esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods  
The spatial-temporal infrared patch-tensor model is described as:  
\[f_{D}=f_{B}+f_{T}+f_{N} \tag{9}\]  
where \(f_{D}\), \(f_{B}\), \(f_{T}\), and \(f_{N}\) represent the original, background, target, and noise images, respectively. As shown in Figure 1, each image frame is split into small image patches, and all the small image patches of consecutive \(L\) frames are superimposed into the 3D patch-tensor. Therefore, the above formula can be rewritten into a tensor form as shown in Formula (2) in Section 2.2.  
In the WNRIPT model, the problem of point target detection is expressed as:  
\[B.T=\frac{min}{B.T}||B||_{W_{B,*}}+\lambda||W_{T}\odot T|| \tag{10}\]  
where \(\left|\left|B\right|\right|\right|_{W_{B,*}}=\frac{1}{L}\sum\limits_{i=1}^{r} \sum\limits_{j=1}^{L}W_{B}(i,i,j)\).  
In order to further improve the performance and efficiency of point target detection, the 3D-TV regularization is introduced into the spatial-temporal tensor model, and its expression is:  
\[B.T.N=\frac{arg\ min}{B.T.N}||B||_{W_{B,*}}+\lambda_{1}||V(B)||_{3D-TV}+ \lambda_{2}||T||_{1}+\lambda_{3}||N||_{F}^{2}\]  
\[s.t.\ F=B+T+N \tag{11}\]  
where \(k\times||*||_{3D-TV}\) is the norm of 3D-TV, and \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\) represent the regularization parameters of the 3D-TV term, target component, and noise component.  
As the Frobenius norm [24,25] has a good noise suppression effect, the Frobenius norm term is further introduced:  
\[B.T.N=\frac{arg\ min}{B.T.N}||B||_{W_{B,*}}+\lambda_{1}||V(B)||_{1}+\lambda_{2 }||T||_{1}+\lambda_{3}||N||_{F}^{2}\]  
\[s.t.\ F=B+T+N \tag{12}\]  
Figure 1: Flow Chart of the MNSTLA Method.  
In this model, the 3D-TV regularization term is introduced, which can fully capture the spatial-temporal information of infrared sequence images, so it is expected to achieve better performance.
## 3 Results and Discussion
### AD Performance and Wastewater Treatment Efficiency
This construction provided longer biomass retention time, lower sludge yield, the ability to partially separate acidogenesis and methanogenesis, better resilience to hydraulic and organic shock loadings, and better substrate accessibility to methanogens, consequently resulting in high treatment efficiencies at low HRT (2-6 h) and high OLR (0.4-28 kg/m\({}^{3}\cdot\)d) as well as increasing the overall stability of the reactor. According to the literature, recirculation provides some benefits during wastewater treatment, such as improving contact between the methanogenic bacteria and substrate components, greater reactor homogeneity, and shortening the time in which maximum methane yield is achieved [54; 55]. Recirculation of the digestate liquid fraction within the anaerobic digester is mostly used in dry anaerobic digestion [54; 56; 57; 58]. [PERSON] and co-workers [52] obtained better biogas production during the anaerobic digestion of pig slurry with increasing frequencies of a liquid fraction of digestate recirculation. [PERSON] and co-workers [59] achieved maximum methane production in 9 days with recirculation compared to 11 days without it. Recirculation of a liquid fraction of the digestate in a system of CSTRs for corn stover anaerobic digestion enhanced methane and biogas yield by 2.3% and 10.8%, respectively [46]. Methane production increased by 26% during the anaerobic digestion of food waste in a hybrid solid-liquid reactor with recirculation compared to a conventional two-phase anaerobic digester without recirculation [60]. However, some problems may occur after the long-term operation with digestate recirculation, such as the accumulation of VFAs, ammonia nitrogen, non-biodegradable intermediates, and other inhibitory substances [61].
