esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Stress Estimation Based on the Monitored Strain
### Model Space and Data Space  
In the Specification for the Design of Highway Reinforced Concrete and Prestressed Concrete Bridges and Culverts in China [24], CEB-FIP MC 90 is used for the calculation of creep and shrinkage strain. Thus, the CEB-FIP MC 90 model was adopted in this study to calculate the temperature strain, creep strain, and shrinkage strain [25]. Meanwhile, to consider the uncertainty caused by the model selection, two model uncertainty factors for creep and shrinkage (\(\alpha_{c}\) and \(\alpha_{s}\)) were introduced into our analysis, and updated by the monitored data. This would eliminate some effects of model selection on the results.  
The forward model of the concrete strain shows that the concrete strain is influenced by a number of factors including the concrete elastic modulus, concrete compressive strength at the age of 28 days, drying age, loading age, relative humidity, stress, and model uncertainty. Among these factors, the drying age and loading age are artificially determined for actual bridges, so they were not considered as model parameters for this study. Since the concrete compressive strength, concrete elastic modulus, and relative humidity can be obtained in actual experiments, these factors were also not selected as model parameters. The uncertainty factors of the model and structural stress were selected as model parameters since they are difficult to obtain from structural monitoring.  
In order to obtain the boundary conditions of a model space, the prior distribution of all the parameters needs to be determined first. For the creep and shrinkage model suggested in the CEB-FIP MC 90, [PERSON] and [PERSON] [26] determined the statistical characteristics (mean and coefficient of variation) of the uncertainty factor of the model by comparing the predictions of the creep and shrinkage effects from extensive experiments, as shown in Table 1.  
Prior information of the stress in a concrete structure depends on the monitoring points of the structure. In order to obtain the stress based on the strain in concrete structures, parameters in the data space should be the concrete strains of certain monitoring points at certain ages. Since the concrete strain is related to the location of the monitoring points and concrete ages, the data space is essentially a matrix composed of the strain vectors of different positions and different ages. The model space is a hypercube composed of model parameters. The boundary condition of the model space is defined as the 0.997 double-sided quantile values of all parameters.
## 3 Method
### Waveform Retracking Method
#### 3.2.1 Thermal Noise Removal and Normalization  
A simple average-value filter in the 1-25 th gates is applied firstly to eliminate the extremely abnormal noise. Based on the statistic of HY-2B altimeter coastal waveforms, the gates from 5 to 10 are chosen to calculate the thermal noise and remove it from the echo signal. The maximum of the eight-point moving average at each gate is taken as the normalization factor, which is subsequently adjusted according to the accurate leading edge power to ensure that the maximum of the leading edge is approximately 0.9 normalized power. The power mentioned subsequently refers to the normalized power in this study.
## 3 Materials and Methods
### Scenarios Investigated  
The disruptive event was assumed to be due to a traffic accident or fire occurring within the north tube (reported in red in Figure 2). In order to investigate the worst effects on the traffic, the incidental event was assumed to occur at a peak hour in the morning (i.e., at the beginning of 7:00 a.m.). The duration of the partial or complete blockage of the mentioned north tube was assumed to be equal respectively to 1 h, 2 h, or 3 h (i.e., from 7:00 to 8:00 a.m., from 7:00 to 9:00 a.m., or from 7:00 to 10:00 a.m.).  
