esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristic** & **Overall (n = 31)** & **Responders (n = 18)** & **Non-Responders (n = 13)** & _p Value_ \\ \hline Anthropometry & & & & \\ Height (m), mean (SD) & 1.71 (0.1) & 1.72 (0.1) & 1.71 (0.1) & 0.6591 \\ Weight (kg), mean (SD) & 77.9 (17.4) & 84 (18.1) & 69.5 (12.5) & **0.0291** \\ BMI (kg/m\({}^{2}\)), mean (SD) & 26.5 (4.8) & 28.5 (4.9) & 23.8 (3.2) & **0.0073** \\ BMI, n (\%) & 25.0 (81) & 12 (48) & 13 (52) & \\ \(\geq\)30 kg/m\({}^{2}\) & 6 (19) & 6 (100) & 0 (0) & \\ \hline CT \({}^{\text{a}}\) & & & & \\ \hline Sarocemic & 14 (56) & 6 (43) & 8 (57) & \\ Non-sarcopenia & 11 (44) & 11 (100) & 0 (0) & **0.0029** \\ SM (cm\({}^{2}\)/m\({}^{2}\)), mean (SD) & 135.2 (29.4) & 139.5 (32.5) & 126.0 (20.0) & 0.3983 \\ TAT (cm\({}^{2}\)/m\({}^{2}\)), mean (SD) & 125.21 (118.63) & 149.18 (131.2) & 74.28 (62.1) & **0.0018** \\ VAT (cm\({}^{2}\)/m\({}^{2}\)), mean (SD) & 172.2 (123.4) & 207.8 (131.1) & 96.7 (57.7) & **0.0113** \\ SAT (cm\({}^{2}\)/m\({}^{2}\)), mean (SD) & 191.0 (89.4) & 225.3 (86.2) & 118.1 (38.7) & **0.0010** \\ IMAT (cm\({}^{2}\)/m\({}^{2}\)), mean (SD) & 12.4 (10.1) & 14.5 (10.7) & 8.0 (7.7) & **0.0510** \\ VATSAT, mean (SD) & 0.88 (0.45) & 0.93 (0.47) & 0.77 (0.41) & 0.3983 \\ FFT (kg), mean (SD) & 46.6 (8.8) & 47.9 (9.8) & 43.0 (5.9) & 0.4025 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Anthropometry, body composition, physical activity, and dietary characteristics of patients with advanced melanoma. Data are presented for the entire cohort and both responders and non-responders. Differences between groups were evaluated via either [PERSON]’s test, Wilcoxon test, or Student’s _t_-test, as appropriate. \(p\) values of \(\leq\)0.05 are shown in bold. Flow cytometric analysis of absolute cell counts for innate and adaptive immune subsets was performed in 10 responders and 4 non-responders. Despite the small sample size, responders had significantly lower counts of neutrophils (3641 vs. 5258) and monocytic myeloid-derived suppressor cells (MDSCs) (118 vs. 272) than non-responders (\(p=0.04\), Student's \(t\) test).
# Project Robust Scheduling Based on the Scattered Buffer Technology
## 3 Literature Review of the Scattered Buffer Scheduling Algorithms
### Stc  
STC was proposed by [PERSON] et al. The idea of STC is to start with a schedule without buffer, and use iterative algorithm to insert successively the buffer times into the schedule to improve the schedule robustness gradually until buffer times cannot be inserted. STC method takes the activity weight, the activity time variability and the resources allocation into account. The key of this algorithm is to calculate the starting time criticality for each activity; it can be calculated by the equation below.  
\[\text{stc}(j)=P\big{(}\hat{s}_{j}>s_{j}\big{)}\times w_{j}=\gamma_{j}\times w_{j} \tag{8}\]  
\[\gamma_{j}=\sum_{(i,j)\in T(A\cup R)}P\Big{(}\hat{d}_{j}>s_{j}-s_{i}-LPL(i,j) \Big{)} \tag{9}\]  
where \(\gamma_{j}\) means the probability that activity \(j\) cannot start according to the predicated starting time; \(\hat{s}_{j}\) and \(s_{j}\) represent, respectively, the realized and predicated starting time of activity \(j\); \(\hat{d}_{j}\) is the realized duration of activity \(j\); and \(LPL(i,j)\) is the longest path between the activity \(i\) and the activity \(j\), that is, the maximum sum of the activity durations. The specific pseudo code of the STC is as follows.  
