esa-sceva/satcom-chunk-collection huggingface dataset:
# Multivariate Statistical Process Control Using Enhanced Bottleneck Neural Network
## 3 Case Study : Wastewater Treatment Plant (WWTP) Monitoring
### Real Process Case  
In order to illustrate the effectiveness of the proposed strategy, the monitoring approaches have been tested on real data collected from an actual wastewater treatment process of Annaba, situated in the North-East Algeria (450 km of Algiers and 8 km east of Annaba city), where the real data contains 1300 observations of some sensors (see Table 2) in normal operating conditions, with a medium flow of 83,620 m\({}^{3}\)/day and peak flow during dry weather 5923 m\({}^{3}\)/h.  
The purification process used is activated sludge, and has two principal treatment sectors; the primary includes bar racks, grit chamber, de-oiling, and sand filters, whose objective is the removal of solids. The secondary sector is made for removing the organic matter present in the influent wastewater, where the organic matter pollutants serve as food for microorganisms. This is done by using either aerobic or anaerobic treatment processes. The schematic of the used actual plant is shown in Figure 6. The monitored sensors in this work are given in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**N** & **Monitored Sensors** & **Notation** \\ \hline
1 & Dissolved oxygen in influent & \(S_{O}\) \\
2 & Nitrites in influent & \(S_{NO2}\) \\
3 & Nitrates in influent & \(S_{NO3}\) \\
4 & Ammoniacal nitrogen in influent & \(NH_{4}\) \\
5 & Chemical oxygen demand in influent & \(COD\) \\
6 & Dissolved oxygen in effluent & \(S_{O}\) \\
7 & Nitrites in effluent & \(S_{NO2}\) \\
8 & Nitrates in effluent & \(S_{NO3}\) \\
9 & Ammoniacal nitrogen in effluent & \(NH_{4}\) \\
10 & Chemical oxygen demand in effluent & \(COD\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The monitored sensors for the real process.  
Figure 6: Schematic of Annaba wastewater treatment plant.
# Temperature Load Mode of Bridge in Permafrost Region of Qinghai-Tibet Railway
## 6 Temperature Effect Analysis  
The simulation model of CWR on a five-span 32 m bridge was established by using ANSYS software, as shown in Figure 15. The beam adopts a double-piece T-shaped concrete simply supported beam of the Qinghai-Tibet railway, and the rail adopts a U75V60 rail. The fixed support is located on the left side of the simply supported beam, and 100 m long subgrade sections are set on both sides of the bridge span to eliminate the influence of the boundary. The beam-rail interaction model has been verified by the example in UIC 774-3. The daily temperature difference of the bridge is taken as 17.8 \({}^{\circ}\)C of the maximum daily beam temperature difference in the Za'gya Zangbo region. The stress and displacement distribution of the CWR on the bridge are shown in Figure 16.  
Figure 14: Diurnal variation in beam section temperature field in the Tuotuhoe region.  
Figure 15: Mechanical model diagram of CWR on the bridge.  
Since the fixed pier is located on the left end and the bridge expands and deforms to the right, the rail near the movable support of the right abutment bears the maximum additional temperature pressure of 103.3 kN, and the rail near the left abutment bears the maximum temperature tension of 85.5 kN. On each bridge span from left to right, the rail's additional pressure gradually increases and the additional tension gradually decreases, indicating that the beam-rail interaction caused by bridge expansion gradually accumulates to the right. The displacement distribution of each span bridge is almost the same, increasing linearly from the fixed end to the movable end, and the maximum longitudinal displacement is 5.0 mm. The maximum longitudinal displacement of the rail occurs in the middle of the bridge, and the displacement is 2.2 mm. The maximum relative displacement of beam and rail is 3.4 mm, which appears at the right abutment. It can be seen that under the conditions of strong radiation and large temperature difference in the Qinghai-Tibet railway area, the bridge produces frequent telescopic displacement, which can easily lead to the attenuation of the performance of rail fasteners and bridge bearings and has an adverse impact on the durability of track structure.
