esa-sceva/satcom-chunk-collection huggingface dataset:
# Industry 4.0 Maturity Model Assessing Environmental Attributes of Manufacturing Company
## 3 Materials and Methods
### Findings  
The first part of this article deals with a thorough search of the available literature on the main topics--environmental manufacturing and carbon footprint, cost aspects and industrial engineering methods, and maturity models assessing the readiness of companies for Industry 4.0. Based on the review, the following summary of findings was established:  
* By implementing environmental \"factors\" into production (processes), we can achieve sustainable development without degrading the environment;
* Materials recycling or substitution by alternative products (with the same function) is a way of green production;
* Using digital technologies can accelerate the reduction of global emissions;
* Carbon footprint is one of the most effective ways to calculate the impact of green manufacturing;
* Product carbon footprint is used to determine the effect products have on emissions and the environment;
* Environmental manufacturing, even if it represents a positive approach for the environment, leads to higher costs, leading to the need for governmental support;
* Maturity models assess a company's readiness for Industry 4.0 in several dimensions, which they classify into several defined levels;
* Environmental aspects are not assessed in maturity models, and they do not occur in dimensions, subdimensions, or even in criteria.  
Specifying the general objective mentioned at the end of the Introduction Section follows. The results from the state-of-the-art review clearly show [50] (which is also summarised in Table 1) that models for the maturity of companies for Industry 4.0 do not include environmental aspects of production, nor the interconnection of carbon footprint (as the main indicator of environmentally friendly production). Thus, it is essential to implement this into a maturity model. Since none of the existing models meet our criteria, a proposal for a new maturity model is presented here.
# A Cross-Layer Optimized Opportunistic Routing Scheme for Loss-and-Delay Sensitive WSNs
## 4 System Model  
### Network Model  
We consider a periodic data collection wireless sensor network [29]. In this network, a large number of sensor nodes are deployed randomly with density \(\rho\) in a circular area with a radius \(R\) to monitor continuously some preset activities. All the sensor nodes are same in the aspect of initial energy, and sending rate. Like in [61; 62], an opportunistic multi-hops routing protocol is used.  
A special node called sink locates at the center of the network, which is set for collecting the data generated by the entire network to achieve some specified function. Each sensor node monitors a nearby environment and generates a data packet in a sensing period. All the data packets will be sent directly or transmit by relay nodes in a multi-hop style to the sink unless losses or errors happen.  
In addition, we assume the MACA protocol being included in MAC layer to mitigate the hidden station problem and exposed station problem [62].  
Figure 8: End-to-end reliability in opportunistic routing.  
A successful transmit is performed if both data and ACK packets are received without errors by the intended recipients [62]. If either of them has an exception, the transmission is regarded as failed. When a failure appears, retransmission will not be performed. The sender node will reorganize the data packet and transmit it in the next round.