Procedure: The iteration step of the STC heuristic  
Calculate all stc(i)  
Sort activities by decreasing stc(i)  
**While** no improvement found **do**  
take next activity \(j\) from list  
**if**\(\text{stc}(j)\) = 0: procedure terminates  
**else** add buffer in front of \(j\)  
update schedule  
**if** improvement and feasible **do**  
store schedule  
goto next iteration step  
**else**  
remove buffer in front of \(j\)  
restore schedule  
The generation of the robust scheduling by STC needs simulation calculation. Therefore, for large-scale projects, this simulation computation takes too much time, and it has some limitations in application.
# Closed-Form Performance Analysis of the Inverse Power Lomax Fading Channel Model
## 3 IPL Fading Channel Model Application
### Outage Probability  
The outage probability is defined as the probability that the instantaneous SNR \(\gamma\) falls below the specified threshold value \(\gamma_{th}\):  
\[\mathrm{P}_{\mathrm{out}}(\gamma_{th})=\mathbb{P}\{\gamma\leq\gamma_{th}\}. \tag{21}\]  
**Theorem 3.**_The outage probability \(P_{out}\) for the IPL channel model and its high-SNR asymptotics are given by_  
\[\begin{cases}\mathrm{P}_{\mathrm{out}}(\gamma_{th})=\left(1+\frac{1}{\Omega_ {\alpha,\beta}}\left(\frac{\gamma}{\gamma_{th}}\right)^{\beta}\right)^{-\alpha },\\ \\ \mathrm{P}_{\mathrm{out}}(\gamma_{th})\bigm{|}_{\gamma\to\infty}\backsimeq\left( \Omega_{\alpha,\beta}\left(\frac{\gamma_{th}}{\gamma}\right)^{\beta}\right)^{ \alpha}.\end{cases} \tag{22}\]  
**Proof of Theorem 3.** The proof of Theorem 3 is trivially obtained by noting that \(\mathrm{P}_{\mathrm{out}}(\gamma_{th})=\mathbb{P}\{\gamma\leq\gamma_{th}\}=F_{ \gamma}(\gamma_{th})\) with the help of Lemma 1. \(\qed\)  
It is well known (see [55]) that under certain conditions, the outage probability can asymptotically be expressed as \(\mathrm{P}_{\mathrm{out}}\big{|}_{\gamma\to\infty}=\left(\mathcal{G}_{c} \tilde{\gamma}\right)^{-\mathcal{G}_{d}}\), where \(\mathcal{G}_{d}\) and \(\mathcal{G}_{c}\) are termed as the diversity and coding gains.  
**Corollary 1.**_The diversity and coding gains of the IPL channel model are expressed in terms of the channel parameters as follows: \(\mathcal{G}_{d}=\alpha\beta,\mathcal{G}_{c}=\Omega_{\alpha,\beta}^{-\frac{1}{ \beta}}\)._
# Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models
## 1 Introduction
### Contribution  
This paper presents a novel framework based on Rough Set Theory (RST) for feature selection, designed to enhance machine learning classifiers under uncertain and complex data conditions. Our work advances RST-based feature selection through three key innovations:  
* **Advanced RST Methods:** We introduce three RST-based algorithms--MLReduct, MLSpecialReduct, and MLFuzzyRoughSet--that outperform traditional methods like MLVarianceThreshold. MLSpecialReduct, in particular, achieves minimal attribute reduction while maintaining high accuracy.
* **Dynamic Dependency Optimization**: MLSpecialReduct uniquely combines incremental attribute evaluation with real-time redundancy checks, eliminating the need for postprocessing.
* **Hybrid Fuzzy Rough Handling:** MLFuzzyRoughSet introduces adaptive membership thresholds (Section 5.3.5), automatically tuning \(\alpha\)-cuts based on data distribution.
* **Computational Efficiency**: Our methods achieve 12% higher accuracy than tri-level reducts [15] while reducing runtime by 3\(\times\) (Section 5.4).
* **Statistical Robustness**: All results include 10-fold cross-validation with \(p\) < 0.01 significance.
* **Broad Applicability**: Validated on both private clinical data and public benchmarks (Section 5.5).
