esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Federated Lemus Optimization Algorithm (FedLO)
The proposed FedLO framework, illustrated in Figure 1, operates in a structured manner, commencing with initializing both local optimization parameters and the global model. Clients, treated as individual solutions, are strategically selected to participate in collaborative learning. The process then evaluates predefined stopping criteria; if unmet, the global model is transmitted to all chosen clients. Subsequently, each client undertakes local model training, employing local optimization mechanisms to fine-tune the model according to its specific dataset characteristics. Scores reflecting the performance of locally trained models are collected from all clients, and the framework checks whether all models have updated their weights. If not, the process continues by requesting and receiving the best-performing model among participants, updating it based on the received information. The iterative cycle persists until all models have completed their updates. Upon meeting the stopping criteria, the FL process concludes. FedLO ensures collaborative model improvement while considering the nuances of local data, fostering an effective and efficient FL environment. Notably, for each client in this study, the nearest solution corresponds to the previously identified best model in this paper. The process of updating the weights in the FedLO algorithm is illustrated in Figure 1. First, the server initializes the model and FedLO parameters before distributing the model to clients. Each client trains the model using FedLO weights and computes a score based on the lowest loss or highest accuracy achieved. The client with the highest score sends this value to the server, which then updates the global model's weighted collection with the best client's model. The pseudocode for the FedLO is detailed in Algorithm 1, outlines a strategy for optimizing distributed client performance in FL environments. Figure 1: FedLO framework. ```
1:Initialize: Set algorithm parameters, client positions, and communication intervals. 2:Initialize global best position: \(gbest\)
3:Initialize lemurs' positions on each client: \(L_{i}^{j}\)
4:for each iteration \(itr=1,2,3,\ldots,Max_{itr}\)do
5:for each client \(k\)do
6: Evaluate fitness of lemurs on client \(k\)
7: Update \(nbest\) using lemurs' local best positions
8:endfor
9:for each client \(k\) in parallel do
10:for each lemur \(i\) on client \(k\)do
11: Update lemurs' positions based on exploration and exploitation phases
12:endfor
13:endfor
14: Communicate global best positions (\(gbest\)) among clients
15:for each client \(k\)do
16: Update global best position based on received information
17:endfor
18:endfor
```  
**Algorithm 1** Federated Lemurs Optimization Algorithm (FedLO)
## 4 Discussion
The results of other relevant works should be concisely discussed to compare the performance of apple MC and pH predictive models with those of other fruits. [PERSON] et al. [12] used NIR HSI (1000-1550 nm) and the PLSR model to investigate the non-destructive estimation of MC and pH in intact tomatoes. The \(R\)-values for MC and pH prediction were between 0.710 to 0.810 and 0.370 to 0.710, respectively. Although the fruit types were different, the higher reliability in predicting MC is in agreement with our observation for apples. [PERSON] and [PERSON] [17] utilized NIR HSI in the range of 900-1700 nm together with successive projection algorithm and least squares support vector machine to predict Fuji apple's MC and pH and reported \(R\)-values of 0.984 and 0.882, respectively. Their better performance compared to our results can be mainly attributed to the different spectral ranges they utilized. It is well-known that instruments working in the NIR range are more expensive than VIS-NIR as they utilize InGaAs instead of silicon detectors. Alternatively, different apple cultivars and modeling tools may also account for the discrepancy between the results. In a separate study, [PERSON] et al. [48] utilized VIS-NIR transmittance spectroscopy to estimate pH in Fuji apples, achieving \(R\)-values ranging from 0.63 to 0.69. Once again, variations in apple types and different data-capturing techniques could potentially explain their superior performance. Overall, comparing our results with those of other relevant works highlights the potential of non-destructive techniques for predicting fruit quality parameters and the importance of considering various factors such as fruit type, spectral range, and modeling approaches when interpreting and comparing the results of different studies.