Figure 3 shows the scenarios investigated: (i) Scenario 0 corresponds to the ordinary functionality of each tunnel tube without any disruptive event (i.e., unidirectional traffic transit on the two lanes of each tube, both for the north and south direction, respectively); (ii) Scenario 1 represents the partial closure of the tube interested by a traffic accident or fire (i.e., only one lane is closed, while the remaining lane continues to be used for unidirectional traffic towards the north direction), the speed limit imposed at the entrance portal is in this case equal to 60 km/h; (iii) Scenario 2 corresponds to the complete closure of the aforementioned north tube (i.e., both lanes are closed) with the reorganization of the traffic flow by using the adjacent tube for bi-directional traffic, in this respect different hypotheses were made about the intervention time of the emergency service team to open the two traffic by-passes at the tunnel portals from the start of the incidental event (i.e., 10 min (Scenario 2a), 20 min (Scenario 2b), and 30 min (Scenario 2c)), the speed limits imposed at the two portals are 60 km/h; (iv) Scenario 3 is similar to Scenario 1, however it also involves the use of the alternative itinerary for heavy goods vehicles (HGVs) only traveling on the motorway section towards the partially blocked north tube, in this scenario includes the activation of the variable message signs (VMS) located before the Motorway Junction B in order to suggest the alternative route to HGVs only; (v) Scenario 4 is similar to Scenario 2, however it also involves the use of the mentioned alternative itinerary for HGVs only (i.e., Scenarios 4a, 4b, and 4c) traveling on the motorway section towards the completely blocked north tube, obviously through the activation of the mentioned VMS; (vi) finally, Scenario 5 regards the complete closure of the north tube affected by the incidental event, in which the alternative itinerary is used by all the vehicles (i.e., both passenger cars and HGVs) of the motorway section traveling towards the completely blocked north tube.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Speed Limits**} \\ \cline{2-3}
**Road Segments** & \multicolumn{2}{c}{**[km/h]**} \\ \cline{2-3}  & **Passenger Cars** & **HGV** \\ \hline Motorway & 130 & 100 \\ Major rural road & 70 & 50 \\ _(alternative itinerary)_ & 70 & 50 \\ Minor rural roads & 70 & 50 \\ Motorway junctions & 40 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Speed limits for the road sections of the transportation network investigated.
## 3 Results and Discussion
### Xylitol Fermentation from EOP Acid Hydrolysate
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Raw Material** & **Pretreatment** & **Detoxification Method** & **Microorganism** & **Xylitol Yield (g/g)** & **Reference** \\ \hline \hline \multirow{2}{*}{Poplar wood} & 120 °C, 4 h, 1.8\% H\({}_{3}\)SO\({}_{4}\) & Vacuum evaporation & _Candidia guillirmmodii_ FT1 20037 & 0.52 & [41] \\ \hline \multirow{2}{*}{Popl mall hardwood wastes} & \multirow{2}{*}{140 °C, 60 min, 30 g/L H\({}_{3}\)SO\({}_{4}\) 12.5\% DM} & Activated charcoal and ion-exchange resin & _Candidia bullini_ BCRC 21 432 & 0.11 & [42] \\ \cline{2-6}  & & & & _Candidia bullini_ BCRC 20 334 & 0.21 & [42] \\ \cline{2-6}  & & & & _Candidia tropicalia_ BCRC 20 520 & 0.45 & \\ \hline \multirow{2}{*}{Corn fibre} & 90 °C, 51 min, 1.1\% H\({}_{3}\)SO\({}_{4}\) 10\% DM + 120 & Non detoxification & _Candidia bullini_ NCAM Y01308 & 0.53 & [35] \\ \cline{2-6}  & \multicolumn{2}{l}{\({}^{\circ}\)C, 30 min, 1.1\% H\({}_{3}\)SO\({}_{4}\) 10\% DM} & \multicolumn{2}{l}{} & PH adjustment and activated charcoal & _Candidia bullini_ XM02G & 0.52 & [17] \\ \hline \multirow{2}{*}{Rapeseed straw} & 130 °C, 60 min, 2\% H\({}_{3}\)SO\({}_{4}\) 10\% DM & Ethyl acetate extraction & _Debruyomyces hasnenii_ NRRL Y-7426 & 0.45 & [32] \\ \cline{2-6}  & & & & _Candidia guillirmmodii_ FT1 20037 & 0.55 & \\ \hline \multirow{2}{*}{The wheat straw} & 121 °C, 30 min, 1\% H\({}_{3}\)SO\({}_{4}\) 10\% DM & Activated charcoal & _Candidia guillirmmodii_ FT1 20037 & 0.42 & [43] \\ \cline{2-6}  & & & & _Candidia tropicalia_ CBF94 & 0.44 & [44] \\ \cline{2-6}  & & & & & \\ \hline \multirow{2}{*}{Eshaustedololive pomace} & 130 °C, 130 min, 3.5\% H\({}_{3}\)SO\({}_{4}\) 33.3\% DM & Dananoffiltration & _Debruyomyces hasnenii_ NRRL Y-1448 & 0.26 & [40] \\ \cline{2-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Yylitol yields reported in the fermentation of hemicellulose hydrolyses from diverse lignocellulosic. residues, using different types of microorganisms.
