esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### The Self-Organizing Map (SOM)  
As highlighted in Section 4, SOM is an important tool for creating and representing highly dimensional input space, which binds correlated variable densities and displays them using colors or the variable region. Another advantage of this algorithm is that it can encapsulate complex data and still maintain the topographical attributes of the data in the input vector space [78]. Consider a neural network of \(b\) weights consisting of \(y\) input correlating to an Android app having its permissions represented by binary string \(\{0,1\}\). Let \(g\) represent the matching neuron; then, the Euclidian distance existing between \(y\) and \(b\) is expressed as:  
\[e_{d}=\|y-b_{g}\|=min_{i}\{\|y-b_{i}\| \tag{6}\]  
\[b_{i}(h+1)=b_{i}(h)+r_{gi}(h)[y(h)-b_{i}(h)] \tag{7}\]  
Equation (7) was achieved by adjusting matching neuron \(g\) in the neighbor neuron index \(i\) and the kernel \(r_{gi}\) and time \(h\). The function \(r_{gi}\) is a function with reference to the  
Figure 4: Points correlation and proportions.  
Euclidian distance and time. If the learning rate of the model is \(\alpha\) and the location of the two-dimensional network neuron is \((h)\), Equation (7) becomes:  
\[r_{gi}(h)=r\;\big{(}\|l_{g}-l_{i}\|,h\big{)}\alpha(h)r_{gi}(h)=E\left(-\frac{ \left(\|l_{g}-l_{i}\|^{2}\right)}{(2\ell^{2}(h))}\right)\;\alpha(h) \tag{8}\]  
where \(\ell\) is the Gaussian kernel factor (Section 4.1, Equation (1)). During the network training, the learning rate of 0.01 with a batch size of 8 was set on the weight decay of 0.001 at momentum of 0.9. We set the value of \(\alpha(h)\) high to enhance better transformations in the Self-Organizing Map weight points (Figure 5) and the U-matrix (Figure 6) of the permissions. However, the limitation of setting a high value of the learning rate is that changes in the network neurons were not incremented significantly. Consequently, the training parameters must be tested using exploratory analysis before permission can be visibly visualized.  
As shown in Figure 6, there are some regions (in the hexagon upper region) that are darker than others, while others are sparsely populated involving. During the inspection,  
Figure 5: SOM weight positions of the permission input variables.  
Figure 6: SOM U-matrix for Android permissions. The hexagons indicate spaces between network neurons with a binary string \(\{0,1\}\), which forms the Euclidean distance.  
we discovered that permissions presented in the darker regions are those that use traditional mobile features such as text messaging and calls, among others (see Section 4.4). This shows that those regions have related input patterns implying that Android apps in the same category with permissions in the same region do not have necessarily behave the same and request the same permissions. It then means that Android apps in the same cluster can request similar permissions. It is then significant to understand applications and permission clusters by performing exploratory plane analysis to determine Android permission correlation and how they are frequently requested.
## 3 Results and Discussion
### Freezing and Melting Characteristics  
#### 3.2.1 Freezing Process  
Initially the nanocomposite, which is in the liquid phase at room temperature (32 \({}^{\circ}\)C), gets sensibly cooled to the freezing temperature by placing it inside the cold bath maintained at the desired temperature. After sensible cooling, the nanocomposite begins to solidify starting from the outermost surface, which is exposed to the heat-conducting surface. As a result, the outermost layer starts to solidify first. The solidification process continues until the midpoint of nanocomposite solidifies. After complete solidification, sensible cooling of the solidified sample takes place until the nanocomposite reaches \(-3\)\({}^{\circ}\)C. The bath temperatures for freeing experiments were \(-6\)\({}^{\circ}\)C and \(-10\)\({}^{\circ}\)C.  
Figure 5 shows the freezing curve at \(-6\)\({}^{\circ}\)C and \(-10\)\({}^{\circ}\)C for the base fluid and nanocomposites for different volume percentages of GnP from 32 \({}^{\circ}\)C to \(-3\)\({}^{\circ}\)C. The time taken by the base fluid to solidify was 91.5 min, whereas the time taken by the 0.1%, 0.2%, 0.3%, 0.4%, and 0.5% vol. fraction nanocomposites to solidify was 77 min, 71 min, 65 min, 59 min, and 55 min, respectively. Thus, the addition of nanoparticles aided to reduce the freezing time by 15.30%, 22.40%, 28.96%, 35.52%, and 39.89% for the 0.1%, 0.2%, 0.3%, 0.4% and 0.5% vol. fractions, respectively.  