## 2 Materials and Methods
### Experiments
#### 2.2.3 Experiment 3: The Procedure for Selecting Routes  
In this experiment, we performed three simulations. In the first simulation, the vehicles routes were chosen with the links' travel times calculated every 90 s based on the links speeds of the previous period, i.e., \(\mathrm{travel\_time_{T}^{x}}=\mathrm{link\_length^{x}}/\mathrm{PER\_speed^{x} }_{T-1}\), with \(\mathrm{travel\_time_{T}^{x}}\) being the representative travel time of segment x at any time t belonging to period T. In the second simulation, the links' travel times were calculated every 90 s considering the links' speeds of the previous two periods, i.e., \(\mathrm{travel\_time_{T}^{x}}=\mathrm{link\_length^{x}}/\left(\mathrm{(PER\_speed^{x} }_{T-1}+\mathrm{PER\_speed^{x}}_{T-2}\right)/2\right)\). In the third simulation, the travel time of each segment was a constant, so \(\mathrm{travel\_time_{T}^{x}}=\mathrm{link\_length^{x}}/\mathrm{normal\_speed}\) for any T. For all simulations, the route of a vehicle was computed using the Dijkstra algorithm, which selects the route with the shortest travel time.
## 3 Mesh Stiffness of Internal Spur Gears with Tooth Wear
### Single-Tooth Mesh Stiffness
In general, the contact ratio of internal spur gear pair is from 1 to 2. Therefore, superimposing single-tooth stiffness of the meshing teeth, the single-tooth mesh stiffness can be deduced as follows:  
Figure 10:7  
where the subscripts \(i=1,2\) represent the driving gear and driven gear, respectively. Given the same wear depths and the gear parameters as in Section 2, the single-tooth mesh stiffness with different non-uniform wear depths can be obtained as presented in Figure 13, while Figure 13a presents the single-tooth mesh stiffness of \(\alpha_{1}\). It can be seen that tooth wear decreases the mesh stiffness and the mesh range is smaller. In addition, \(\alpha_{1}\) denotes the meshing position of the driving gear and it varies from tooth root to tooth tip, where \(\alpha_{1}\) varies from small to large. In this figure, the initial meshing position \(\alpha_{1}\) is increased and the final meshing point \(\alpha_{1}\) remains unchanged, which indicates that the single-tooth meshing range gradually decreases with the increasing wear depth. However, we usually consider the single-tooth mesh stiffness at different rotational angles. The single-tooth mesh stiffness of \(\varphi_{1}\) is shown in Figure 13b, where \(\varphi_{1}\) denotes the rotational angle of the driving gear. It can be seen that the single-tooth mesh stiffness of \(\varphi_{1}\) increases significantly in both mesh-in and mesh-out stages as the depth of wear increases, which is obviously different from that of \(\alpha_{1}\). Meanwhile, the range of rotational meshing also increases with the increase of gear wear depth.
## 3 Results and Discussion  
### Production of eLpp-OmpA-AppA and Display of AppA  
#### 3.1.1 Construction of the Expression Plasmid, pESE-AppA  
The expression plasmid encoding the eLpp-OmpA-AppA protein (pESE-AppA, see Section 2.4) was constructed by subcloning the mature AppA sequence into pESE, a pET22b(+)-based vector that encodes eLpp-OmpA under control of the isopropyl-\(\beta\)-D-thiogalactopyranoside (IPTG)-inducible T7 lacO promoter [41]. DNA sequencing confirmed the precise insertion of AppA codons (i.e., in-frame with those of eLpp-OmpA), which implies correct protein fusion (Figure 2) and the potential for gene expression under appropriate induction conditions. Furthermore, as a pET-based plasmid, pESE-AppA uses T7 RNA polymerase (T7 RNAP) for high-level expression of cloned genes and constitutively expresses LacI, a lacO-binding protein that negatively regulates gene expression under non-induced conditions [53; 54; 55].
## 5 Discussion
### Theoretical Contribution  
The theoretical contribution of our work is twofold: First, we present a knowledge framework (see Table 2) that outlines three levels, nine topics, and five aspects. This framework categorizes these topics into enablers, value creators, and outcomes, and examines each topic through five aspects. It consolidates the existing infrastructure, technology, capabilities, theoretical foundations, strategies, solutions, and evaluation criteria incumbent manufacturers encounter when implementing digital technologies for DDBMI. Incumbent manufacturers face significant challenges in adopting DDBMs, especially due to the intricate interplay and interconnectedness of these various topics. Therefore, their considerations must include a conceptual understanding of these interrelated topics. By defining these topics and aspects and identifying their relationships, our framework facilitates knowledge transfer and supports future decision-making processes. It illustrates the impact of these nine topics on DDBMI and is instrumental in developing integrated solutions tailored to incumbent manufacturers' needs. Additionally, it is a valuable resource for future research in that it provides an overview of the connections between enablers, value makers, and outcomes, thereby stimulating further and deeper analyses of these topics.  