## 3 Results and Discussion
### Dielectric Properties
On one hand, it exhibits the highest dielectric permittivity, but on the other hand, it also shows an increase in grain size non-uniformity. In order to compare the influence of gadolinium admixture on the properties of Bi\({}_{7}\)Fe\({}_{3}\)Ti\({}_{3}\)O\({}_{21}\) ceramics, the temperature profiles of the real part of the electric permittivity measured at several selected frequencies of the measurement field were also analyzed (Figure 7). Analyzing the above characteristics, it can be seen that there is no phase transition for all tested materials in the entire temperature range and that with the increase in the frequency of the measuring field \(f\), the value of the maximum of the real part of the electric permittivity \(\varepsilon^{{}^{\prime}}_{\max}\) decreases, while moving towards higher temperature values. The systematic shift of the maxima \(\varepsilon^{{}^{\prime}}_{\max}\) in the direction of increasing temperature values (along with the strengthening of the frequency of the measurement field _f_) represents dielectric relaxation [27]. The literature data show that for Aurivilius phases, dipole disorder and relaxation properties are related to order errors in the sequence of perovskite-like layers. Further analyzing the characteristics from Figure 7, it is noticed that for low frequencies, with the increase in the content of gadolinium ions, the value of the real part of the electrical permittivity increases significantly (especially for the dopant x = 0.6). For high frequencies, the changes are small. In order to determine the quantitative impact of the gadolinium \begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\mathbf{Bi_{7-x}Gd_{x}Fe_{3}Ti_{3}O_{21}}\) \\ \(x\) & \(\mathbf{\textit{T}_{max}}\) [K] & \(\varepsilon^{{}^{\prime}}_{max}\) \\ \hline
**0** & 524 & 3623 \\ \hline
**0.2** & 579 & 1312 \\ \hline
**0.4** & 498 & 2674 \\ \hline
**0.6** & 529 & 6835 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Electrical parameters measured in the measurement field f = 1 kHz for Bi\({}_{7-x}\)Gd\({}_{x}\)Fe\({}_{3}\)Ti\({}_{3}\)O\({}_{21}\) ceramic materials for x = (0, 0.2, 0.4, 0.6). admixture on the dispersion, both for the temperature and the dispersion of the real part of the permittivity, formulas \(\Delta\)T\({}_{\text{max}}\) = T\({}_{\text{max}}\)(1000 kHz) \(-\) T\({}_{\text{max}}\)(1 kHz) and \(\Delta\)\(\epsilon^{\prime}\)\({}_{\text{max}}\) = \(\epsilon^{\prime}\)\({}_{\text{max}}\)(1 kHz) \(-\)\(\epsilon^{\prime}\)\({}_{\text{max}}\)(1000 Hz) were used. Table 5 summarizes the information read from the characteristics shown in Figure 7.  
Although there is a minimum on the \(\varepsilon^{\prime}\)(T) curves, the value of which depends on the frequency, the corresponding temperature does not shift to higher values with the increase in the frequency of the measurement field. This fact is most likely related to the decrease in the space charge density caused by the introduction of a homovalent dopant.
## 3 Results and Discussion
### DSC Measurements of PDMSB Homopolymers  
For a study of the melting and crystallization behavior of PDMSB homopolymers, samples were heated with a rate of 10 K min\({}^{-1}\) to 353 K, and the temperature was kept constant for 10 min to delete thermal history. Then, the sample was cooled to 253 K with a rate of 10 K min\({}^{-1}\) to obtain the DSC exotherms, followed by heating again to obtain the DSC endotherms. In Figure 3, the DSC curve of PDMSB with an average molar mass of 5.6 kg mol\({}^{-1}\) is exemplarily given.  
Figure 2: Exemplary SEC-MALLS trace (**a**) and molar mass distribution (**b**) of PDMSB obtained by SEC-MALLS measurement synthesized via anionic ROP in THF at \(-\)60 \({}^{\circ}\)C.  
From Figure 3, it becomes clear that only one crystallization peak in the vicinity of 283 K during the cooling process was apparent. However, subsequent heating showed that there are unambiguously two melting peaks at 295 K and another at 303 K. Similar multiple melting peak phenomena have been reported in studies with low molar mass PEO homopolymers. In those investigations with PEO, the untypical thermal behavior is known as the integral folding (IF) and nonintegral folding (NIF) structure. [PERSON] _et al._ pioneered this issue and studied the relationship between linear growth rate, single crystal morphology and the IF structure [41; 42; 43; 44; 45]. [PERSON] _et al._ demonstrated the existence of the NIF structure using time resolved small-angle X-ray scattering (SAXS) [46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56]. At first glance, it seems that a similar mechanism might play a role for low molar mass PDMSB homopolymers, but further investigations are necessary, as described in the following section.