# Aerial Image Segmentation of Nematode-Affected Pine Trees with U-Net Convolutional Neural Network
## 2 Materials and Methods
### Methods
#### 2.2.4 Evaluation Systems  
In the image segmentation task, the objective is to ascertain whether the characteristics of a pixel in the image mimic those of a pine nematode-infested tree, while considering the influence of neighboring pixels. To address the challenge of segmenting remote sensing UAV images, we utilize four key metrics to evaluate segmentation accuracy. Mean Intersection over Union (_MloU_) gauges the precision of image segmentation by quantifying the overlap between the model's predicted segmentation and the ground truth. Mean Pixel Accuracy (_MPA_) reflects the average proportion of pixels accurately predicted by the model. Accuracy signifies the percentage of correctly categorized pixels across the entire image. The _F1 Score_, being the harmonic mean of Precision and Recall, is invaluable for evaluating the model's performance in handling both positive and negative instances. Additionally, the Confusion Matrix, a \(2\times 2\) structure, aids in computing the _F1 Score_ and outlines four possible outcomes for monitoring the pine nematode tree model during the prediction phase.  
True Positive (TP): a true example, indicating that the model correctly judges diseased tree pixel points (Positive) as diseased tree pixel points.  
True Negative (TN): true negative example, indicating that the model correctly judged other pixel points (Negative) as other pixel points.  
False Positive (FP): a False Positive example, indicating that the model incorrectly judges other pixel points as diseased tree pixel points.  
False Negative (FN): false negative example, indicating that the model incorrectly judged the diseased tree pixel point as other pixel points.  
To evaluate the segmentation accuracy of the model, we selected four metrics: _MloU_, _MPA_, _Accuracy_, and _F1 Score_. The mathematical expressions for these metrics are as follows:  
\[Accuracy=\frac{P_{tp}+P_{tn}}{P_{tp}+P_{fp}+P_{tn}+P_{fn}} \tag{4}\]  
\[F1\ Score=\frac{2\cdot P_{tp}}{\left(2P_{tp}+P_{fp}+P_{fn}\;\right)} \tag{5}\]\[MIoU=\frac{1}{N}{\sum\frac{P_{tp}}{P_{tp}+P_{fp}+P_{fn}}} \tag{6}\]  
\[MPA=\frac{1}{N}{\sum\frac{P_{tp}}{P_{tp}+P_{fp}}} \tag{7}\]
## 4 Coded Cooperation with Soft Information Relaying and Relay Selection
### Outage Probability Analyses of the CC-SIR-RS Scheme  
Referring to (19), (21), and (22), the SNR of the equivalent end-to-end channel \(S-R_{r}-D\), denoted by \(\Gamma_{SR,D}\), is calculated as follows:  
\[\Gamma_{SR_{r}D} = \frac{G_{SR,}\frac{1}{2^{R}_{rD}}\rho_{r}\
u\frac{R_{R}}{R_{S}} \frac{P_{SD}}{\sigma_{\pi}^{2}}|h_{SR_{r}}|^{2}|h_{R,D}|^{2}\Gamma_{R_{r}}}{G _{SR,}\frac{1}{2^{R}_{rD}}\rho_{r}\
u\frac{R_{R}}{R_{S}}\frac{P_{SD}}{\sigma_ {\pi}^{2}}|h_{SR_{r}}|^{2}|h_{R,D}|^{2}+\Gamma_{R_{r}}+1} \tag{27}\] \[= \frac{\Gamma_{R,D}\gamma_{SR_{r}}\gamma_{R,D}\Gamma_{R_{r}}}{ \Gamma_{R,D}\gamma_{R_{r}}\gamma_{SR_{r}}+\Gamma_{R_{r}}+1},\]  
where \(\Gamma_{R,D}=(G_{SR_{r}}/d_{R,D}^{\alpha})(R_{R}/R_{S})\rho_{r}\