## 3 Proposed Lightweight Deep Learning Approach
### Temporal Efficient Residual Network
#### 3.2.4 Basic Block of ResNet Style  
The basic block, designed for feature extraction from optical fiber sensing signals, is adapted from ResNet18 [42]. It comprises two \(9\times 1\) convolutional layers, followed by batch normalization (BN), ReLU activation, and a residual connection. The residual connection links the input and output (after BN) of the convolutional layers, allowing the network to learn identity mappings. It helps mitigate issues like gradient vanishing and explosion. The residual connection operates as follows:  
\[H(x)=identify(x)+F(x) \tag{2}\]  
where \(H(x)\) represent the output of the basic block, \(x\) denote the input to the convolutional layer, \(F(x)\) is the transformed feature obtained after passing through the two convolutional layers, and \(identify(x)\) denotes the input identity mapping of \(x\), i.e., the input is passed directly to the output without any transformation. The purpose of this design is to preserve the original information of the input signal during feature extraction, thus increasing the sensitivity of the network to subtle features, while avoiding the loss of information caused by the deep network.
## 5 Conclusions  
The objective of the current study was to develop a guidance for accurate landslide mapping in steep terrains. In this context, we performed four identical tests within four active landslides spreading throughout Western Greece. The main conclusions that emerged from this research included the following:  
* It was proved that the acquisition of UAV oblique and nadir imagery and the synergistic processing increase overall centimeter accuracy.
* In general, oblique imagery provides more accurate results in steep terrains compared to nadir imagery. However, in areas that combine high slopes, dense urban settlements, and narrow streets (as in Messarista village), nadir imagery could not be omitted.
* Even in flat areas, such as Patras Port, the combined use of oblique and nadir imagery ameliorates the overall accuracy.
* A UAV flight campaign should be adjusted each time to an investigated area's characteristics and local topography.
* In steep terrains, an average flight altitude between 70 and 110 m above ground level or a ground spatial resolution of around 3 cm are recommended for both nadir and oblique campaigns in order to assess centimeter accuracy.  
Conceptualization, K.G.N.; methodology, K.G.N. and A.K.; software, K.G.N. and A.K.; validation, K.G.N., A.K. and I.K.K.; formal analysis, A.K.; investigation, K.G.N., A.K. and I.K.K.; resources, K.G.N.; data curation, K.G.N., A.K. and I.K.K.; writing--original draft preparation, K.G.N. and A.K.; writing--review and editing, K.G.N., A.K. and I.K.K.; project administration, K.G.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available, due to privacy considerations.  
The authors declare no conflict of interest.
## 4 Methodology and Data  
### Measurement  
A questionnaire comprising three parts was developed to gather data for the study. The first part aimed to collect information about the respondents and their respective companies. The second part focused on determining whether the respondents' companies had adopted various Industry 4.0 technologies, including cloud computing, IoT, big data and analytics, additive manufacturing, autonomous robots, AR, VR, AI, blockchain, simulation, cybersecurity, CPS, MES, and mobile technology.  
In the third part of the questionnaire, implementation success and associated success factors were assessed using five-point scales ranging from 1 to 5 (representing strongly disagree to strongly agree). Following the approach of prior research on the implementation of advanced manufacturing technology [109; 110; 111], implementation success was operationalized as improvements in operational performance, encompassing aspects such as quality, efficiency, cost, and flexibility following the adoption of Industry 4.0 technologies.  
The items pertaining to success factors were primarily adapted from the existing literature on Industry 4.0, IT/IS, and AMT (refer to Appendix A for details). To enhance the clarity and validity of the questionnaire, an expert panel consisting of three scholars was convened to review and refine it. Subsequently, the questionnaire underwent a pre-test phase involving feedback from six senior managers representing manufacturing firms. Their suggestions were carefully considered and incorporated to refine the questionnaire further.
# Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks
## 3 System Model
### NOMA-Enabled Transmission Model  
We assume that each UE can only be grouped into one NOMA cluster [44]. We define \(x_{m,n}=1\) to indicate that UE \(m\) is assigned to the NOMA cluster \(n\). On the other hand, \(x_{m,n}=0\) indicates that this assignment does not occur. Then,  
\[\sum_{n=1}^{N}x_{m,n}=\left\{\begin{array}{c}1,\text{ task }D_{m}\text{ transmit to ES through NOMA cluster }n,\\ 0,\text{ task }D_{m}\text{ is computed locally}.\end{array}\right.. \tag{2}\]  
Since the result obtained after video processing is very small, we do not consider the process of sending the result back to the UEs, and only consider the process of offloading the video stream to the MEC server. The uplink transmission rate of the UEs for the NOMA scheme is  
\[R_{m}=\frac{1}{K}W\sum_{n=1}^{N}x_{m,n}\log_{2}\left(1+\frac{p_{m,k}h_{m,k}}{ \sigma_{k}^{2}+\sum\limits_{j\
eq m}p_{jk}h_{j,k}}\right), \tag{3}\]  
where \(W\) denotes the total transmission bandwidth, which may be bisected by \(K\) subchannels; \(p_{m,k}\) represents the transmit power of the UE \(m\) to the MEC server on subchannel \(k\); \(h_{m,k}\) represents the channel gain between the UE \(m\) and the MEC server on subchannel \(k\); \(\sigma_{k}^{2}\) indicates the noise power on the subchannel \(k\). Then, the translation delay of UE \(m\) can be expressed as  
\[t_{m}^{U}=\frac{l_{m}}{R_{m}}\cdot\frac{1}{\rho_{m}}, \tag{4}\]  
where \(\rho_{m}\) is the compression ratio of the video frame for UE \(m\), which is determined by video resolution and bit rate [45].  
When the video stream task \(D_{m}\) is offloaded to the edge for computing, the energy consumption generated by the UE \(m\) during transmission is  
\[e_{m}^{U}=p_{m,k}\cdot t_{m}^{U}. \tag{5}\]
## 2 Related Works
### Optimization Objectives for Controller Deployment  
Network performance optimization parameters considered in existing solutions usually include reliability, load balancing, delay, and event response time. In order to reasonably place the controller and allocate the switch-controller mapping relationship, optimization is generally achieved by optimizing single or multiple network performance parameters. The algorithm proposed in [17] partitions the network to minimize the maximum delay between switches. Reference [18] considers the controller load factor, which is regarded as a k-center problem with capacity, significantly increasing the capacity of the control plane. Reference [19] defines the controller placement problem for load balancing, which minimizes the average delay between switches and controllers while maintaining load balancing among controllers. Some methods consider multiple optimization objectives. Reference [20] employs a bargaining game to search for the optimal placement strategy of the controller for minimizing the objectives of delay, communication overhead, and load balancing. In order to ensure that the communication between the switch and controller is not interrupted to guarantee the network's reliability, Reference [21] proposes two reliable placement strategies. One is that there must be two disjoint control paths between the switch and the controller. The other considers that the switch must be connected to two different controller duplicates for efficient failover. In the multi-objective approach, some objectives are conflicting; for example, if the average delay in the switch-controller interaction is minimized, the average delay of the inter-controller becomes more extensive, which leads to the degradation of QoS. In addition, some solutions to the controller placement problem also consider other performance metrics, such as energy efficiency. Reference [22] proposes an energy-aware solution called GreCo, which aims to minimize network energy consumption.  
In the early period of network establishment, the secure placement of controllers is a challenging problem. Reference [23] proposes a controller placement strategy for multi-link failures with maximum resource utilization and minimum worst-case delay to achieve efficient network performance. Reference [24] proposes a heuristic algorithm to solve CPPwith a single-link failure. In addition, the authors also exploit a greedy algorithm based on Monte Carlo simulation to solve CPP with multiple-link failures. Apart from this, the security of the network depends on the performance of the intrusion detection algorithm. Reference [25] proposes a machine-learning-based anomaly detection algorithm to quickly guide traffic classification and attack flow redirection problems to analyze unknown threats and attacks better.
## Data and Methods
### Object Detection
#### 3.4.3 Licence Plates  
For vehicle license plate detection, the analysis is comprised of two consecutive stages. Initially, a neural network based on the Cascade R-CNN architecture [33] and pre-trained on the BDD100K dataset [35] is used for vehicle detection. Indicative performance metrics for the pre-trained model are presented in Section 3.4.2. Subsequently, the detected objects are cropped from the frame and form the input to the neural network used in the second stage of the process. More specifically, in order to detect vehicle license plates we fine-tune a neural network based on the Cascade R-CNN architecture. The neural network is fine-tuned within the PyTorch framework [36] and the backbone part of the model is pre-trained on the COCO dataset [31]. The license plates dataset that is utilized consists of 433 images of vehicles of different types accompanied by the corresponding license plate annotation files in PascalVOC format and is publicly available on MakeML ([[https://makeml.app/datasets/cars-license-plates](https://makeml.app/datasets/cars-license-plates)]([https://makeml.app/datasets/cars-license-plates](https://makeml.app/datasets/cars-license-plates)), date accessed 10 December 2022). In order to evaluate the model, we use the _license plate evaluation_ dataset described in Section 3.3.
