esa-sceva/satcom-chunk-collection huggingface dataset:
# Polystyrene-Sepiolite Clay Nanocomposites with Enhanced Mechanical and Thermal Properties
## 3 Results and Discussion
### Flame Retardancy Test  
The flame-retardant property of the nanocomposite samples was determined by ASTM Standard D4986-20, as shown in Figure 9. Table 4 exhibits the time taken by the flame to reach T1 and T2, marked at 25 mm and 60 mm lengths on the samples, respectively. The faster the flame travels to reach the mark (T1) and (T2), the lower the flame retardancy of the sample will be. The visuals of Figure 9a show the lighting of the composite sample with a Bunsen burner for 60 s. Figure 9b,c show the flame traveled the length of (T1) and T2, respectively, marked on the rectangular composites' samples. The burning rate determined as per the ASTM D4986-20 standard shows a continuous decrease by incorporating the filler. Figure 10 shows a consistent increase in flame retardancy observed from pristine samples S0 to S4. A maximum decrease of 48% in the burning rate was determined for the S4 sample.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample Code**} & **Sepiolite** & **Time (T1) in Sec for** & **Time (T2) in Sec for** & **Burning Rate** \\  & **Loading** & **(25 mm Length)** & **(60 mm Length)** & **(cm/min)** \\ \hline
**S0** & 0\% & 12 & 60 & 50 \\ \hline
**S1** & 1\% & 16 & 64 & 36 \\ \hline
**S2** & 2\% & 19 & 66 & 31 \\ \hline
**S4** & 4\% & 25 & 71 & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Flame retardancy test data.  
Figure 8: TGA analysis of pristine PS and PS/sepiolite nanocomposites.  
Figure 9: (**a**) Lighting of composite sample, (**b**) Distance of 25 mm traveled by the flame in T1, (c) Distance of 60 mm traveled by the flame in T2.
# Hydraulic Characterization of Green Roof Substrates by Evaporation Experiments
###### Abstract  
Green roofs can be a valid solution for stormwater management in urban environments. The objective of this study was to develop a laboratory procedure for the hydraulic characterization of artificial substrates, used in the realization of green roofs, based on transient evaporation and steady-state unit hydraulic gradient (UHG) experiments. The retention, \(\theta(h)\), and hydraulic conductivity, \(K(h)\), curves of two commercial substrates Terra Mediterranean(r) (TMT) and AgriTERRAM(r) (ATV) and a specifically developed substrate made by mixing peat, compost and sandy loam soil (MIX) were investigated. The unimodal van Genuchten-Mualem (VGM) hydraulic functions obtained by the direct evaporation method with different choices of the fitting parameters were compared with UHG measurements of \(K(h)\) conducted close to saturation. A numerical inversion of the transient evaporation experiments performed by Hydrus-1D software was also conducted, assuming that the hydraulic properties could be expressed either by unimodal or bimodal VGM models. The results indicated that an appropriate a priori choice of the residual water content parameter improved the estimation of the water retention curve. Moreover, the water retention data estimated from the direct evaporation method were not statistically different from those obtained with the inverse Hydrus-1D. The unsaturated hydraulic conductivity estimations obtained by the direct and inverse methods were highly correlated and the use of the bimodal VGM model improved the estimation of \(K(h)\) in the wet range. The numerical inversion of laboratory evaporation data with the hydraulic characteristics expressed by the bimodal VGM model proved to be a reliable and effective procedure for hydraulic characterization of artificial substrates, thus improving the reliability of simulated water fluxes in green roofs.  
green roof; evaporative method; Hydrus 1D; hydraulic characterization; retention curve; conductivity curve +
Footnote †: journal: Applied Sciences
# Efficient Control of a Non-Linear System Using a Modified Sliding Mode Control
## 3 Simulation and Experimental Results
### Simulation Results of SMCSPO and PDSMCSPO
#### 3.2.3 Step Output  
Step input was given to the system to observe the results. It was observed that the convergence of the output of PDSMCSPO was faster than SMCSPO. The reason was explained in the control theory chapter. In Figure 12, it is visible that the output response of PDSMCSPO (blue line) is faster than the SMCSPO (red line).
## 2 Experimental Study
### Repeatability Verification and Error Analysis  
#### 2.3.1 Repeatability Verification  
Pre-experiments were conducted to verify the performance of the experiment. Firstly, the wind speed at the duct outlet was tested to ensure the stability of the air supply of the fan. As shown in Figure 4b, the velocity measurement points were arranged according to the equal cross-section method. Figure 5a shows the fluctuation of wind speed at each test point when the fan frequency was 40 Hz. As shown in Figure 5b, the turbulence intensity (standard deviation/average value) at each test point did not exceed 3.92%. The above test indicated that the stability of wind flow at the duct outlet could be ensured [30].  