## 3 Environmental Conditions and Buoy Modeling
### Environmental Loads and Conditions  
An ocean buoy experiences various environmental forces, including waves, winds, and currents [32; 33]. This study investigated how BS motion affects wave loads across different sea conditions. We assumed that the wind and current velocities remain constant across sea states, with wind speed set at 10 m/s and current speed around 1 m/s near the water surface. Generally, wind and current loads are considered to be static loads, and this approach was applied in this simulation. Different wind and current loads cause the position of the buoy to change. However, in the case of multiple buoys, assuming that the loads between buoys are the same, the change in their relative position is not significant. Wave forces are dynamic and are calculated using [PERSON]'s equation, which separates the drag and inertia components [49]. To ensure wide applicability, we utilized the wave spectrum defined by the WMO ranging from sea state 3 (SS3) to sea state 8 (SS8), which was originally developed for assessing ship performance in various environmental conditions. The WMO sea state codes are further described in Table 1 in Appendix B. In addition, we incorporated the Joint North Sea Wave Project (JONSWAP) spectrum into our analysis, with a peak enhancement factor of 3.3 [50].
# Indoor Wavelet OFDM VLC-MIMO System: Performance Evaluation
## 1 Introduction
### State of the Art Regarding FFT-OFDM and DWT-OFDM
The cyclic prefix (CP) is added before data transmission to minimize the inter-symbol-interference/inter-channel-interference (ISI/ICI) and to increase the bandwidth efficiency. Furthermore, CP reduces the channels of spectral inclusion. Wavelet (WT) fairly reflects a new term. It is used instead of FFT in OFDM. Wavelet-OFDM is named by orthogonal wavelet division multiplexing (OWDM), which is related to the inverse discrete wavelet transform (IDWT) rather than inverse discrete Fourier transform (IDFT) [13; 14]. The great reason for utilizing DWT based on the OFDM system is the length of the basic functions which combat the narrowband interference better and inherently further resistant to ICI. [PERSON] et al. [15] studied the overview and performance estimation results of OFDM techniques including the traditional-OFDM and wavelet-OFDM. The authors in [16] proposed a new wavelet-OFDM analytical system which confirms Wi-Fi standard hiring the transmitter's windowing function (rectangular waveform). Authors in [17] investigated a wavelet-OFDM for 4G of wireless communication. [PERSON] et al. [18] elaborated the FFT-OFDM performance technique in opposite to WT based multicarrier system utilizing a simple zero-forcing (ZF) equalization in the time domain. Moreover, the wavelet has been applied in other different fields such as civil structures [19; 20]. The Meyer WT is both orthogonal and symmetric [14]. This provides a numerical approach to the DWT/IDWT through the use of a low pass filter (LPF)/high pass filter (HPF). To implement a wavelet transformation, these filters must satisfy the orthonormal basis, which means that they must be normalized and orthogonal to each other. [PERSON] et al. utilized (16-QAM, 32-QAM, 64-QAM, and 128-QAM) and different types of wavelet filters Daubechies (Db-3, Db-5, Db-8, Db-10) and Haar filter to compare the performance between the FFT-OFDM and WT-OFDM [21].