## 3 Results and Discussion
### Phosphorus and Phytic Acid Content
These materials may contain higher levels of smaller forms of myo-inositol phosphate (such as IP3, IP4 and IP5), which can coelute with phytic acid during purification, leading to an overestimation of the actual phytic acid content. Extreme phytic acid values, comparable to those found in plant storage organs, are often questionable and may be indicative of measurement errors, underscoring the need for more accurate and reliable methods [58, 63, 64, 65]. EOC 2 exhibited the highest phosphorus (P) and phytic acid content, measuring 0.14 g/100 g and 0.48 g/100 g, respectively. Our phosphorus findings are consistent with those reported by others, who recorded values of 0.069 g/100 g P for olive cakes from the two-phase system and 0.12 g/100 g P for olive cakes from the three-phase system [20]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Olive Cakes**} \\ \cline{2-6}  & **EOC 1** & **EOC 2** & **TPOC (Pitted)** &
\begin{tabular}{c} **TPOC** \\ **(Dehydrated)** \\ \end{tabular} & **COC 1** & **COC 2** \\ \hline
**Total Phosphorus** & 0.07 \(\pm\) 0.01 b & 0.14 \(\pm\) 0.02 a & 0.13 \(\pm\) 0.02 a & 0.11 \(\pm\) 0.02 ab & 0.12 \(\pm\) 0.01 a & 0.13 \(\pm\) 0.01 a \\
**Phytic Acid** & 0.26 \(\pm\) 0.04 b & 0.48 \(\pm\) 0.06 a & 0.45 \(\pm\) 0.08 a & 0.38 \(\pm\) 0.07 ab & 0.43 \(\pm\) 0.02 a & 0.46 \(\pm\) 0.04 a \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Phosphorus and phytic acid content of olive cakes (g/100 g). Studies analysing the phosphorus and phytic acid content of olives have documented values around 0.02% P and 0.12% phytic acid [67].
# Greenhouse Ventilation Equipment Monitoring for Edge Computing
## 3 Experiments
### Training of Object Detection and Embedding Model
#### 3.2.3 Fan State Recognition  
To assess the validity of state embedding for fans, we classified their states based on these embeddings. In our study, we analyzed fans within the same segment of video footage, detecting and embedding a state sequence for 16 fans, each with a length of 117, corresponding to the frame count of the video segment. The distribution of these sequences, followed a dimensionality reduction via a Principal Component Analysis (PCA) [22].  
Figure 11 illustrates clusters in which each color-coded point signifies the coordinates of fans in various frames post-PCA reduction. Fans not in operation are encircled in red. These clusters demonstrate distinct groupings that may be influenced by varying angles of capture, leading to differing distributions upon dimensionality reduction. It is important to observe that the fans currently in operation are organized in a ring-like pattern, whereas those that are not operational tend to be grouped in compact clusters. This distinct spatial arrangement can be utilized to identify the operational state of fans.  
To verify whether the sequence of state embeddings accurately represents the operational state of fans, a binary classification task was performed using a single-layer Long Short-Term Memory (LSTM) network. This network processed sequences of eight embeddings with a hidden layer dimension of 64. Our experiment utilized ten sequences from five fans (each fan contributed one sequence for both operational and non-operational states), dividing them into a training set with six sequences from three turbines and a validation set with four sequences from the remaining eight turbines. Notably, the sequences corresponding to turbines in operation and those not in operation were 117 and 105 units long, respectively. The results, as depicted in the Figure 12, demonstrated that the model achieved a 100% accuracy rate by the end of the first epoch. Furthermore, throughout the training process, we observed a steady convergence of classification loss on the valida  
Figure 10: Impact of enhancement techniques on image reconstruction. The colored image is the original image, and the grayscale image is the reconstructed image. (**a**) Normalization; (**b**) gamma transformation; (**c**) histogram equalization.  
tion set, indicating the model's effectiveness in distinguishing between the two operational states of fans based on the embeddings.