To illustrate, the average velocity \(u_{0}\) of the outlet cross-section is calculated by Equation (1):  
\[u_{0}=\frac{Q}{A}=\frac{\sum(u_{i}\times A_{i})}{\sum A_{i}}, \tag{1}\]  
where \(Q\) is the air volume at the outlet of wind duct, m\({}^{3}\)/s. \(A\) is the cross-sectional area of the wind duct outlet, m\({}^{2}\); \(A_{i}\), \(u_{i}\) represent the area and wind speed of each part of the equal-area partition, respectively. The correlation between the fan frequency and \(u_{0}\) was determined by repeated tests. As can be seen in Figure 5c, there was a strong linear relationship between them, demonstrating that adjusting the fan frequency effectively controlled the velocity at the duct outlet.
# Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor
## 4 Experiments  
In this section we demonstrate the effectiveness of our proposed MCDRC by comparing it with other compressors on different types of sensor data.
## 4 Service and Communication Technology Adaptation Method
### Sorting and Decision Making Based on the GTOPSIS Method  
The fundamental principle of the TOPSIS method involves sorting alternatives by calculating their Euclidean distances from both the positive ideal solution and the negative ideal solution, respectively. The positive ideal solution is identified by maximizing benefit-type indexes and minimizing cost-type indexes, whereas the negative ideal solution is determined by maximizing cost-type indexes and minimizing benefit-type indexes. This method is widely used in multi-criteria decision making, with strong operability and objective and comprehensive decision results. However, TOPSIS is sensitive to outliers, which may affect the accuracy of the results.  
GRA is a method of ranking by calculating the grey relational degree between subsequences and the reference sequence. Due to its capacity to handle uncertain information, GRA is relatively robust to outliers, making it less susceptible to the influence of extreme values. Nevertheless, the selection of the reference sequence in GRA involves a high degree of subjectivity.  
Consequently, this paper proposes the GTOPSIS method, which combines GRA with the TOPSIS method. GTOPSIS seeks a hybrid distance based on the principle of minimum information entropy, which is then used to calculate a relative hybrid closeness for ranking and decision making. The decision results derived from the improved ranking method are more stable and reliable. The specific steps of the decision-making method based on GTOPSIS are as follows.
## 5 Performance Evaluation
### Methodology  
We implemented our proposed scheme and one existing scheme on NS-3 and conducted performance comparison. As most of the existing attestation schemes do not consider the metrics (e.g., network delay) that we focused on in this paper, we choose a typical software-based attestation scheme [11] (also called SWATT) as a baseline scheme and compare its performance with our proposed scheme. In SWATT, the verifier sends a challenge to the remote device and then determines whether it is compromised based on the response time in the \"challenge-response\" process.  
In our experiments, we consider a scenario of \(225\) nodes that form a \(15\times 15\) array. Nodes are deployed in a \(0.750\,\text{m}\,\times\,0.750\,\text{m}\) area uniformly, _i.e._, each node is deployed in a square with a side length of \(50\,\text{m}\). The verifier, which is located at \((0,0)\), verifies nodes on different locations in the network. With the loss of generality, we assume all nodes are the same and have the 802.11b NICs in ad hoc mode. In our evaluation setup, 802.11b physical layer with DsssRate of 1 Mbps and the AODV route protocol are used.  
To simulate a real situation, we assume that the adversary prefers to attack some important nodes in the network and to remove attack traces after the injection of malicious code, in order to prevent the detection from the verifier. To simplify the simulation, we assume that all attacks are independent and that the time of each attack has an exponential distribution with a rate parameter \(\lambda\), which represents the average number of attacks in a unit of time. We also assume that the life time of malicious codes is \(20\%\) of the unit interval, whereas the simulation lasts for \(100\) unit intervals.  
To evaluate the efficiency of our proposed scheme, we consider two cases: (i) attestation efficiency is used to compare the efficiency with the same number of attestations; and (ii) attestation overhead is used to compare the required attestation to reach the same efficiency. We compare our proposed scheme and the baseline scheme in terms of following metrics: (i) successful attestation is defined as the number of attestations that detect an attack; (ii) the number of attestations is defined as the total number of attestations; and (iii) the number of undetected attacks is defined as the number of attacks that are not detected.  
In addition, to evaluate the computation overhead on remote nodes, we also consider the overhead of computing the checksum, which tends to compare the overhead of the remote device computing the checksum. To this end, we define the metric response time as the time difference between the challenge request sent and the checksum response received.