Similarly, at the bath temperature of \(-10\)\({}^{\circ}\)C, the time taken to solidify the base fluid was 55.5 min, whereas for the 0.1%, 0.2%, 0.3%, 0.4%, and 0.5% vol. fractions of nanocomposites, the time taken was  
Figure 5: Freezing curves at from 32 \({}^{\circ}\)C to \(-3\)\({}^{\circ}\)C with a bath temperature of \(-6\)\({}^{\circ}\)C and \(-10\)\({}^{\circ}\)C.  
46 min, 43.5 min, 40 min, 36 min, and 31 min, respectively. Thus, the addition of nanoparticles reduced the freezing time by 12.25%, 15.48%, 16.12%, 25.16%, and 31.61% for the 0.1%, 0.2%, 0.3%, 0.4%, and 0.5% vol. fractions, respectively.
# Translating Speech to Indian Sign Language Using Natural Language Processing
## 9 Results and Discussion
#### Case 3  
Input: Teacher  
Videos shown: [t,e,a,c,h,e,r]  
In Case 3 (as shown in Figures 10 and 11), the user enters \"Teacher\" as the input. There is no video present in the database for \"Teacher\"; therefore, the system breaks the input into letters and shows the videos for each individual letter.  
**Case 4**  
Input: exchange rate  
Videos shown: [exchange rate]  
In Case 4 (as shown in Figure 12), the user enters \"exchange rate\" as the input. The video for the entire input is present in the database and therefore only the video which depicts \"exchange rate\" in sign language is shown to the user.  
**Case5**  
Input: Kangaroo is an animal  
Videos shown: [Kangaroo, a,n,i,m,a,l]  
In Case 5 (as shown in Figures 13 and 14), the user enters \"Kangaroo is an animal\". The sentence/keywords after applying NLP and ISL grammar rules is \"Kangaroo animal\". In this, \"is\" is removed, as it is a stop word. The following videos are shown to the user.  
* \"Kangaroo\" is shown in one video, as the sign language video for the entire word is present in the database.
* \"animal\" is broken into letters and videos for individual letters which are shown, as the sign language video for \"animal\" is not present in the database.  
**Case 6**  
Input: Letter of authority  
Videos shown: [Letter of authority]  
In Case 6 (as shown in Figure 15), the user enters \"Letter of authority\" as the input. The video for the entire input is present in the database and therefore only the video which depicts \"Letter of authority\" in sign language is shown to the user.  
**Case 7**  
Input: 2  
Videos shown: [2]  
In Case 7 (as shown in Figure 16), the user enters the number \"2\" as the input. Since the numbers between 0 and 9 are present in the database, only one video which depicts \"2\" in sign language is shown to the user.  
**Figure 16**: Case 7. Available video for number \({}^{2}\).  
Figure 14: Case 5. Available video for letter ’a’.  
**Case 8**  
Input: 30  
Videos shown: {3, 0}  
In Case 8 (as shown in Figures 17 and 18), the user enters the number \"30\" as the input. There is no video present in the database for the number \"30\"; thus, the system breaks the input into two components, i.e., \"3\" and \"0\", and shows the separate videos for both of them.  
**Case 9**  
Input: How are you?  
Videos shown: {How, you?}  
In Case 9 (as shown in Figures 19 and 20), the user enters \"How are you\". The sentence/keywords after applying NLP and ISL grammar rules is \"How you\". In this, \"are\" is removed, as it is a stop word. Since there is no video present in the database for \"How are you\", the sentence is broken into words, i.e., \"how\" and \"you\".  
Case 10  
Input: Good Evening  
Videos shown: [Good Evening]  
In Case 10 (as shown in Figure 21), the user enters \"Good Evening\" as the input. The video for the entire input is present in the database and therefore only the video which depicts \"Good Evening\" in sign language is shown to the user.