## 3 Results and Discussion
### Differential Scanning Calorimetry of Margarine
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **10 \({}^{\circ}\)C** & **20 \({}^{\circ}\)C** & **30 \({}^{\circ}\)C** & **35 \({}^{\circ}\)C** \\ \hline \multirow{8}{*}{BWO} & 1 & 21.59 \(\pm\) 0.10 \({}^{\text{a}}\) & 9.75 \(\pm\) 0.30 \({}^{\text{a}}\) & 4.31 \(\pm\) 0.10 \({}^{\text{a}}\) & 2.13 \(\pm\) 0.02 \({}^{\text{a}}\) \\  & 2 & 18.35 \(\pm\) 0.00 \({}^{\text{c}}\) & 7.36 \(\pm\) 0.10 \({}^{\text{c}}\) & 3.93 \(\pm\) 0.08 \({}^{\text{b}}\) & 2.09 \(\pm\) 0.00 \({}^{\text{b}}\) \\  & 3 & 16.14 \(\pm\) 0.10 \({}^{\text{e}}\) & 6.11 \(\pm\) 0.10 \({}^{\text{d}}\) & 3.69 \(\pm\) 0.09 \({}^{\text{c}}\) & 2.04 \(\pm\) 0.01 \({}^{\text{c}}\) \\  & 4 & 15.21 \(\pm\) 0.10 \({}^{\text{f}}\) & 5.51 \(\pm\) 0.09 \({}^{\text{e}}\) & 3.20 \(\pm\) 0.10 \({}^{\text{d}}\) & 2.01 \(\pm\) 0.01 \({}^{\text{d}}\) \\  & 5 & 11.31 \(\pm\) 0.10 \({}^{\text{h}}\) & 4.31 \(\pm\) 0.10 \({}^{\text{f}}\) & 2.90 \(\pm\) 0.06 \({}^{\text{e}}\) & 1.93 \(\pm\) 0.02 \({}^{\text{e}}\) \\  & 6 & 8.94 \(\pm\) 0.30 \({}^{\text{i}}\) & 4.01 \(\pm\) 0.00 \({}^{\text{g}}\) & 2.89 \(\pm\) 0.04 \({}^{\text{e}}\) & 1.92 \(\pm\) 0.01 \({}^{\text{e}}\) \\ \hline \multirow{8}{*}{HBO} & 2 & 20.34 \(\pm\) 0.10 \({}^{\text{b}}\) & 9.28 \(\pm\) 0.20 \({}^{\text{a}}\) & 3.85 \(\pm\) 0.09 \({}^{\text{b}}\) & 1.80 \(\pm\) 0.06 \({}^{\text{f}}\) \\  & 3 & 17.28 \(\pm\) 0.30 \({}^{\text{d}}\) & 8.31 \(\pm\) 0.10 \({}^{\text{b}}\) & 2.97 \(\pm\) 0.07 \({}^{\text{e}}\) & 1.20 \(\pm\) 0.03 \({}^{\text{g}}\) \\ \cline{1-1}  & 4 & 13.40 \(\pm\) 0.20 \({}^{\text{g}}\) & 5.58 \(\pm\) 0.09 \({}^{\text{e}}\) & 2.84 \(\pm\) 0.10 \({}^{\text{e}}\) & 1.24 \(\pm\) 0.03 \({}^{\text{h}}\) \\ \cline{1-1}  & 5 & 3.50 \(\pm\) 0.20 \({}^{\text{j}}\) & 0.80 \(\pm\) 0.10 \({}^{\text{h}}\) & 0.00 \(\pm\) 0.00 \({}^{\text{f}}\) & 0.00 \(\pm\) 0.00 \({}^{\text{i}}\) \\ \cline{1-1}  & 6 & 0.90 \(\pm\) 0.08 \({}^{\text{k}}\) & 0.00 \(\pm\) 0.00 \({}^{\text{i}}\) & 0.00 \(\pm\) 0.00 \({}^{\text{f}}\) & 0.00 \(\pm\) 0.00 \({}^{\text{i}}\) \\ \hline \hline \end{tabular} Different small letters between rows show a significant difference between samples (\(p\leq\) 0.05). Data are presented as mean \(\pm\) standard deviation.  
\end{table} 
Table 2: Solid fat content (SFC) of margarine prepared with BWO and HBO.
# A Method for Identifying the Mood States of Social Network Users Based on Cyber Psychometrics
## 3 Materials and Methods
### The Construction and Evaluation of the Feature Set  
This research collected 52 features of the Weibo users and divided them into four categories, which are listed in Table 3. The first category, denoted as \(D_{i}\), covers basic information about the users, including the length of Weibo ID, gender, registration date, number of labels, and length of personal profile. The second category, denoted by \(S_{j}\), contains the number of followers, number of follows, and total tweets, demonstrating the social characteristics of users. The third category reveals prosperities of users' tweets, including the average length of tweets, average times for the use of the @ signal, monthly average, number of tweets, and other related features. These features are denoted as \(T_{m}\). The last category indicates the sequential sentiment features of Weibo users' blog text, including the sentiment-related features of tweets in five specific periods, denoted as \(M_{n}\). The five periods include the last week, month, 3 months, 6 months, and 1 year. Therefore, the users' feature set can be represented as: MoodStates \(=\{\overline{Di,S}\},\overline{Tm,\overline{Mn}\}\).  
