) and \(Q_{x,s_{i,q}}^{b}\) indicate the number of passengers getting on and off the bus \(x_{i}\) at station \(s_{i,q}\) of line \(i\); \(F_{s_{i,q}s_{i,q}}\) is the number of passengers who failed to get on the bus \(x_{i}\) of line \(i\) at station \(s_{i,q}\); \(C_{x,s_{i,q}}\) indicates the number of passengers on the bus \(x_{i}\) of line \(i\) at station \(s_{i,q}\); \(t_{s_{i,q}-1},s_{i,q}\) denotes the travel time from station \(s_{i,q-1}\) to station \(s_{i,q}\), when \(q=1\), \(t_{s_{i,q-1}s_{i,q}}\) represents the time from the parking lot to the departure station; \(U_{x,s_{i,q}}\) indicates the dwelling time of bus \(x_{i}\) of line \(i\) at station \(s_{i,q}\); \(\delta_{1}\) and \(\delta_{2}\) indicate the time window for easy transfer; \(t_{x,s_{i,q}}\) indicates the time when the bus \(x_{i}\) of line \(i\) arrives at station \(s_{i,q}\); \(l_{x,s_{i,q}}\) denotes the time when the bus \(x_{i}\) of line \(i\) leaves station \(s_{i,q}\); \(T_{1}\) and \(T_{2}\) are the scheduling simulation time window; \(I_{\min}\) and \(I_{\max}\) indicate the minimum and maximum departure interval during the simulation; \(T_{x_{j},s_{i,q}}^{s_{j,p}s_{i,q}}\begin{cases}1,&\delta_{1}\leq t_{x_{j}s_{i,q}}-l_{x_{j}s_{j,p}}\leq\delta_{2}\\ 0,&\text{other}\end{cases}\) indicates whether the bus \(x_{j}\) of line \(j\) at station \(s_{j,q}\) can be easily transferred to the bus \(x_{i}\) of station \(s_{i,q}\) on line \(i\), where \(\delta_{1},\delta_{2}\) are the time window for synchronous transfer; \(P\) indicates the proportion of choice other transportation modes for passengers who are failed to get on the bus; \(Y_{x_{j}s_{i,q}}\) indicates the number of passengers who failed to get on the bus at the bus \(x_{i}\) of line \(i\) at station \(s_{i,q}\) and chose other transportation modes; \(R_{x_{j}x_{i,q}}^{s_{j,p}s_{i,q}}=\left\{\begin{array}{c}1,l_{x_{j}s_{i,p}} \leq t_{x_{j}s_{i,q}}\\ 0,\text{other}\end{array}\right.\) indicates whether the two lines can be transferred. (2) Decision variables \(A_{x_{i}}\) indicates the departure time of bus \(x_{i}\) of line \(i\) from the parking lot.
## 3 Least Squares Estimation Model for GNSS Positioning and Outlier Correction  
LSE has been used for the GNSS positioning since the start and still is the key part of many GNSS positioning algorithms such as WLSE, IRLS, etc. A standalone LSE algorithm is not a trustworthy estimator especially in GNSS-challenging environments and this is proved by experimental results in Section 9. In this section, we provide a theoretical understanding of the LSE for GNSS positioning because all positioning algorithms follow the same or a very similar approach.
# Recent Progress of Metal-Air Batteries--A Mini Review
## 5 Advanced Manufacturing of MABs
### Spray Coating and 2D Printing  
Spray coating is a traditional coating method to fabricate composites by depositing particles onto the substrate surface. According to the working mechanism, spray can be divided into two types, cold spray and thermal spray. Thermal spray delivers melted metal drops or non-metal particles at high temperatures and forms a coat on the substrate [74]. Thermal spray has relatively wide applications in metal or alloy materials processing, such as surface coating and corrosion resistance. In contrast to thermal spray, cold spray without heating is a coating process to accelerate particles using the supersonic driving gas passing through a convergent-divergent nozzle and subsequently ejected onto a substrate in high speed [75]. Cold spray enables the delivery of various materials, including high melting point metal materials, low melting point polymer materials, even biomaterials. [PERSON] [76] reviewed 24 new applications of cold spray, such as medical devices, electronics, microdevices, and so on.  