# Experimental and Numerical Investigations on the Slate Shearing Mechanical Behavior
## 6 Conclusions and Prospects  
### Conclusions  
In this study, indoor and simulated direct shear tests were carried out on layered slate specimens under varying normal stresses and bedding dip angles. The strength parameters, shear failure mode, AE characteristics, and micro-mechanism were thoroughly analyzed. The main conclusions drawn from the obtained results are as follows:  
The \(\tau_{\rm p}\) increases linearly with \(\sigma_{\rm n}\), but the rate of increase is inversely related to \(\beta\). Slate cohesion positively correlates with \(\beta\), while \(\varphi\) and AE activity negatively correlate. The energy release rate is 3.92 \(\times\) 10\({}^{8}\) aJ/s at 0\({}^{\circ}\) and 1.93 \(\times\) 10\({}^{8}\) aJ/s at 90\({}^{\circ}\), with the least energy released by microcracks with vertical bedding.  
As \(\sigma_{\rm n}\) increases, lateral cracks become more pronounced, and compression force chain (FC) distribution becomes more uniform and denser. Cracks along the inclined bedding plane form a wide shear band (mainly compression FC) and often a dense tensile chain along the bedding plane.  
The shear strength variation of layered slate under numerical and experimental conditions differs significantly from shale due to macro and micro factors. Existing theoretical formulas should be used cautiously to predict slate strength.
## 3 Results and Discussion
### Soil
#### 3.1.4 Ammonium  
Measurements of ammonium in the soil samples for the five soil depths (0-5, 5-10, 10-20, 20-40, and 40-60 cm) with the three nitrogen doses (20, 40, and 60 kg N ha\({}^{-1}\)) for each type of tillage (NT and CT) are presented in Table 7, and ANOVA results are reported in Table 6.  
ANOVA (Table 6) showed no significant difference between the three nitrogen doses (F value = 0.60 and _p_-value = 0.573), with mean values of 8.7, 8.3, and 8 mg kg\({}^{-1}\) for the 20, 40, and 60 kg N ha\({}^{-1}\) doses, respectively. Tillage type (F value = 325.14 and _p_-value = 0.003) and soil depth (F value = 5.94 and _p_-value = 0.001) were differed significantly. Mean ammonium content was much higher under NT (10.6 mg kg\({}^{-1}\)) compared to CT (6.0 mg kg\({}^{-1}\)). [PERSON]'s post hoc test identified three groups of depths: 0-5 cm, with a mean value of 10.1 mg kg\({}^{-1}\); 5-10, 10-20, and 20-40 cm, which did not significantly differ, with mean values of 8.3, 7.9, and 8.1 mg kg\({}^{-1}\), respectively; and 40-60 cm, with a mean value of 7.3 mg kg\({}^{-1}\). Ammonium content at the soil surface was higher in NT than in CT, with an advantage under 40 kg N ha\({}^{-1}\) treatment, in agreement with studies evaluating the effect of N dose [27] or N form [28; 29]. With respect to total nitrogen, all interactions between the three factors were statistically significant.
## 2 Literature Review
### Method and Case Study Selection  
Among the useful and effective methods stated in the previous section, the DEA approach was selected in this study to enforce and increase the efficacy of the traditional FMEA method, with the intention of covering the mentioned shortcomings: (i) The DEA model ensures the assignment of different weights to each risk factor to cope with the shortcomings. (ii) The time and cost required to solve each failure mode will be considered as additional risk indicators and will be used as parameters for the DEA model and deal with the shortage. (iii) Sub-failure modes and their interrelationships will be considered and synthetized using an aggregation procedure to handle the deficiency, and (iv) a combination of outputs and inputs that are different from those found in the literature will be used to address the efficiency limitation. The traditional RPN number has been used since the ranking of the failures will rely on the output of the efficiency measurement and not the RPN ranks. Other improved versions of RPN calculation can definitely be used, such as the exponential RPN, the partial risk map (PRISM), or the action priority (AP).  
Why has RFID been selected as an illustrative example of the proposed method?  
The fourth industrial revolution, or Industry 4.0, gains advantages every day. As a result of the Internet of Things and cloud technology, everything has become smart and linked together. Radio frequency identification technologies play a critical role in enabling the ease of these connections. Moreover, RFID helped to improve the efficiency and effectiveness of various processes, services, and products in different organizations. This technology became popular by the 1970s, and since then, we have been surrounded by it wherever we go. Every person has more than one RFID tag in their wallet, for example, a control access card, a passport, a bank card, an ID card, etc. This means that these small parts play crucial roles in our daily lives. Therefore, ensuring the proper functioning of this technology is of significant interest. For all of these reasons, this study used combined FMEA and DEA to reach an appropriate priority rank for failures of an RFID system, which helped to decrease the amount of time and money wasted, and increased the system's efficiency, safety, and reliability.  