# Communication Technologies in Emergency Situations
## 5 Discussion
### Future Work Directions  
As has been shown in previous sections, there are a plethora of techniques and technologies to address the communication challenges that arise in emergency situations. Although these solutions have proved to operate successfully when needed, such a broad range of alternatives complicates the task of selecting the most appropriate set of technologies for each situation. In fact, there are several technological challenges to be addressed when providing a communications infrastructure for emergency situations: reliability, flexibility, dynamic behaviour, hostile scenarios, multi-vendor devices, and scalability. This section discusses the future work directions derived from this systematic literature review. The first future work direction that this research envisages the construction of a formal taxonomy of technologies for ECS. Given the wide range of alternatives, it would result in massive help to provide a tool that identified the relations between the different technologies, their associated threats and strengths, and their typical use-cases. In addition, this taxonomy could include a cost analysis (which is often an oversight in the reviewed literature) for each technology so practitioners and system designers could reliably assess the cost effectiveness of each tool. A very first step toward building this taxonomy could be to define a standard and structured framework to position and understand the interactions between each of the technologies required to implement an ECS. For instance, it would be possible to establish a mapping between the conception of a communications infrastructure for emergency situations and the well-known Ubiquitous Sensor Network (USN) layered model [59]. In fact, communications infrastructures for emergency situations share several similarities with the challenges faced by USNs [59]. In fact, in a USN, a dynamic set of heterogeneous sensors and actuators--which can be associated with FRs, citizens, or devices in the communications for emergency situations domain--with different capabilities are deployed and span a large-scale area in order to cooperate together. Therefore, there are some lessons that could be learned from the USN's domain and exported to the communications for emergency situations use-case. The idea of the possible mapping between ECS and the USN layered model is further elaborated in what follows.
## 2 Methodology
### Workflow Simulation  
Prior to development in a real scenario, it is necessary to not only test the workflow in a controlled environment, but also to pretrain the DRL optimizer algorithm. To perform this simulation, the proposed scenario (Figure 1) is deployed over a non-orchestrated network. Since one of the main objectives of this simulation is to test the optimization feature of a real scenario, we need to generate network problems in the workflow. We consider a network to be the communications infrastructure itself over which the multimedia content will flow, so the initiative is to create possible corruptions in the network and, therefore, in the reception of the content to train the algorithm against possible future inconveniences.  
In this case, it is considered an in-network adaptation to a given network bottleneck. For this purpose, a Traffic Manager (TM) acts as a controlled environment composed of a network interface, where there is a limit on the link bandwidth. The network interface will act as an input to the point that contains the components where the network problem could affect the service and possibly obtain a network bottleneck to train the algorithm under all conditions.  
To create network problems, it is necessary to deploy a background traffic generator that will simulate different hosts that consume bandwidth and compete for the limited bandwidth available in the TM. This tool is based on the well-known open-source tool iPerf [18]. The bandwidth transmitted by this background service is generated randomly, so that both the network and the optimizer have no control over these values, simulating a close to reality networking problems environment.  
The objective is to perform an optimization of the multimedia service, evaluating the QoE in the receiver, and optimizing the bitrate. The content will be received at the end of the flow, which will feed the optimizer to predict the actions that enable better QoE in the environment. These actions are reflected in the transcoder, which will adapt the bitrate to the actions taken. During training, the receiver is expected to have low-quality video at certain times, including image artifacts such as block loss. By using background traffic, the environment will face cases where the conditions are extremely poor, and in initial phases the optimizer will not know how to properly act (learning from different conditions). More details about this problem are presented in Section 2.4. Once trained, we can assess whether, through training, we have managed to alleviate video problems caused by various conditions in the network, obtaining the best possible QoE.