The Pearson correlation coefficient was used in the process of feature extraction and was calculated as follows:  
\[\rho=\frac{\sum_{i=1}^{n}\left(X_{i}-\overline{X}\right)\left(Y_{i}-\overline{ Y}\right)}{\sqrt{\sum_{i=1}^{n}\left(X_{i}-\overline{X}\right)^{2}}\sqrt{\sum_{i=1}^{n} \left(Y_{i}-\overline{Y}\right)^{2}}} \tag{3}\]where \(\varrho\) represents the degree of correlation between the Weibo users' features and their sentimental scores. If \(\rho>0\), it means that the higher the value of features, the higher would be the user's sentimental score. The larger the value of \(\varrho\), the larger the growth rate of their sentimental scores.
# Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review
## 3 Deep Learning-Based Studies for CRC Diagnosis
### Polyp Detection
The original ResNet-50 model was also modified by using DropBlock regularization [77] at deeper layers to make it robust towards noise and artifacts. With extensive experiments using both datasets, the optimal values for precision, recall, and F1-score were found to be 0.932, 0.928, and 0.93, respectively. Another study in [78] created an endoscopic dataset from different sources and annotated the ground truths by collaborating with experienced gastroenterologists. Due to the severe differences in the existing datasets in terms of image resolution and color temperature (possibly due to different imaging equipment setups), the authors built a new dataset to serve as a benchmark to train and evaluate the DL models for polyp detection and classification. The new dataset included multiple publicly available endoscopic datasets as well as some independently collected from the University of Kansas Medical Center. Due to the extreme imbalance among the total number of image frames in each dataset, an adaptive sampling rate was utilized to homogenize the representativeness of each polyp by extracting important frames from the video. In total, 116 training, 17 validations, and 22 testing sets were generated, each comprising of 28,773, 4254, and 4872 frames, respectively. By using these datasets, eight of the most popular state-of-the-art object detection models were evaluated. These included Faster RCNN [72], YOLOv3 [79], SSD [80], RetinaNet [81], DetNet [82], RefineDet [83], YOLOv4 [84], and ATSS [85]. Using these frameworks, three different types of experiments were conducted: first, frame-based one-class polyps detection, second, two-class polyps detection, and the third, sequence-based two class polyp detection. For the two frame-based detection experiments, the performance was measured by regular object detection metrics, while for the sequence-based detection, regular object detection was applied to each frame. Finally, the voting procedure was applied to pick the mostly predicted polyps. For both the frame-based and sequence-based detection methods, RefineDet performed very well with an F1 score of 88.6, and 86.3, respectively. Other similar studies published recently on polyp detection using endoscopy images samples are listed in Table 5.
## 4 Performance Analysis and Simulation Results
### Performance Evaluation and Discussion
Note that the analysis presented is based on the assumption that a good PAPR reduction technique is available, and a lot of related studies are going on [24]. To find the optimum operating strategy between two schemes, we define the threshold value, \(J\) as follows:  
\[J=EE_{r}^{RPC}-EE_{r}^{ITXP} \tag{21}\]  
When \(J\geq 0\), we can choose the RPC. Otherwise we can choose ITXP for the optimum operation. Figure 5 present \(J\) versus \(P_{PA}\), and the cross over point is not so much different even though we increase the \(P_{PA}\). In Table 3, we present points of the \(J=0\). Figure 5: \(J\) versus \(P_{PA}\), (**a**) ZF precoding, (**b**) MF precoding, when \(N_{t}=400\), \(K=40\). Lines are based on analysis from Equations (19) and (20), and red ‘+’s are simulation results. In the case of ZF precoding, if \(P_{PA}\) takes more than 25.5-32.9 when \(P_{tot}\) = 100, we should choose RPC, otherwise we should choose ITXP for the optimum EE operation. In the case of the MF precoding, if \(P_{PA}\) takes more than 12.52-12.74 when \(P_{tot}\) = 100, we should choose RPC, otherwise we should choose ITXP for the optimum EE operation. For example, when \(P_{PA}\) = 33 with a PAPR reduction =12 dB and ZF precoding, it is better to choose PRC because \(J>0\) at that point. The difference of \(J=0\) points among various PAPR reduction is not so significant, especially in the case of MF precoding. Also, in the case of MF precoding, \(P_{PA}\) for \(J\) = 0 points is smaller than that in the case of ZF precoding due to the fact that ITXP does not improve so much compared to the EE performance of the MF precoding relative to that of the ZF precoding because the interference also increases as the TX power increases. Since the analytical results are well-matched with the results of simulation, we can use the analytical results for \(J\) to determine the EE optimized operational scheme. To show the characteristic of the decision threshold, \(J\), we present three 3D graphs in Figure 6. As observed in Figure 6a, as \(P_{PA}\) and/or PAPR reduction increases, \(J\) also increases. It is also noteworthy that the proposed method is not affected by the variations in the parameters. As we can see from (b) and (c) of Figure 6, even though \(N_{t}\) and \(K\) change, the thresholds do not change.