Printing is another advanced manufacturing method, including inkjet printing, lithography, 3D printing, 4D printing. In this section, we will first discuss lithography and inkjet 2D printing. Inkjet printing [77; 78] is additive manufacturing and appears after screen-printing and spin coating. The principle of inkjet printing consists of five stages: drop ejection, drop flight, drop impact, drop spreading, and drop solidification. Inkjet printing has been used in depositing functional inks onto various substrates for numerical devices, to specific, sensors, micro-batteries, solar cell, and other conductive parts of cells [79; 80; 81; 82]. [PERSON] [83] thoroughly reviewed technology issues and influence on different substrates for printed capacitive sensors. Furthermore, since [PERSON] [84] reported \"Dip-pen nanolithography\" (DPN) in science, lithography became the focus of contemporary microfabrication. A DPN system is composed of an atomic force microscope tip as a \"nib\", solid-state substrate as \"paper\", and molecules with a chemical affinity for the solid-state substrate \"ink\". By controlling the AFM tip, the nib directly writes controlled patterns on the substrate materials. Lithography contributes to microfabrication in nanomaterials and micro-devices, such as micro-reactors and sensors, micro-optical system [85; 86; 87; 88]. [PERSON] [89] reported nanoimprint lithography in the processing of flexible electronics, conductive electrodes, optoelectronic devices, flexible microlens, and flexible sensors. Certainly, it is feasible to print electrodes with a thin-film structure for metal-air batteries.  
Figure 16: Of 1-, 2-, 3-, and 4D concepts. A 4D structure is a structure (x, y, z) made by 3D changes over time (t). Arrows indicate the direction of change with respect to time.
## 2 The Evolution and Trends of 6G Network Architecture
### The Design Fundamentals of 6G Networks
#### 2.1.2 Non-IP-Based Networking Protocols  
The Internet Protocol version 6 (IPv6) has been in existence for several decades, and there is an increasing demand for the standardization of entirely new networking protocols. The research landscape on protocols beyond IP is extensive, and the European Telecommunications Standards Institute (ETSI)'s Next-Generation Protocol (NGP) Working Group is actively exploring multiple solutions as potential candidates for a groundbreaking approach. At the wireless edge, more than 50% of networking traffic begins or ends, it is entirely reasonable to seek a solution specifically tailored to the wireless sector [7; 8].
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 4 Proposed Protocol
### Login and Authentication Phase
**Step 5:**: After receiving the response of \(FN_{j}\), \(V_{i}\) verifies the freshness of the random number in \(D_{4}\) from \(FN_{j}\) and computes \(R_{i-j-cs}=r_{1}\cdot(r_{2}R_{3}),h(N_{j}\parallel R_{i-j-cs})=K_{ij}\oplus h (N_{i}\parallel R_{i-j-cs}),SK_{i-j-cs}=h(h(N_{i}\parallel R_{i-j-cs}) \parallel h(N_{j}\parallel R_{i-j-cs})\parallel R_{i-j-cs})\), \(Auth^{\prime}_{j}=Auth_{j}\). Figure 3: Proposed fog node registration phase. \(h(h(N_{i}\parallel R_{i-j-cs})\parallel R_{i-j-cs}\parallel SK_{i-j-cs})\), and \(Auth^{\prime}_{i}=h(Auth^{\prime}_{j}\parallel h(N_{j}\parallel R_{i-j-cs}))\). Then, \(V_{i}\) verifies whether or not \(Auth^{\prime}_{i}=Auth\); if conditions are not true, \(V_{i}\) will terminate the current session. After the authentication phase is fully completed, a secure shared session key is established between \(V_{i}\), \(FN_{j}\), and \(CS\). Figure 4: Proposed authentication phase.
## 2 User Needs and Requirements Analysis  
### Diverse User Needs  
The users of the SANS IC-DAS are quite diverse. Categorized by different roles, they can be external facility users, instrument scientists, and supporting teams including sample environment teams, detector teams, and so forth. In addition to the rudimentary instrument control functionality, each role has more specific requirements of the system. For example, supporting teams require in-depth control of instrument components, clear status indication, and sufficient logs for monitoring and diagnostic troubleshooting. Instrument scientists have a good overall knowledge of the whole instrument system and are highly experienced in configuring an instrument for specific scientific requirements. However, they need to be freed from tedious hands-on operations to focus on scientific aspects of user experiments. The largest and most important group are external neutron scattering facility users. We want to empower them to successfully conduct experiments and collect useful measurement data with minimum effort expended on instrument operation. As the SANS instrument suite covers a large span of scientific communities with different skill sets; different experience levels with neutron scattering instruments; and, most crucially, different ways of conducting experiments suited for their sciences, our IC-DAS must make it easy for all of those communities to be productive during the short period of time they spend at an instrument. For example, users from the experimental condensed matter physics community usually have a single sample, but require various sample environment conditions in a particular experiment. They need to be able to easily visualize and interpret the reduced data to decide on the next condition to explore. They usually spend more time on a sample and require quick, flexible manipulation of many instrument and sample environment parameters. On the other hand, biochemists or biologists conducting solution SANS experiments usually use highly standardized instrument configurations for a series of samples with small variants. They need an effortless way of setting up their experiments, while they focus primarily on preparing fresh samples on-site. For these two extreme cases and others between them, a variety of user interfaces reflecting different workflows need to be designed and developed.  