Second, our paper contributes to a better understanding of the theory of organizational preparedness by categorizing the nine topics across five aspects. The knowledge framework can be utilized to evaluate incumbent manufacturers' preparedness for DDBMI by clarifying the topics' interdependence as enablers, value makers, and outcomes. The knowledge framework prompts incumbent manufacturers to assess their preparedness regarding each aspect of the topics by addressing the following questions: (1) What level of preparedness is necessary to achieve our desired business outcomes and implement the planned DDBMI? (2) What level of preparedness do we currently possess for each topic and aspect concerning DDBMI by identifying strengths and weaknesses? (3) What are the key positive and negative interactions among different topics and aspects that could influence our overall business objectives? Each point within the knowledge framework, associated with a different topic and its related aspect, must undergo empirical testing, meta-analysis, and scrutiny by internal and external experts to assess preparedness for DDBMI.
## 5 Challenges, Open Research Problems, and Future of WBANs
### Compatibility Problems  
The integration of multiple sensory devices that operate at different frequencies increases the compatibility problems. Communication between devices takes multiple bands and use different protocols. This can lead to interference between different devices, especially in the unlicensed industrial, scientific and medical band radio. WBANs and the widespread healthcare systems must be developed to ensure compatibility between different devices.
# A Bayesian Control Chart for Monitoring Process Variance
## 3 The Construction of the Shewhart-Type Variance Chart with the Bayesian Approach
### The Parameters of the Prior Distribution of \(p\) and Process Variance Are Known
#### 3.1.1 The In-Control ARL of the Shewhart-Type Bayesian Variance Chart  
The average run length (ARL) is used to measure the performance of a control chart. The in-control ARL, denoted by \(ARL_{0}\), of the Bayesian variance chart is associated with \(n\), \(\alpha_{0}\), and \(\beta_{0}\). Under the in-control process, the chance that the proposed chart alarms a false signal is calculated by:  
\[ALARM_{0}=P(\,M_{t}\leq LCL_{M}\text{ or }M_{t}\geq UCL_{M}|M_{t}\sim Beta- Binomial(n,\,\alpha_{0},\,\beta_{0})). \tag{17}\]  
So, \(ARL_{0}=1/ALARM_{0}\). Table 1 presents the \(ARL_{0}\)s of the proposed Bayesian variance chart for various combinations of \(n\), \(\alpha_{0}\), and \(\beta_{0}\).  
There are many infinite \(ARL_{0}\)s in Table 1, indicating that the proposed Bayesian variance chart does not release false alarm under an in-control process. However, there also appear many \(ARL_{0}\)s smaller than 370.4, indicating that the proposed chart frequently releases false alarm under the in-control process. The inconsistent results may be attributed to the discontinuity of the monitoring statistic.
# Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps
## 2 Literature Review
### Orbital Scale  
A large portion of the articles employing satellite images aimed to either compensate for data gaps present in a primary data source by fusing it with another source of data (for example, combining optical and SAR images) [6; 45; 47; 48; 49; 51; 105; 106], or increase the spatial resolution of the relatively coarse images collected by satellites with high revisit frequencies [42; 43; 44; 55; 57; 58; 107; 108; 109; 110]. In the latter, the fused results usually inherit the details of the high spatial resolution images and the temporal revisit the frequencies of their counterparts, although the quality of the fused data usually do not match that obtained through actual missions, especially when surface changes are rapid and subtle [72]. As argued by [PERSON] et al. [111], different sensors and image processing algorithms lead inevitably to data with some level of inconsistency, which can make rapid changes difficult to detect. Landsat and MODIS images and products still dominate, but other satellite constellations, such as Sentinel, Worldview, GeoEye, and others, are being increasingly adopted. Data fusion has been applied to satellite images for quite some time, and well established techniques, such as STARFM and its variants, are still often used, but the interest for machine learning techniques, especially in the form of deep learning models, has been growing consistently. Water management in its several forms (evapotranspiration estimation, mapping of irrigated areas, drought detection, etc.) is by far the most common application. Yield estimation, crop monitoring, land cover classification, and prediction of soil properties are also common applications. A major challenge associated with the orbital scale is the existence of highly heterogeneous regions with a high degree of fragmentation [109; 112]. Solutions to this problem are not trivial and, as stated by [PERSON] et al. [113], \"... successful mapping of a fragmented agricultural landscape is a function of objectively derived datasets, adapted to geographic context, and an informed optimization of mapping algorithms\". However, there are cases in which target areas can have sizes smaller than the pixel resolution of the satellite images [53]. In theses cases, pairing the images with images or other types of data obtained at higher resolutions (aerial or proximal) may be the only viable solution. Satellite data were fused together with proximal and aerial data in ten and six studies, respectively (Table 5).