## 4 Experiments and Analysis
### Experiment Results and Error Analysis
However, Figure 6a shows that this system often makes mistakes at predicting the rainfall levels, especially for the high magnitude of precipitation, often erroneously forecasted as a low magnitude of precipitation; this is because the entire weather system is very complicated. The purpose of these experiments was to predict precipitation by inputting atmospheric pressure, temperature, relative humidity, and wind force is only to demonstrate that adding input conditions can increase the accuracy of the case-based FCTF reasoning systems. We just added two input conditions in experiment number four compared with experiment number two, and the forecast accuracy improved by 0.36 in hit rate. We believe that, in the practical meteorological services, we can choose more suitable input conditions to improve the accuracy of precipitation forecasts dramatically.
## 2 Aerodynamic Model of the Quadrotor Considering Rotor Interference  
Compared with the isolated rotor, the intensity of aerodynamic interference between the adjacent rotors is determined by the rotor spacing. Considering that the interference is mainly caused by the rotor wake and interactions between the downwash flows, the stability and flight efficiency of the quadrotor is challenged in hover. Figure 1 shows the aerodynamic interference model of micro quadrotor aircraft.  
In the Figure 1, \(\Omega\), \(T\) and \(R\) are the rotational speed, the thrust and the radius of rotor. The distance between the rotating center of two adjacent rotors is \(L\). And the thrust of quadrotor can be expressed as follows:  
\[T=\sum_{i=1}^{4}T_{i}=\sum_{i=1}^{4}b\Omega_{i}^{2}\ (b>0,\ i=1,\ 2,\ 3,\ 4), \tag{1}\]  
Figure of Merit (_FM_) can be used to measure the hover performance of rotor, and it is defined as follows:  
\[FM=\ \frac{C_{T}^{3/2}}{\sqrt{2}C_{P}}=\frac{T^{3/2}}{P\sqrt{2\rho A}}=\frac{T^{ 3/2}}{Q\Omega\sqrt{2\rho A}} \tag{2}\]  
where \(C_{T}\), \(C_{P}\), \(P\), \(Q\), \(\rho\) and \(A\) are thrust coefficient, power coefficient, power, torque, air density, and the area of the rotor disk, respectively. The definitions of thrust and power coefficients are as follows:  
\[C_{T}=\frac{T}{\rho A\Omega^{2}R^{2}}, \tag{3}\]  
\[C_{P}=\frac{P}{\rho A\Omega^{3}R^{3}}=\frac{Q}{\rho A\Omega^{2}R^{3}}, \tag{4}\]  
In order to analyze the interference between adjacent rotors with different rotor spacings, a dimensionless quantity L/R is defined for rotor spacing, which is ranged from 2.2 to 4.0. In the next sections, a custom-designed experimental setup is applied to measure the thrust and power of the rotor, and then CFD methods are applied to simulate the flow field characteristics systematically. Finally, the optimal rotor configuration with better aerodynamic performance is obtained.
###### Abstract  
Inverting land cover reflectance or derived indices from low-spatial-resolution images to refine the spatial resolution of this data is cost-effective for land surface monitoring applications that face technical or budget limitations. Based on the linear spectral mixing model, many approaches have successfully unmixed coarse mixed pixels using high-spatial-resolution land cover maps in the past decades. However, in some cases, the solutions of linear systems composed of several mixed pixels may not be acquired due to the underdetermined problem. This study presents the causes of this problem and proposes an iterative approximation strategy to address it. An elastic-window-based algorithm was developed, where the initial size of the window was calculated based on the land cover of the mixed pixel. Mixed pixels of neighborhoods with similar land covers were then selected to form the unmixing linear system, which was examined through a simulation test to ensure it was not underdetermined. Otherwise, the window would expand to include more adjacent pixels. This process was repeated until a successful solution was obtained. A statistical analysis of sixty land cover maps from around the globe shows that the underdetermined problem exists at a low level but becomes more serious with an increase in mixed scale. The results demonstrate that the proposed algorithm effectively prevents the underdetermined problem for mixed pixels of different scales and can be integrated into the coarse NDVI downscaling procedure to refine spatial resolution. This study provides a reference for estimating underdetermined mixed pixels and benefits applications that require dealing with the inversion of land cover values directly.  
linear spectral unmixing; downscaling; linear system; time series; land cover +
Footnote †: journal: Applied Sciences  
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