Our analysis of previous usage data further supports the above requirements. For example, the wizard-like GUI extension (Figure 1) previously developed within SPICE served 99.4% of Bio-SANS experiments and 7.8% of GP-SANS experiments over the 2 years since its deployment in 2016 (HFIR was in a long outage from late 2018 to late 2019). The rest of the experiments during that period were served by SPICE macros similar to scripting interfaces. Based on these findings, the new system implements and improves on both of these tools, offering wizard-like Panel Scans and Scripting Tool based on Python, in addition to the simple start/stop button-click GUI and Table Scan feature that were part of the previous EPICS/CS-Studio system.
# Comprehensive Review of Storage Optimization Techniques in Blockchain Systems
## 4 Methods
### Other Solutions
#### 4.7.3 Rollups  
Rollups [105] is an advanced blockchain scalability solution that enables off-chain execution and validation of a large volume of transactions. The transaction data and state transitions are then aggregated and submitted to the main chain in a compressed format, significantly reducing on-chain storage redundancy. Specifically, Rollups aggregates transaction data and state updates, recording only essential transaction summaries (such as transaction hashes and state roots) and necessary validity proofs (e.g., zero-knowledge proofs such as zk-SNARKs or fraud proofs) on-chain. This approach eliminates the need to store and process the detailed data of each individual transaction. Moreover, Rollups employs state compression techniques, such as recording only account balances or state changes, rather than storing complete account information. As a result, the on-chain data volume is drastically reduced. For instance, in the case of Ethereum, Rollup can reduce the storage requirements for batched transactions to as low as 1/10 to 1/100 of that required by the base chain, thereby greatly enhancing storage efficiency.  
By minimizing on-chain data storage needs, Rollups not only mitigates the storage redundancy challenges inherent in blockchain scaling but also reduces transaction costs. This makes it an ideal solution for large-scale, high-frequency applications such as decentralized exchanges, payment networks, and NFT marketplaces. The mechanism enables higher transaction throughput and lower storage overhead while preserving the security and decentralization of the blockchain, positioning Rollup as a key approach for achieving blockchain scalability.
# Improving Data Communication of Enhanced Loran Systems Using 128-_ary_ Polar Codes
## 2 Materials and Methods
### Conventional eLoran Data Communication System  
The conventional data encoding process for the eLoran system, specified by Eurofix [6] and consistent with ITU M589-3 recommendations, utilizes a CRC-aided RS code defined over the Galois Field GF\((q)\) where \(q=128\). As illustrated in Figure 1a, the encoding process begins with \(K^{\prime}=56\) message bits, which is first encoded by a 14-bit CRC encoder, characterized by the following generator polynomial:  
\[G_{CRC}(x)=x^{14}+x^{13}+x^{7}+x^{5}+x^{4}+1, \tag{1}\]  
resulting in a \(K^{\prime}=70\) bit sequence. These 70 bits are then mapped into \(K=10\) information symbols, each represented as a 7-bit element of GF\((128)\). These \(K\) symbols are subsequently encoded by an RS\((M,K)\) code, where \(M=30\) is the code length. The field GF\((128)\) is constructed using the primitive polynomial \(f(x)\):  
\[f(x)=x^{7}+x^{3}+1, \tag{2}\]  
Figure 1: (**a**) System model of traditional eLoran data communication system with RS code and EDP-MD [8] demodulation; (**b**) system model of eLoran data communication system with 128-_ary_ polar code and SS demodulation.  
and the generator polynomial \(G_{RS}(x)\) for the RS code is given by the following:  
\[G_{RS}(x)=(x-a)\cdot\Big{(}x-a^{2}\Big{)}\cdot\Big{(}x-a^{3}\Big{)}\cdot\ldots \cdot\Big{(}x-a^{20}\Big{)}. \tag{3}\]  
where \(a\) is a primitive element of \(\mathrm{GF}(128)\) derived from \(f(x)\).  
For data modulation, the traditional eLoran system employs 128-_ary_ PPM, as detailed in Eurofix [6]. Each of the \(M=30\) RS-coded symbols is transmitted within a Group Repetition Interval (GRI). Each GRI consists of 6 standard eLoran pulses, and each pulse can be modulated to one of three time-slot positions (see Figure 2): 1 \(\upmu\)s advance, prompt (no shift), or 1 \(\upmu\)s delay. This three-level modulation across 6 pulses yields \(3^{6}=729\) possible pulse sequences per GRI.  