The framework of this paper is presented in Figure 1. First, the modified FMEA is applied, followed by an aggregation procedure to combine the sub-failure modes of each parent failure mode. Thereafter, the DEA model used the outcomes of the aggregation procedure as inputs/outputs for the efficiency measurement; then, a final rank is assigned to each failure mode. The calculations are explained in Section 2.  
Figure 1: The algorithm of the aggregated FMEA–DEA method.
# Integration of Production Planning and Scheduling Based on RTN Representation under Uncertainties
## 2 Mathematical Model
### Scheduling Layer Model
#### 2.2.1 Deterministic Model
Time constraints:  
\[T^{s}(i,n)\leq H\ ;i\in I_{r}\,\,n\in N. \tag{28}\]  
\[T^{f}(i,n)=T^{s}(i,n)+\alpha_{i}\cdot w(i,n)+\beta_{i}\cdot b(i,n);i\in I_{r}\, \,n\in N. \tag{29}\]  
Constraint (28) represents that the start time \(T^{s}(i,n)\) of tasks \(i\) at the event point \(n\) should be less than or equal to the scheduling horizon \(H\). In Constraint (29), \(\alpha_{i}\cdot w(i,n)\) and \(\beta_{i}\cdot b(i,n)\) respectively represent the constant term and variable term of task duration time, namely the end time \(T^{f}(i,n)\) of tasks \(i\) at the event point \(n\) should be equal to the duration time of the task plus the start time of the task. 6. Sequence constraints:  
a. Same task in the same unit:  
\[T^{s}(i,n+1)\geq T^{f}(i,n)\ ;i\in I_{r}\,\,n<N\,\,n\in N. \tag{30}\]  
Constraint (30) represents that the start time of tasks \(i\) at the event point \(n+1\) should be greater than or equal to the end time of tasks \(i\) executing on the same device at the event point \(n\). b. Different tasks in the same unit:  
\[T^{s}(i,n+1)\geq T^{f}(i^{\prime},n)\;;i\in I_{r},\,i^{\prime}\in I_{r},i\
eq i^{ \prime},r\in R^{l},n<N\,,n\in N. \tag{31}\]  
Constraint (31) represents that the start time of tasks \(i\) at the event point \(n+1\) should be greater than or equal to the end time of different tasks \(i^{\prime}\) executing on the same device at the event point \(n\). c. Different tasks in different units:  
\[T^{s}(i,n+1)\geq T^{f}(i^{\prime},n)-Sla\cdot(1-w(i^{\prime},n))\;;i\in I_{r},\,i^{\prime}\in I_{r}\,,i\
eq i^{\prime}\,,r\in R^{S}\,,n<N\,,n\in N. \tag{32}\]  
In Constraint (32), if \(w(i^{\prime},n)=1\), the start time of tasks \(i\) at the event point \(n+1\) should be greater than or equal to the end time of different tasks \(i^{\prime}\) on different devices at the event point \(n\); If \(w(i^{\prime},n)=0\), Constraint (32) is relaxed, where \(Sla\) is a positive big number. 7. Utility supply and utility time constraints:  
\[w(i,n)\cdot B_{i}^{\min}\leq b(i,n)\leq w(i,n)\cdot B_{i}^{\max};n\in N,i\in I _{r}. \tag{24}\]0  
\[w(i,n)\cdot B_{i}^{\min}\leq b(i,n)\leq w(i,n)\cdot B_{i}^{\max};n\in N,i\in I _{r}. \tag{24}\]1  
\[w(i,n)\cdot B_{i}^{\min}\leq b(i,n)\leq w(i,n)\cdot B_{i}^{\max};n\in N,i\in I _{r}. \tag{24}\]2  
Constraint (33) represents that the maximum supply \(U_{u}^{\max}\) of utility \(u\) should be greater than or equal to the total consumption of the same utility \(u\) at the same event point \(n\). The consumption of utility is determined by constant term \(\lambda_{iu}\cdot w(i,n)\) and variable term \(\varphi_{iu}\cdot b(i,n)\). Constraint (34) represents that the start time consuming utility \(u\) should be less than or equal to the scheduling time horizon \(H\). Constraint (35) represents that the start time of the utility \(u\) at the event point \(n+1\) should be greater than or equal to the start time of the utility \(u\) at the event point \(n\).