## 2 Related Work
Monitoring and dealing with a large number of casualties is an important key parameter in disaster response scenarios. The CodeBlue platform [25] provides such a protocol along with a software framework that integrates eHealth devices (such as wearable vital sign sensors, handheld computers and location-tracking tags) to handle disaster response and emergency care scenarios. The prototype proposes to integrate device discovery, robust routing, traffic prioritization, security and RF-based location tracking. In a disaster scenario, handheld computers carried by first responders receive and visualize multiple patients' vital signs on the implemented application. Based on these observations, triage operation can help to optimize the chances of survival. Along with these objectives, security and privacy are studied according to legal ramifications specific to the USA regulations. The platform presented in this paper does not focus on disaster scenarios and considers a more general everyday health-care usage. In addition, Internet next-generation communication standards are used (IPv6 and LTE). In a recent European project (IIP) [26], one of the priorities is to create a reliable, stable and universal implementation of IPv6 network services, including DHCPv6, DNS and mobility management mechanisms, as well as applications, including VoIP and IPTV. With respect to these objectives, one target concerns eHealth. By deploying wireless medical sensor technologies over IPv6 to enhance connectivity and security, delivering healthcare services remotely will be possible. One of the objectives is the removal of NAT to allow easy access for service and/or devices and perform remote configuration and maintenance, which is an important issue for the elderly and disabled persons living alone. Our platform also focuses on the future deployment of IPv6, but considers a vehicular setting as a special use case for integration.
# Reliability and Efficiency of Metamodel for Numerical Back Analysis of Tunnel Excavation
## 3 Datasets  
The Fast Lagrangian Analysis of Continua v7.0 (FLAC), which is a finite-difference-based numerical analysis program developed by Itasca [14] and widely used in tunnel design and research fields, was used to develop a database for the metamodel and for performing the GSA. The Hoek-Brown model was used as the constitutive model, and a total of 100,000 analyses were performed by changing the ten values of the five ground parameters individually, as described later in Section 3.2. The total of 100,000 numerical analyses required 36 days, 18 h, and 37 min for complete execution. The vertical displacements at the tunnel crown according to the 100,000 analyses were in the range of \(-1.6\times 10^{-2}\) to \(-3.2\times 10^{-6}\) m, and the negative sign indicates the direction of gravity.
## 4 Results and Discussion
### Experimental Results of the Process Parameter Optimizations  
The FA was applied in order to search for the optimal adjustable process parameters of each of the test 299 samples, with the parameter settings of the algorithm given in Table 4. The FA is an iterative method, and a preassigned end condition is therefore needed. In this paper, we set the maximum number of iterations to 50 and the number of initial firefly solutions to 10. The average time spent searching for the optimal process parameters of each test sample was about 0.62 s. Additional details concerning the FA are shown in Figure 4.  
In our experiments, the 299 test samples were used to evaluate the performance of each method when searching for the optimal process parameters. The basis of evaluation  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Attractiveness, \(\beta_{0}\) & 1.0 \\ \hline Light Absorption Coefficient, \(\gamma\) & 1.0 \\ \hline Number of Initial Firefly Solutions & 10 \\ \hline MCL & 50 \\ \hline \(\sigma\) & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter settings of the FA.  
Figure 4: Flowchart of the FA used for optimizing the process parameters.  
is the success rate: how often the final product attributes fall in their reasonable quality ranges. Our experiments used the following combinations of methods: RF+PSO, RF+ABC, RF+Firefly, SVGG+PSO, SVGG+ABC, and SVGG+Firefly, the results of which are shown in Table 5. There we see that all the resulting test sample product attributes met their product quality requirements when the SVGG+Firefly combination was used, with its success rate of 100% clearly outperforming the other combinations.
# A Low Power IoT Sensor Node Architecture for Waste Management Within Smart Cities Context
## 4 Node Architecture
### Filling Level Sensor
Due to the wide range of sensors available on the market, the one used for the prototype was chosen putting the cost requirement in first place. For this reason an HC-SR04 sensor was employed: this device is available on the market at even just 1 euro and, according to its datasheet [68], it is characterized by a 15 mA working current. The HC-SR04 sensor is provided with 4 connectors: the \(V_{CC}\) and Ground pins that allow to power it and the Trigger and Echo pins that are used to generate and sense the emitted and reflected ultrasound signals respectively. Once the time interval \(t\) between signal emission and receiving is measured, the distance \(d\) can be calculated according to the following formula:  
\[d=\frac{v_{spread}\cdot t}{2} \tag{1}\]  
where \(v_{spread}\) is the ultrasonic spread velocity in the air. In fact, such a speed depends on temperature: at a temperature of 15 \({}^{\circ}\)C, for instance, the speed is \(v_{spread}=340.7\,\mathrm{m/s}\). Formally, considering that \(T\) is the temperature at which the sensor is exposed (expressed in \({}^{\circ}\)C), then the actual spread velocity can be evaluated as follows:  
\[v_{spread}=331.4\,\frac{m}{s}+0.62T. \tag{2}\]  
In other words, according to Equation (2), if a variation of 20 \({}^{\circ}\)C occurs (which, incidentally, is feasible) then it would cause a variation of speed less than 4%. Since a bin is approximately 1 m tall, then such a variation could be negligible when the filling level is measured. Obviously, the filling level raises as the bin is filled up (it could be thought as the bin would get shorter), hence such a variation constantly becomes more negligible, if finer estimates are needed, then a temperature sensor could be embedded within the node so as to compensate the changing of the spread velocity. Anyhow, for the prototype described in this paper the temperature compensation is avoided due to the reason just explained. Eventually, Equation (1) is implemented by the microcontroller of the IoT node which, as it will be covered in detail soon, is also in charge of supplying the sensor only when it is strictly necessary.