# Resource Allocation for Cognitive LEO Satellite Systems: Facilitating IoT Communications
## Appendix A Proof of Lemma 1  
It is difficult to prove Lemma 1 directly, but we can use the proof by contradiction. Based on (19), we have  
\[\frac{\gamma_{1,s}}{\gamma_{2,s}}=\frac{p_{1}}{p_{2}}\frac{\frac{a_{2}p_{2}}{ 1+\gamma_{2,s}}+\frac{a_{1}p_{  
For \(q_{1}>q_{2}\), if we assume \(\gamma_{1,s}\leq\gamma_{2,s}\), then, we have the following analysis.  
It is obvious that  
\[\frac{\alpha_{2}}{1+\gamma_{2,s}}-\frac{\alpha_{2}}{1+\gamma_{1,s}}\leq 0. \tag{10}\]  
Based on (21), it is difficult to find out the relation between \(p\) and \(q\) directly. Thus, (21) is deformed as  
\[\frac{1}{\beta^{*}}=\frac{\alpha_{1}}{1+\beta^{*}}+\frac{\alpha_{2}}{\frac{q} {p}+\beta^{*}}+\frac{\sigma^{2}}{q}. \tag{11}\]  
From (11), it is obvious that if \(p\) decreases with \(q\), then Equation (11) is impossible to be satisfied. That is to say, if \(q_{1}>q_{2}\), then, we have \(p_{1}>p_{2}\). Thus,  
\[\frac{\gamma_{1,s}}{p_{1}}-\frac{\gamma_{2,s}}{p_{2}}<0. \tag{12}\]  
Since  
\[\frac{q}{p}=\frac{\alpha_{2}+\frac{\sigma^{2}\beta^{*}}{q}+\frac{\alpha_{1} \beta^{*}}{1+\beta^{*}}-1}{\frac{1}{\beta^{*}}-\frac{\sigma^{2}}{q}-\frac{ \alpha_{1}}{1+\beta^{*}}},\]  
it is easy to see that \(q/p\) decreases with \(q\), i.e., \(q_{2}/p_{2}>q_{1}/p_{1}\). Thus,  
\[\frac{q_{2}}{p_{2}}\gamma_{2,s}>\frac{q_{1}}{p_{1}}\gamma_{1,s},\]  
which gives  
\[\frac{\alpha_{1}}{1+\frac{q_{2}}{p_{2}}\gamma_{2,s}}-\frac{\alpha_{1}}{1+ \frac{q_{1}}{p_{1}}\gamma_{1,s}}<0. \tag{13}\]  
Based on (10), (12), (13), for \(q_{1}>q_{2}\), if \(\gamma_{1,s}\leq\gamma_{2,s}\), then Equation (10) is impossible to be satisfied. Thus, for \(q_{1}>q_{2}\), we \(\gamma_{1,s}>\gamma_{2,s}\), which proves Lemma 2.