## 3 Results
ER, estrogen receptor; BC, breast cancer; **(C)** Percent change in tHb among pCR and non-pCR patients stratified by breast cancer subtypes. TNBC: triple-negative breast cancer. Regarding antiangiogenic therapy, non-responders to bevacizumab, determined based on FDG-PET/CT, showed both lower tumor shrinkage (mean \(\pm\) SE; \(-\)11.9 \(\pm\) 10.5%) than responders (\(-\)38.7 \(\pm\) 8.8%; \(p\) = 0.07) and poorer survival than the responders (\(p\) = 0.1, Kaplan-Meier method, data not shown). Representative case studies of a patient with a tumor responsive to bevacizumab and a patient with a non-responsive tumor to bevacizumab are shown in Figure 5A-1,A-2. Non-responders also showed markedly lower tumor SO\({}_{2}\) immediately after bevacizumab infusion compared to responders, and the SO\({}_{2}\) level in non-responders was significantly lower than that of the responders from days 1 to 3 (Figure 5B). Tumor Hb (tHb) of non-responders transiently decreased on day 1 after bevacizumab infusion, but recovered to baseline between days 3 and 6. In contrast, tHb among responders showed a sustained decrease during the observation period. Specifically, tHb levels on days 3 and 6 were significantly higher in non-responders compared to responders. The findings imply that tumors that are non-responsive to bevacizumab ab display acute hypoxia and further angiogenesis after drug infusion. Figure 5: (**A**) Hemodynamic monitoring of single-agent bevacizumab in breast cancer. (**B**) Tumor tHb and SO\({}_{2}\) change during bevacizumab followed by paclitaxel. Bev: bevacizumab; PTX: paclitaxel; MRI: magnetic resonance imaging. Regarding neoadjuvantendocrine therapy, PET non-responders represented PD (\(n=0\)), SD (\(n=1\)), PR (\(n=18\)), and CR (\(n=1\)), while PET non-responders represented PD (\(n=2\)), SD (\(n=3\)), PR (\(n=2\)), and CR (\(n=0\)). There were significant differences between the two responder groups regarding clinical response (\(p=0.003\), data not shown). Although PET responders and non-responders did not differ regarding invasive size at post-surgical histological evaluation, the Ki67 proliferative index among PET responders was significantly lower than that of non-responders. TRS was performed before and after 2 weeks, 1 month, 2 months, and 3 months of letrozole therapy initiation. Representative case studies of a patient with PET response and a patient with PET non-response are shown in Figure 6A-1,A-2. Among some patients with PET-responsive tumors, peak tHb disappeared immediately after letrozole therapy initiation, while tHb values increased in some patients with non-responsive tumors despite continued letrozole therapy. The decline in tHb value at 1 and 3 months after letrozole therapy among PET responders was significantly larger than that seen among PET non-responders (\(p=0.01\)) (Figure 6B). Our findings reveal a close relationship between glycolysis and the hemodynamics of malignant tumors, implying that hemodynamic response monitoring using TRS is a feasible approach even in patients who receive endocrine therapy.
# Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach +
## 4 Results
### Simulation Results
#### 4.1.1 Optimal Window Length  
Since the length of the fixed lag (i.e., the measurement window size) of the SEKF can be tuned and affects its performance, we first need to figure out if there is a way to optimally determine such a window size, which asks for the determination of a quantitative cost index. Since the performance are related to the estimation error that can be obtained, we choose the Root Mean Squared Error (RMSE) for position and orientation as a figure of merit, i.e.,  
\[e_{p}^{i}=\sqrt{\frac{\sum_{k=1}^{N_{i}}(\hat{x}_{k}-x_{k})^{2}+(\hat{y}_{k}-y _{k})^{2}}{N_{i}}},\]  
and  
\[e_{\theta}^{i}=\sqrt{\frac{\sum_{k=1}^{N_{i}}(\hat{\theta}_{k}-\theta_{k})^{2} }{N_{i}}},\]  
where \(N_{i}\) is the number of samples for the \(i\)-th Monte Carlo execution. Therefore, we first analyse the effect of the window size by computing thebox-and-whiskers of Figure 5 for the different trajectories with different window lengths (from 2 to 180 samples) and assumingthe detection of four RFID tags.  