## 4 Discussion
### Uncertainty Analysis  
Currently, numerous studies on regional ecosystem WUE exist, some based on flux station data and others on model simulations, yet accurately quantifying regional terrestrial ecosystem WUE remains challenging. First, different calculation methods express varying meanings [78]; second, research methods and data contain uncertainties, particularly in high-altitude areas such as the QTP, where data collection is difficult. Various factors, such as differences in observation scales, data uncertainties, and model uncertainties, contribute to discrepancies in WUE results within the same area [78]. Finally, quantifying the impact  
Figure 7: Spatial distribution of GPP, ET, and WUE’s CV ((a) shows the CV of the annual GPP for the whole growing season, (b) shows the CV of the annual ET, (c) shows the CV of the annual WUE, with the histogram in the upper left corner showing the area ratio within each variation range).  
of climate change, human activities, and other factors remains problematic [71]. This study constructed a random forest model using GPP data from ground observations, but the limited number and uneven distribution of observation stations inevitably affect modeling accuracy. Previous studies have shown that key variables for GPP simulation include air temperature, precipitation, SRAD [79], terrain factors [24], vegetation status [80], and soil moisture [81]. In this study, some of these variables were used for GPP simulation, demonstrating the feasibility of this approach.
## 1 Introduction
Some relevant studies in the field [45; 46; 47; 48; 49; 50] confirm that thymus EO has been found to have good antimicrobial activity against _Bacillus_, _Staphylococcus_, _Fusarium_ and _Aspergillus_ spp., in comparison to the effects of EO with commercial biocide. [PERSON] et al. [49] point out the inhibitory effect of thyme, clove and cinnamon EOs on _Candid albicans_ and different finds of mould. The EOs of eucalyptus and lavender tested with good results as natural preservatives for leather [51], with oregano EO being successfully used as a bactericidal agent in the leather industry by [PERSON] et al. [26; 44]. Combinations of the EOs [52], _Thymus vulgaris_ and _Pimpinella anisum_ and methanol extracts, have shown outstanding antibacterial properties against the pathogenic bacteria, _Staphylococcus aureus_, _Bacillus cereus_, _Escherichia coli_, _Proteus vulgaris_, _Proteus mirabilis_, _Salmonella typhiri_, _Salmonella typhiriumum_, _Klebsiella pneumoniae_ and _Pseudomonas aeruginosa_. EOs obtained from _Matricaria chamomilla_ have been tested [53] as environmentally friendly in cultural heritage environments, against _Aspergillus_ spp. ; similarly, [PERSON] et al. [54] pinpoint in the same study, the intense antifungal activity of camphor oil, in comparison with other tested oils. [PERSON] et al. [55] point out that EOs from southern Italy, derived from _Origanum_, tested with the minimal inhibitory concentrations, _in vitro_, with evident antibacterial and antifungal activity. EOs like _Origanum vulgare_ and _Thymus vulgaris_ have also been applied by [PERSON] et al. [56] to emphasise the green remediation in the biodeterioration of cultural heritage artworks, which is induced by fungal colonisation (e.g., by that of _Aspergillus flavus_). The above-mentioned authors concluded that valid green conservation strategies(with no negative effects for human health and no environmental pollution) could possibly replace traditional biocides. In Romania, recently, an EO isolated from thyme (_Thumus vulgaris_) was assayed for antifungal activity against _Candida albicans_ and _Aspergillus niger_ on sheepskins, with it being shown to have outstanding antifungal proprieties [57; 58; 59]. Moreover, the studies by [PERSON] et al. [60; 61] and [PERSON] et al. [62] have emphasised the possibility of using antifungal ecological materials to treat cultural heritage objects made of natural leather. Following the above, this research aimed to investigate the microbial load of an old heritage object, made of natural animal sheepskin, which metabolic activity could generate, thus accelerating the biodegradation process, and the loading of the aerosol with biological particles, like spores, toxins and allergens, as well as other harmful substances. Such action can cause problems regarding the health of workers and users and the application of natural biodegraders. Consequently, the antifungal properties of some EOs of _Lazandula angusifolia_, _Citrus limon_, _Mentha piperita_, _Marjoram_, _Melaleuca alternifolia_, _Origanum vulgare_ were tested, with the results of their applications being recorded at certain time intervals.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 5 Testbed Deployment and Experimentations  
### Testbed Hardware  
Our experimental testbed is composed of 45 Arduino nodes deployed in an area of \(65\times 10\) m. Each node has two components: (i) ATmega2560 [51]: a 8-bit microcontroller (256 KB of flash memory and 8 KB of SRAM), and (ii) XBee radio module [52] (IEEE 802.15.4). The transmission power is set to 2 dBm. Source nodes are in addition equipped with TTL serial JPEG camera [53]. The testbed is integrated in the existent large-scale platform FIT IoT-LAB [22; 23], as described in [54; 55]. The choice of the Arduino platform was motivated by many reasons. From one hand, this type of platform is widely used among makers and scientific community. Furthermore, Arduino platform offers the possibility to connect hardware extensions, such as still image camera (that we are using), wireless sensors, UART (Universal Asynchronous Receiver Transmitter) connection to other IoT-nodes (such as WiFi-enabled IoT ESP8266 nodes providing secure MQTT (Message Queuing Telemetry Transport) connections to the cloud), etc... From the other hand, the integration of the platform within FIT IoT-LAB makes it available for other users. Even if there are other type of nodes which may be more powerful than Arduino nodes, using Arduino in our work let us face the challenge of optimizing our development and making an interesting return of experience.