Sensors_**2015**, _15_, 248-273; doi:10.3390/s150100248  
OPEN Access  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Structure Fidelity Approach for Big Data Collection in Wireless Sensor Networks**  
**[PERSON] 1, [PERSON] 1 and [PERSON] 2,3,* * \({}^{1}\)** Department of Computer Science, Central China Normal University, Wuhan 430079, China;  
E-Mails: [EMAIL_ADDRESS] (M.W.); [EMAIL_ADDRESS] (L.T.)  
\({}^{2}\) School of Information Technology, Jiangxi University of Finance and Economics,  
Nanchang 330013, China  
\({}^{3}\) School of Computer Science, Colorado Technical University, Colorado Spring, CO 80907, USA  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS] or  
[EMAIL_ADDRESS]; Tel.: +1-404-645-4067.  
Academic Editor: [PERSON]  
_Received: 19 September 2014 / Accepted: 11 December 2014 / Published: 25 December 2014_  
**Abstract:** One of the most widespread and important applications in wireless sensor networks (WSNs) is the continuous data collection, such as monitoring the variety of ambient temperature and humidity. Due to the sensor nodes with a limited energy supply, the reduction of energy consumed in the continuous observation of physical phenomenon plays a significant role in extending the lifetime of WSNs. However, the high redundancy of sensing data leads to great waste of energy as a result of over-deployed sensor nodes. In this paper, we develop a structure fidelity data collection (SFDC) framework leveraging the spatial correlations between nodes to reduce the number of the active sensor nodes while maintaining the low structural distortion of the collected data. A structural distortion based on the image quality assessment approach is used to perform the nodes work/sleep scheduling, such that the number of the working nodes is reduced while the remainder of nodes can be put into the low-power sleep mode during the sampling period. The main contribution of SFDC is to provide a unique perspective on how to maintain the data fidelity in term of structural similarity in the continuous sensing applications for WSNs. The simulation results based on synthetic and real world datasets verify the effectiveness of SFDC framework both on energy saving and data fidelity.  
**Keywords:** data collection; spatial correlation; wireless sensor network; structure fidelity
# Performance Evaluation of IM/DD FSO Communication System Under Dust Storm Conditions
## 5 Conclusions  
This study presented a probabilistic framework for evaluating the performance of an IM/DD FSO communication system under dust-induced fading conditions. Real dust samples collected from an actual storm were used in a controlled laboratory setup to emulate realistic channel conditions. The measured signal data was statistically analyzed, with the Beta distribution identified as the most suitable model for characterizing dust-induced signal fluctuations. Based on this distribution, closed-form expressions were derived for key performance metrics, including BER, outage probability, and average channel capacity. The analytical results demonstrated close agreement with Monte Carlo simulations across a wide SNR range, validating the accuracy and consistency of the proposed model. Notably, the BER reached the FEC threshold of \(10^{-3}\) at approximately 10.5 dB, and the outage probability fell below \(10^{-3}\) at an average SNR of 10 dB. Future work will extend this framework to incorporate additional channel impairment such as turbulence and pointing errors, enabling a more comprehensive evaluation of FSO performance under composite environmental conditions. This will support the development of more robust and adaptive FSO systems suitable for deployment in diverse and challenging outdoor scenarios. Moreover, exploring the impact of particle characteristics under controlled dust classifications represents a valuable direction for  
Figure 5: Average channel capacity versus the system SNR.  
future research, particularly to generalize the proposed model across different atmospheric dust conditions.  
This article is derived from a research grant funded by the Research, Development, and Innovation Authority (RDIA), Saudi Arabia, under grant number (13292-psu-2023-PSNU-R-3-1-EF). The authors were also supported by the Prince Sultan University, Riyadh, Saudi Arabia.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges (APCs) for this publication. Also, the authors would like to thank the RFTONICS center in KSU for accessing their facilities.  