Of course, while the window length is inversely proportional to the uncertainty, it is actually directly proportional to the computational burden. In particular, Figure 6 reports the computation time of the SEKF as a function of the window length.  
These results are collected using a 1.70-GHz Intel Core i3 microprocessor endowed with 8 GB RAM. It is now evident from Figures 5 and 6 that the higher is the computation  
Figure 5: The RMSE for position (**a**) and orientation (**b**) of the SEKF with different window sizes.  
time, the lower is the localisation error (up to 100 samples). To select the window size, we consider the sampling time \(T_{s}=200\) ms selected at the beginning of this section as a threshold for a real-time execution of the algorithm (i.e., the computation should end before the next data arrives), hence the window length has been fixed to \(n=55\) samples.
## 5 Safety  
### Laser Scanner Placement  
In the past, human safety was one of the main concerns that prevented autonomous robots from performing certain specific tasks (e.g., physical human-robot interaction). Basic human safety is ensured here using a pair of Hokuyo UAM-05 LP-301 safety laser scanners, placed at opposite corners of the robot. However, to achieve human protection all around, the construction of Phollower had to be tailored to not have any part or a component interfering with the active measuring field of each laser scanner. The key requirement is to ensure the widest possible field of the scanners vision, ideally to cover all 360\({}^{\circ}\) around the robot and thereby operate safely and navigate precisely. This was achieved by narrowing the cover plates in the section that corresponds to the active measuring field of the scanners, as shown in Figure 5. In this way, a 360\({}^{\circ}\) continuous protective safety field was secured without any blind angles. A similar approach was used in [6]. Both scanners are fixed directly to the robot's structure and protected by the top cover plate, while only the scanning part of the scanner remains unprotected.  
Safety scanners enable to set up the safety zone up to 5 m and the warning zone up to 20 m. These scanners are primarily dedicated for AGV applications, and a safety zone can be recalculated in real time based on data obtained from digital inputs and based on data from safety encoders. Safety scanners meet the requirements of PLd Cathegory 3 and SIL 2 standards.
## 6 Conclusions  
In this work, we propose a unique mobility-based MAC protocol for traffic safety applications in VANET. With no cluster-head selection requirement, the communication among vehicles is performed by using a clustering-based TDMA schema for hopsector message distribution, which is completely in a distributed environment. Stability is increased by clustering the nodes on the basis of their speed. The cluster maintenance overhead is reduced by not electing the CH. This scheme supports the reality that the highest priority must be given to the real-time traffic possessing higher sensitiveness to attain time slots compared to non-safety-based traffic, which is given a lower priority. Depending on its mobility characteristics, the TDMA mechanism assigns periods of time to the clusters. Reliability and timely delivery of safety messages are ensured by allowing frame synchronization between different clusters. The performance outcome shows that the network spends the minimum period of time compared with other scenarios. This is because of the small size of the cluster formation. Compared with other protocols, the MAMC-MAC protocol performs better delivering messages securely while maintaining high reliability even in a heavy traffic environment along with guaranteeing throughput.  
The proposed MAMC-MAC protocol is efficient when scaling over a number of hops for delivering the messages in real-time traffic and results in increased bandwidth utilization and lowering interference. However, the proposed work is constrained to the traffic moving in a similar direction with uniform density. When the density is non-uniform, the interference is increased and bandwidth utilization is decreased; this is why we assume the vehicular density to be uniform.  
In the future, we plan to extend our proposed work for various traffic scenarios depending on the assigned priority, facilitating non-safety messages, and incorporating adaptive contention window scheme during slot assignment. Additionally, the research shall be extended to designing protocols that consider dynamic single-hop and multi-hop dissemination and are capable of scheduling messages depending on their level of priority. Furthermore, a study shall be performed on the services that are applied irrespective of the location.  
Conceptualization, R.C. and N.G.; methodology, R.C. and N.G.; validation, R.C. and N.G.; writing--original draft preparation, R.C., N.G. and A.S.T.; writing--review and editing, R.C., N.G. and A.S.T. All authors have read and agreed to the published version of the manuscript.  
Figure 13: PIRT comparison.