## 1 Introduction
Developing ways to better utilize asymmetry is consistent with a philosophy of trying to use methods that can capture measures deemed important by doctors thereby building upon their knowledge base, instead of trying to supplant it. However, measuring asymmetry involves registration and comparing multiple images, and thus it is a more complicated process. Registration is the matching of points, pixels, or structures in one image to another image. Registration of mammograms is difficult because mammograms are projections of compressed three-dimensional structures. Primary sources of registration errors are differences in positioning and compression, which manifests itself in visually different images. The problem is more complex because the breast is elastic and subject to compression. Additional sources of difficulty include the lack of clearly defined landmarks and the normal variations between breasts. Strictly speaking, precise mammogram registration is intractable. However, an approximate solution is possible [40]. Warping techniques have been used [41], as well as statistical models [42] or mutual information as a basis for registration [43]. The technique advanced in this paper learns image comparison models based upon a clustering that encapsulates an approximate registration and uses them to compare the mammograms of the left and right breasts. This also avoids direct registration when measuring the image similarity. The rest of this paper is organized as follows. Section 2 describes mammogram images and feature extraction from mammograms. Section 3 details the approach taken as an initial data exploration and classification approach, and the development of a distance function. Section 4 describes the improvement to CAD approaches through the inclusion of similarity. The conclusion is in Section 5. Figure 1: The typical set of four images that make up a mammogram, the side view of the left breast in (a), the side view of the right breast in (b), the top view of the left breast in (c), the top view of the right breast in (d). The cancerous areas are outlined in red. Since the images come in sets, the non-cancerous cases are examples of similar images, while the cancerous cases are examples of dissimilar images, and these examples can be used to determine image similarity. Note that the textures of the cancer are very similar to non-cancerous areas, which is why image comparisons are so important in the analysis of mammograms. Also note that the cancer is apparent in both images of the same breast, which provides additional information for the analysis. This image set was correctly classified by the method described in Section 3.
## 2 Research Methodology
### Feature Extraction
#### 2.2.3 Rotation of EC Signal  
The demodulated and filtered fastener signatures were found to be shifted from the in-phase direction (real part). In order to retrieve maximum information and have a better visualisation, the complex EC signal was rotated such that the fastener signatures were projected along the in-phase direction. This to an extent aids in suppressing other responses in other demodulation angles not prevailing to the fastening system. The EC signal was rotated by degree \(\theta\) or \(\Phi\) radian, such that the peak amplitude of the fastener signatures were maximised. The signals were rotated by an optimal angle (found from the previous study [25]) of 83\({}^{\circ}\) and 222\({}^{\circ}\), respectively for the two carrier frequencies.  
The EC signal does not get affected due to the presence of nonconductive or nonmagnetic materials in the sensor-to-target gap. The disturbances arising due to the presence of conductive and magnetic material in the sensor-to-target gap can be suppressed to an extent by the above-mentioned low pass filtering and rotation of EC signal techniques. The low pass filter was set to extract the fastener signatures and to remove other high frequency components which could add to the energy content of the fastener signatures. The cut off frequency of the filter is dependent on the speed of the sensor and must be adjusted accordingly. Different components will have different geometric shapes, different values of magnetic permeability and electrical conductivity. Hence they will occur at different angles from the in-phase direction compared to the fasteners. Rotation of the EC signal based on fastener signature will thus to a major extent suppress information pertaining to other disturbances.  
Four features for both channels are extracted for individual fasteners namely peak-to-peak, RMS, and magnitude of the fastener signature at clamp frequency and the arc length of the complex signal. Three separate feature matrices will be used as an input for the classification purpose in this study. The first feature matrices will use the four features obtained from fastener signatures acquired from the 18 kHz channel. The four features obtained from the 27 kHz channel representing the fastener signature will be used for the second feature matrices. The first and second feature matrices will have a dimension of 2967 \(\times\) 4 (2967 samples and four features). The third matrices contain the combination of 18 kHz and 27 kHz features, eight features in total representing a fastener signature. Hence the third feature matrices will have a dimension of 2967 \(\times\) 8 (2967 samples and eight features respectively). The three feature matrices are tested to identify whether a single channel or both channels when combined perform better in missing clamp detection.