u(P_{SD}/ \sigma_{n}^{2})\). It is worth noting that \(\gamma_{SR_{r}},\gamma_{R,D}\), and \(\Gamma_{R_{r}}\) are assumed to be independent and exponential distributed with parameters, \(\gamma_{SR_{r}},\gamma_{R,D}\), and \(\Gamma_{R_{r}}\), where \(\Gamma_{R_{r}}\) is obtained by averaging \(\Gamma_{R_{r}}\) in (20) over a sufficiently large number of blocks. The best relay of the CC-SIR-RS scheme is obtained by the following expression:  
\[R_{\text{opt}}^{\text{CC-SIR-RS}} = \arg\max_{R_{r},r=1,\cdots,n_{R}}\{\Gamma_{SR_{r}D}\}. \tag{28}\]  
Assuming constant PSR for each relay, then the outage probability of the CC-SIR-RS-CPS scheme is given by the following expression:\[p_{\text{out}}^{\text{CC-SIR-RS-CPS}} = P\Big{(}\underset{r=1,\cdots,n_{R}}{\max}\,\Gamma_{SR,D}<2^{2R}-1 \Big{)} \tag{29}\] \[= \prod_{r=1}^{n_{R}}\bigg{[}P\Big{(}\gamma_{SR_{r}}\gamma_{R,D}< \frac{\phi_{r}\Gamma_{R_{r}}+1}{\Gamma_{R_{r}}-(2^{2R}-1)}\Big{)}\bigg{]}\] \[= \prod_{r=1}^{n_{R}}\bigg{[}\int_{2^{2R}-1}^{\infty}F_{\gamma_{SR_ {r}}\gamma_{R,D}}\bigg{(}\frac{\phi_{r}x+1}{x-(2^{2R}-1)}\bigg{)}f_{\Gamma_{R_{ r}}}(x)dx\bigg{]}\] \[= \prod_{r=1}^{n_{R}}\int_{2^{2R}-1}^{\infty}2\sqrt{\frac{\phi_{r}x +1}{\tilde{\gamma}_{SR_{r}}\tilde{\gamma}_{R,D}(x-(2^{2R}-1))}}\.\] \[K_{1}\bigg{(}2\sqrt{\frac{\phi_{r}x+1}{\tilde{\gamma}_{SR_{r}} \tilde{\gamma}_{R,D}(x-(2^{2R}-1))}}\bigg{)}\frac{1}{\tilde{\Gamma}_{R_{r}}} \exp^{-\frac{x}{\tilde{\gamma}_{R_{r}}}}\ dx,\]  
where \(\phi_{r}=(2^{2R}-1)/\Gamma_{R,D}\). The evaluation of the integral in (29) is not a trivial. For this reason, we apply the Monte Carlo method [31], which leads to the following approximation:  
\[P_{\text{out}}^{\text{CC-SIR-RS-CPS}} \approx \prod_{r=1}^{n_{R}}\bigg{[}\frac{1}{M}\sum_{i=1}^{M}2\sqrt{\frac{ \phi_{r}x_{i}+1}{\tilde{\gamma}_{SR_{r}}\tilde{\gamma}_{R,D}(x_{i}-(2^{2R}-1) )}}K_{1}\bigg{(}2\sqrt{\frac{\phi_{r}x_{i}+1}{\tilde{\gamma}_{SR_{r}}\tilde{ \gamma}_{R,D}(x_{i}-(2^{2R}-1))}}\bigg{)}\bigg{]}, \tag{30}\]  
where \(x_{i},i=1,\cdots,M\), are \(M\) realizations generated with respect to the distribution \(f_{\Gamma_{R_{r}}}\) with \(x_{i}>2^{2R}-1\).
## 3 The Aerosol Size Distribution and Optical Constants  
The mathematical representations of particle size distribution functions studied here are shown below [11]:  
\[f_{\mathrm{L-N}}^{\mathrm{mono}}(D)=\frac{1}{\sqrt{2\pi D\ln\sigma}}\times \exp\left[-\frac{1}{2}\left(\frac{\ln D-\ln\overline{D}}{\ln\sigma}\right)^{2}\right] \tag{7}\]  
\[f_{\mathrm{Gamma}}^{\mathrm{mono}}\left(D\right)=D^{\mathrm{s}}\times\exp(- \beta D^{\gamma}) \tag{8}\]\[f_{\text{L-N}}^{\text{bi}}\left(D\right)=n\times\frac{1}{\sqrt{2\pi D\ln\sigma_{1}} }\times\exp\left[-\frac{1}{2}\Big{(}\frac{\ln D-\ln\overline{D}_{1}}{\ln\sigma_ {1}}\Big{)}^{2}\right]+(1-n)\times\frac{1}{\sqrt{2\pi D\ln\sigma_{2}}}\times \exp\left[-\frac{1}{2}\Big{(}\frac{\ln D-\ln\overline{D}_{2}}{\ln\sigma_{2}} \Big{)}^{2}\right] \tag{9}\]  
\[f_{\text{Gamma}}^{\text{bi}}\left(D\right)=n\times D^{\text{a}1}\times\exp(- \beta_{1}D^{71})+(1-n)\times D^{\text{a}2}\times\exp(-\beta_{2}D^{72}) \tag{10}\]  
where the superscript'mono' and 'bi' denote the monomodal and bimodal distributions; the superscript 'L-N' and 'Gamma' denote the Logarithmic Normal distribution and the Gamma distribution. The detailed description of these functions is available in our previous work [11]. To meet the need of optical measurement methods, the particle size range is set within \([0.001,10.0]\)\(\upmu\)m [24]. Table 1 lists the true values of the ASDs mentioned above.  