# Nanosized Ni/SBA-15 Catalysts for CO\({}_{2}\) Reforming of CH\({}_{4}\)
###### Abstract  
Ni, Co, and Co-Ni bimetallic catalysts supported over SBA-15 and over SBA-15 doped with Zn or Ce oxides were prepared and tested in a methane dry reforming reaction. The loading of the metals in the catalyst was 5 wt % for either mono or bimetallic catalysts. The prepared catalysts were tested in a continuous-flow fixed-bed reactor at 800 \({}^{\circ}\)C under atmospheric pressure. XRD, TPR, TPD, and SEM characterization techniques were employed to investigate the catalytic properties of fresh catalysts. SEM and TGA were used to study the catalytic properties of spent catalysts. A remarkable effect on the reduction properties and catalytic performance of catalysts was observed after adding Zn and Ce. Over an 8 h test, Ni/SBA-15 showed the best activity and stability. The conversion was 90% for CH\({}_{4}\) and CO\({}_{2}\). Co-Ni/SBA-15 and Co-Ni/Ce-SBA-15 have shown a reasonable activity and stability. Selectivity of the Ni/SBA-15 catalyst was higher than all other catalysts as indicated by the H\({}_{2}\)/CO ratio. Co/SBA-15 and Co-Ni/Zn-SBA-15 showed a low activity and selectivity. TPD-NH\({}_{3}\) profiles indicated that doping SBA-15 with Ce and/or Zn increased the catalyst acidic sites. Ni/SBA-15 is an excellent potential catalyst for commercial methane dry reforming processes.  
catalyst; CO\({}_{2}\); H\({}_{2}\); methane reforming; SBA-15 2019 1926 10.3390/app9091926
## 4 Materials and Methods
### Construction of Complementing Strains  
We generated a construct to overexpress _cdaA_, through amplification of the complete ORF (819-bp) of _cdaA_ plus 72-bp downstream of its stop codon by PCR using chromosomal DNA from _B. subtilis_ YB955, Vent DNA polymerase, and specific forward (5\({}^{\prime}\)-GCGCATGCATGGCTTTTGAGGATATCC-\(3^{\prime}\)) and reverse (5\({}^{\prime}\)-GCGTCGCACACGCACATAAAGCAAG-\(3^{\prime}\)) oligonucleotide primers that contained SphI and Sall restriction sites (underlined), respectively. The PCR product was first cloned into the pJET1.2/blunt vector and subsequently ligated into the SphI/Sall sites of the integrative vector pDR111 (Table 2). The resulting construct (pPERM1845; Table 2) was linearized and used to transform competent cells of strains _B. subtilis_ PERM1687 (_cdaA::cat_) and _B. subtilis_ PERM1665 (_cdaA::cat_; _disA::ery_).  
A construct to overexpress _disA_ was generated as follows. The full ORF of _disA_ (1080-bp) and 200-bp downstream of the stop codon was first amplified by PCR using chromosomal DNA from _B. subtilis_ YB955, Vent DNA polymerase and specific forward (5\({}^{\prime}\)-GCAGGTCGACATGGAAAAGAGAAAAACGG-\(3^{\prime}\)) and reverse (5\({}^{\prime}\)-GCAGGCTAGCGTACAAACAATTCAGGTATCA-\(3^{\prime}\)) oligonucleotide primers, bearing Sall and NheI restriction sites (underlined), respectively. After cloning into pJET1.2/blunt, the Sall/NheI PCR fragment was ligated into pDR111 previously digested with the same enzymes to generate the construct pPERM1728 (Table 2). This plasmid was linearized and transformed into competent cells of strains _B. subtilis_ PERM1647 (_disA::ery_) and _B. subtilis_ PERM1665 (_cdaA::cat_; _disA::ery_). Chromosomal DNA isolated from transformants colonies selected in medium with the appropriate antibiotics and specific oligonucleotide primers forward (5\({}^{\prime}\)-TTGAGCTCAATGGGGAAAGAAACCG-\(3^{\prime}\)) and reverse (5\({}^{\prime}\)-GATCAAAAGCGGAAC CATTCTTC-\(3^{\prime}\)), were used to determine by PCR, the correct insertion of the construct pPERM1845 (_Phs-cdaA_) and pERM1728 (_Phs-disA_) into the _amyE_ locus.