The author declares no conflicts of interest.
## 3 5G HetNet Services, Architecture, and Related Technologies
### 5G Services  
The three primary 5G-NR services are Enhanced Mobile Broadband (eMBB), Massive Machine-Type Communications (mMTC), and Ultra-Reliable Low-Latency Communications (URLLC). These services aim to utilize 5G technologies to meet the requirements of various 5G applications.  
* **Enhanced Mobile Broadband:** The anticipated benefits of 5G systems over LTE-A systems include increased data rates (20 Gbps in DL and 10 Gbps in UL) and huge capacity (\(10^{6}\) devices/km\({}^{2}\)) [42]. As a result, applications such as streaming HD video, online gaming, and other high-bandwidth services are expected to perform better on the 5G network [43]. Two significant technologies, mm-Wave and M-MIMO, are used to deliver high data rates in 5G networks [44].
* **Massive Machine-Type Communications:** Massive machine-type communication (mMTC) provides scalable connectivity solutions for a massive number of devices and supports a large number of devices using MEC as a technology solution [45]. The mMTC 5G service is well-suited for applications such as the IoT and M2M communications [46].
* **Ultra-Reliable Low-Latency Communications (URLLC):** URLLC 5G service is based on NFV, SDN, and MEC technologies. URLLC is expected to be the most challenging service to provide, requiring a high level of network availability and low latency. The 5G network ensures ultra-reliable communication with a low packet-error rate (\(<\)\(10^{-5}\)). Furthermore, for services such as industrial automation, autonomous vehicles, and remote medical procedures (healthcare), URLLC is a promising solution [30; 47].
# Bounce: A High Performance Satellite-Based Blockchain System
## 4 Slot Protocol
### Accumulator Node Algorithms: Account Balances  
As mentioned above, to support payments, one or more user nodes, called _Accumulators_, can maintain the account balances of all account holder based on the ordered sequence of blocks in the blockchain. This is an application concern, so is not our primary focus. Nevertheless, because payments are a core use case of many blockchain systems, we describe the algorithms here and the implementation in Section 7.  
There are two main issues, the first having to do with the Bounce protocol itself and the second is generic to payment systems.  
* Because user transactions may be sent to several Sending Stations, the same transaction may appear in several Merkle trees. We would want to avoid processing the same transaction more than once.
* In a payment setting, we want to guarantee that accounts can't fall below zero coins. The overall pseudo-code of an accumulator node is shown in Algorithm 5.  
```
1:Input: Commit record containing root \(r\) and corresponding Merkle Tree \(M(r)\).
2:Procedure:
3:if\(r\) has already been seen (possibly from a different Sending Station) then
4: Ignore \(M(r)\)
5:else
6:for each transaction \(T\) in \(M(r)\)do
7:if\(T\) has already appeared in the current or a previously committed Merkle tree then
8: Ignore \(T\)
9:else
10: Process \(T\)
```  
**Algorithm 5** The Accumulators Processing Commit RecordsAlgorithm 5 ensures that an accumulator does not process the same transaction twice. The \"Process \(T\)\" step requires a data structure. We propose constructing transaction identifiers as the concatenation of the wallet identifier of the payor and a sequence number. The account record for a wallet \(W\) consists of \(W\)'s wallet id, a balance, and some representation of the sequence numbers of all committed transactions in which \(W\) is the payer. In the case where each wallet issues a payment transaction only when all of its previous transactions have already appeared in committed Merkle trees, this representation consists simply of the highest sequence number of any committed transaction.  
We now elaborate on what process(T) does in Algorithm 6.  
```
1: Extract transaction identifier of the payer (\(\mathsf{Walletid},\mathsf{seqnum}\)) from \(T\)
2:if\(\mathsf{seqnum}\) is not already in the set of committed transactions of \(\mathsf{Walletid}\)then
3: Add \(\mathsf{seqnum}\) to the set of committed transactions of \(\mathsf{Walletid}\)
4: Execute \(T\) which will cause payment if there are sufficient funds in this wallet
5:endif
```  
**Algorithm 6** The Accumulators Processing a Transaction \(T\) for the payer wallet \(\mathsf{Walletid}\)  
If the funds are insufficient, then the execution of T may be modified, either to do nothing or to perform a partial payment. In our algorithms, we assume for simplicity that insufficient funds result in no payment. When there are sufficient funds, an Accumulator node will update both Wallets.