According to the standard radiation atmospheric model (SRA) of atmospheric aerosols, which was recommended by the radiation committee of the International Association of Meteorology and Atmospheric Physics (IAMAP) in 1983, the atmospheric aerosols with different composition were divided into six categories, i.e., dust-like aerosol, water-soluble aerosol, oceanic aerosol, soot aerosol, volcanic ash aerosol and sulfuric acid aerosol [25]. Later, [PERSON] and coworkers [26] proposed a new kind of global aerosol model and added the mineral aerosol into the model, which represents the aerosol of one-third of global terrestrial deserts and grassland (arid or semi-arid) areas. Figure 1 depicts the spectral optical constants of four kinds of common aerosols, i.e., dust-like aerosol, water-soluble aerosol, oceanic aerosol and soot aerosol, which are available in Reference [25; 26]. In the present work, the spectral optical constants are selected according to soot aerosol.
# Evaluation of Metamorphic Testing for Edge Detection in MRI Brain Diagnostics
## 3 Methodology
### Evaluation of Metamorphic Relations  
After the generation of follow-up test cases, both source and follow-up test cases are used as input to the SUT. The output data is generated by executing the IUT for each test case [7]. Evaluation of MR is performed by comparing the output relation between source and follow-up test cases. The satisfaction of MR shows the absence of faults, otherwise the SUT is faulty. However, if the MR satisfies all the test cases, then it is too weak to find the violation.  
In our study, we implemented our dataset using the first MR (counter-clockwise rotation at 90 degrees) and the rest of MRs will adopt the same information. The mathematical property of MR\({}_{1}\) is given below:  
\[\text{MR}_{1}\text{:}\text{C(E(Im))}=\text{E(C(Im))}\]  
where the following are defined:  
E = Edge detection program and SUT in this case;  
Im = Source test case and could be any image;  
C = Counter-clockwise rotation at 90 degrees;  
C(Im) = Follow-up test case, created by applying counter-clockwise rotation on the source test case.  
Figure 3 shows the source test case and follow-up test case of the respective MR.  
Now, both the test cases are given to the SUT. After applying the edge detection operation on the source and follow-up test cases, the output would be E(Im) and E(C(Im)), respectively. The outputs of source and follow-up test cases are shown in Figure 4.  
To balance the MR, apply counter-clockwise rotation on the output of source test case. Figure 5 shows the output of both source and follow-up test case after balancing the MR.  
Afterwards, the relation between the output of source test case and follow-up test case is checked by comparing output of both the images structurally by using SSIM. If we obtain a full black image after comparison, then the images are exactly similar, and there is no MR violation. However, if both the images are not similar, then it shows an MR violation.