## 3 A New Approach for Bias Detection Combining Rule Mining and Regression Analysis  
To detect and investigate systematic biases in decisional processes, we propose to combine principles of rule mining with regression analysis. We note that a data sanitization process should start before the application of our methodology; for example, to identify highly correlated variables in a dataset that may create multicollinearity problems biasing estimated model coefficients. When two or more highly correlated variables are present, only one should be selected for inclusion in the analysis.  
Our methodology is summarized in Figure 1. First, association rule mining is employed to generate the set of relevant rules (1). For our purpose, we treat each mined rule as a candidate hypothesis for bias. In step (2), we generate regression models by considering the variables included in each selected rule and regress over them to generate the model estimates of the considered outcome variable. In step (3), a model comparison is performed to eliminate'redundant' models that do not add relatively more information to the prediction than a simpler model does. This leaves us with only 'winning' models that, among all evaluated candidates, provide the more convincing evidence for some effect, if any. Finally, in step (4), we extract the coefficients of these selected models to identify (sub)populations of interest for which there is statistical evidence of bias in the decision making. Then, we provide a detailed breakdown of each step of the methodology.
# Efficiency Analysis of Manufacturing Line with Industrial Robots and Human Operators
## 8 Conclusions  
As was expected, the simulation experiments confirm the advantage of applying an automated manufacturing line, as compared to a manually operated one. This can be seen in particular in the case of work in three shifts for a long period of time. Because the models were build based on OEE components, therefore similar manufacturing systems can be directly compared. The productivity of a manufacturing line operated by a robot has improved by about 30%, compared to a manually operated line. Also, the reliability of human operators and robots plays a significant role. The computer simulation of the detailed model of production line with machines, operators and robots with reliability parameters allows for better representation and understanding of a real production process which is important for early design and enables front-end planning.  
However, in other cases of tending machine tools, the difference between human operators and robots may not be clear to see. Therefore, the economic factors related to robot and labour costs also play an important role, because high investment costs are a major deterrent to robotization.  
Results obtained with the proposed method can be used for designing a similar robotic workcell and economic analysis regarding labour costs and costs associated with the investments in robotization. The use of OEE factors allows for comparing results from other manufacturing systems. The reality is that most manufacturing companies have OEE scores closer to 60%, but there are many companies with OEE scores lower than 50%, and a small number of best-in-class companies that have OEE scores higher than 70% [51].  
The proposed method can be also used for developing a digital twin of a manufacturing system for the purpose of Industry 4.0.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**2018** & **I** & **II** & **III** & **IV** & **V** & **VI** \\ \hline _Planned OFE_ & 80\% & 80\% & 80\% & 80\% & 80\% & 80\% \\ \hline _Obtained OFE_ & 77.30\% & 81.70\% & 77.30\% & 82.72\% & 78.82\% & 80.00\% \\ \hline
2018 & VII & VIII & IX & X & XI & XII & Avg. \\ \hline _Planned OFE_ & 80\% & 80\% & 80\% & 80\% & 80\% & 80\% & 80\% \\ \hline _Obtained OFE_ & 76.05\% & 73.29\% & 78.00\% & 77.01\% & 75.68\% & 76.35\% & 77.85\% \\ \hline \end{tabular}
\end{table}
Table 6: Planned and practical OFE values of robotic line (I-XII 2018).  
**Author Contributions:** Conceptualization, A.K. and P.B.; methodology, A.K. and G.G.; modelling, A.K.; validation, G.G.; formal analysis, G.G.; investigation, A.K.; data acquisition, P.B.; writing--original draft preparation, A.K.; writing--review and editing, A.K.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.