From the 729 possible sequences, 141 are designated as \"balanced\" sequences (where the counts of advanced, prompt, and delayed pulses within a GRI are equal, i.e., two of each type). According to Eurofix [6], from this set of 141 balanced sequences, 128 are selected to represent the 128 distinct symbol values \(j\in\{0,\ 1,\ldots,q-1\}\), where \(q=128\). These selected sequences are called \"standard modulation sequences\" in this paper. The modulation level of the \(h\)-th pulse \((h\in\{1,\ 2,\ldots,6\})\) in a standard modulation sequence corresponding to the symbol value \(j\) is denoted as \(S(h,j)\in\{0,1,2\}\), where \(\{0,1,2\}\) corresponds to 1 \(\upmu\)s advance, prompt, and 1 \(\upmu\)s delay, respectively.  
At the receiver, the conventional eLoran system typically uses the Envelope Phase Detection-Majority Decision (EPD-MD) demodulation technique [8]. The output of the EPD-MD demodulator provides symbol estimates to the RS decoder, and the integrity of the final recovered message is verified via a CRC.
# Current Status and Future Directions for Combined Tillage and Sowing Technology and Equipment
## 2 Status of Development of Combined Tillage and Sowing Machines
### Combined Disk Harrow and Sowing Machine
Subsequent research can focus on creating methods to prevent blockages, improve seed metering, and enhance collaboration between components to ensure the continuous stability and efficiency of the operation. Currently, most combined disk narrow and sowing machines are large-sized, which poses challenges for small farms and individual farmers due to their high costs and mismatched implements. Developing small and medium-sized combined disk narrow and sowing machines will be necessary to cater to fragmented, trimmed, and general quality arable land. This will help meet the needs of different agronomic, economic, and land conditions and improve the machines' adaptability and scope of use.
# Approximation by Bicomplex Favard-Szasz-Mirakjan Operators
## 4 Conclusions and Future Directions  
Recently, bicomplex numbers and bicomplex analysis have attracted increasing attention, particularly in the fields of mathematics and physics. Motivated by the theoretical advantages offered by this area, the present work extends the related framework and introduces certain generalizations to approximation theory. To this end, we have employed bicomplex Favard-Szasz-Mirakjan operators, defined analogously to the classical complex Favard-Szasz-Mirakjan operators. In other words, this article can be considered a study that allows algebraic structures to interact with the analytical behavior of mathematical arguments. As part of future research, similar approaches may be incorporated within the scope of other suitable polynomials or operators to obtain improved approximation results.  
The definitions and results presented in this study are considered within the framework of bicomplex spaces. Naturally, this raises the question of whether these structures and findings can be extended to higher-dimensional multicomplex spaces. However, transitioning to higher dimensions may involve considerable structural and analytical challenges. Thus, exploring such generalizations in a rigorous and comprehensive manner remains an important open problem for future research.  
In addition to pure mathematics, the theoretical perspective developed in our work may provide new insights into areas where hypercomplex and complex numbers find application, such as signal processing, Hopfield neural networks, electromagnetic theory, quantum mechanics, etc.  
There is undoubtedly still a lot to be done with convenient linear and positive operators, especially with respect to their approximation results and bicomplex modifications. Moreover, by extending the current theoretical background, interesting properties of bicomplex operators such as univalence, starlikeness, convexity, and spirallikeness may also be investigated.  
**Author Contributions**: Conceptualization, G.A.A., O.O.G., M.R. and S.K.; methodology, G.A.A., O.O.G., M.R. and S.K.; validation, G.A.A., O.O.G., M.R. and S.K.; formal analysis, G.A.A., O.O.G., M.R. and S.K.; investigation, G.A.A., O.O.G., M.R. and S.K.; resources, G.A.A., O.O.G., M.R. and S.K.; writing--original draft preparation, G.A.A., O.O.G., M.R. and S.K.; writing--review and editing, G.A.A., O.O.G., M.R. and S.K.; supervision, G.A.A.; project administration, G.A.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Acknowledgments:** We would like to thank the reviewers who generously shared their time and opinions. This paper is dedicated to [PERSON]. Forever in our hearts.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Review of Oil Spill Remote Sensing
## 5 Detection of Oil on the Sea Bottom
### Laser Fluorosensors  
Laser fluorosensors can detect the aromatic compounds in oil. Laser fluorosensors were tested for their ability to detect oil in the water column. This was shown to be viable to a depth not exceeding about 2 m [33]. Although suggested several times, laser fluorosensors operating on the surface have not been tested for their capability of detecting oil on the sea floor. Sub-sea fluorosensors have been successful however in limited testing.