## 2 The CDMA RoFSO System with Spatial Diversity
### Reverse Link  
On the reverse link, \(K\) CDMA user's signals arrive at the antenna base station at a specific time instant and the carrier to noise plus interference power ratio for each \(k\)th user (\(CNIR_{k}\)) in a P2P link is given as [19]:  
\[CNIR_{k,r,m}(I)\approx\frac{\left(m_{k,m}\rho_{m}L_{tot,m}P_{0}I_{m}\right)^{ 2}}{2\big{(}\langle N_{0,m}B\rangle_{AV}+\langle n_{NLD,r,k,m}\rangle_{AV} \big{)}}, \tag{5}\]  
where the subscript symbol \(r\) denotes the reverse link and all the parameters are the same as the aforementioned for the forward link except for the total nonlinear distortion on the reverse link, i.e., \(n_{NLD,r,k,m}\). The \(n_{NLD,rk,m}\) is a sum of the 3 rd order inter-modulation distortion (IMD3) of the reverse link, the clipping distortion given in (4) and the generated MAI due to the asynchronous transmission \(n_{NLD,r,k,m}=\sigma_{IMD,r,k,m}^{2}+\sigma_{cl,k,m}^{2}+\sigma_{MAI,k,m}^{2}\). The IMD3 for the case of the reverse link and the MAI are evaluated as [19]:  
\[\sigma_{IMD,r,k,m}^{2}=\frac{\left(m_{k,m}I_{ph,m}\right)^{2}}{2}\Bigg{[} \frac{\alpha_{3}m_{k,m}^{2}(2K-1)(K-1)}{8G_{p}}+\frac{\alpha_{3}^{2}m_{k,m}^{4} (K-1)}{64}\Big{(}9+\frac{252K^{2}+300K-648}{10G_{p}}\Big{)}\Bigg{]} \tag{6}\]\[\sigma_{MAI,k,m}^{2}=\left(m_{k,m}I_{ph,m}\right)^{2}\frac{K-1}{12G_{p}} \tag{7}\]  
In his analysis [PERSON] demonstrated that the amount of the MAI for an asynchronous spread spectrum system from \(K-1\) other active users is quantified as \(MAI\approx(K-1)/3G_{p}\)[18] (Equation (3.63)). Therefore, the process gain \(G_{p}\) is tha main parameter for the pseudo-random (PN) code sequences that is taken into account. A perception of the system model for both directions of the forward and the reverse link is illustrated in Figure 1.
# Graph-Based Modeling in Shop Scheduling Problems: Review and Extensions
## 3 The Theory of Shop Scheduling Problems
### Shop Layouts
#### 3.2.2 Job Shop System  
_Job shops_ (JS) are well-known manufacturing systems built for high flexibility and a wide range of applications. Machines within a classic job shop are fixed, unique, meaning they only have the capability of processing one type of operation, and may only process one operation at a time. All machines are available at time zero. Each job is independent of another and can follow a unique path through the machines to fulfill all operations,  
Figure 5: Shop layout classification.  
meaning routes of different jobs are not uniform. All jobs are available after release dates and operations cannot be interrupted.  
The _job shop scheduling problem_ (JSSP) is a frequent topic of research due to its NP hardness [46]. To model systems resembling realistic shop floors, the classic JSSP has been widely expanded upon with the additions of constraints such as blocking, introducing a zero-buffer capacity, [47; 48; 49], no-wait, and no-idle. Classic JSSP has also been expanded to account for distributed work centers, creating the _distributed JSSP_[50]. To model systems resembling realistic shop floors, the classic JSSP has been widely expanded upon with the additions of constraints such as blocking, introducing a zero buffer capacity, [47; 48; 49], and no-wait, meaning consecutive operations of a job must be performed without wait-time on either machine, [51]. Classic JSSP has also been expanded to account for distributed work centers, creating the _distributed JSSP_[50]. To model systems resembling realistic shop floors, the classic JSSP has been widely expanded upon with the additions of constraints such as blocking, introducing a zero buffer capacity, [47; 48].  
Machines in a _flexible job shop_ (FJS) may have the capability of doing more than one type of operation. The classic JSSP can be expanded to include this new attribute, creating the _flexible JSSP_[52]. While increasing flexibility, the inclusion of this attribute increases the complexity as well [53]. Flexible JSSP must now include the assignment of operations to capable machines as the first step, compared to previously classic JSSP assumed a predefined route for each job and a single option of machine to complete a given operation [54]. After that assignment, then flexible JSSP works similarly to JSSP such that a feasible and ideally optimal schedule is found based on predetermined objectives [19].  
The flexible JSSP can be broken down into two subcategories: still including the concept of a predefined route for a given job (operations must be done in a certain sequence), or removing that concept and introducing alternative routes [55].  
Flexible JSSP may come in many names. Such that JSSP with multi-purpose machines, [56], multi-processor JSSP [57], and JSSP with unrelated parallel machines [58].