## 3 Methodology
### Other Implementation Issues  
When performing topographic correction for the mountainous areas with serious undulation, or images acquired with low sun elevation angles, shadow effects cannot be ignored. Basically, two types of shadow may appear [20,49]. For sun-averted slopes with big incident angle exceeding 90 degree, self-shadow occurs. For sun-facing areas obstructed by other objects and receiving no direct solar radiation, casting shadow happens. Shadow areas are relatively dark in the image, and thus they tend to suffer from over-correction most. Some studies even directly detect and mask out the shadow areas before topographic correction, and simply leave them alone [20]. However, low signal from shadow areas due to receiving only the diffuse radiation does not mean there is no signal in the optical satellite images (_versus_ radar images). A lot of contributions reported various methods to restore information of shadow areas [50, 51, 52]. In this study, our model also leads to overcorrection to some extent for the shadow areas, and we attempt to restrain the over-correction by simply using an adjusting factor. Before correction, the shadowed pixels are detected based on the algorithm described in [49]. Then, for each NDVI-dependent group of atmospherically-corrected pixels, the average reflectance of shadow areas is evaluated, and the average reflectance of non-shadow areas within this NDVI interval is also calculated. The ratio of the two average reflectance is employed as the adjusting factor for the over-correction pixels in shadow areas.
## 1 Introduction
Forage productivity and quality through the essential structural contents of feed values (e.g., crude protein, green and dry matter, ash, neutral detergent fibre, etc.) can be easily estimated by statistically upscaling [14; 38; 39; 40; 41; 42; 43]. Although such approaches are accurate and suitable for measuring pasture quality, they are destructive [43; 44] and cannot be scaled to larger areas. Proximal sensing is carried out by equipment such as field spectroradiometers (Field-Spec), ultrasonic sensors, and sonars, with the intent of estimating morphological parameters, e.g., AGB, LAI, sward height, soil moisture, etc., [45; 46; 32; 41; 47] from multispectral wavelengths (i.e., red, blue, green, and near-infrared) [48; 39] or hyperspectral reflectance bands (i.e., 10-20 nm). However, the influence of soil and ground reflectance (albedo) can interfere with spectral distinction [41] and introduce errors due to instrumental instability [47]. In addition, intra-paddock pasture composition variability due to grazing management (i.e., stocking rates) may not be adequately quantified with this approach [49; 32]. In summary, pasture monitoring using direct or proximal approaches can effectively calibrate ground-based pastures, e.g., biomass estimation and retrievals of biophysical parameters for validation purposes. However, such methods can be time-consuming and/or labour-intensive and are often unsuitable for application over a large area [50]. Like other techniques, remote sensing is susceptible to problems associated with technology limitations (i.e., errors from cloud cover, noise, atmospheric and geometric correction, and radiometric resolution) [51]. Global studies have highlighted the possibilities of using remote sensing to monitor pasture cover and biomass while reducing error rates [52; 53; 54]. Most previous work has, however, focused on global, regional, or sub-regional monitoring [55; 56; 57]; much less attention has been paid to pasture remote sensing at the farm or paddock level, likely because most remote sensing applications have been at larger scales [58; 33]. The emergence of newer satellite constellations (e.g., Sentinel and PlanetScope [59]) could be expected to premise innovation in pasture monitoring at a level that can be carried out at more frequent intervals (1-5 days). The primary objectives of this paper are to (i) review existing satellite and UAS applications in pasture monitoring; (ii) investigate existing approaches for the management of pasture traits, productivity, botanical composition, and pasture degradation; and (iii) explore barriers to the adoption of satellite driven technology by end users.
## 4 Wireless Communication Technologies for IoT-Enabled Data Transmission
### Wi-Fi and Cellular Communication  
The transmission distance of Wi-Fi is up to 100 m, which is comparable to Zigbee [81; 83]. Costs are relatively low because Wi-Fi uses an unlicensed band, but this can also lead to interference with other devices sharing the same band [88; 89]. Therefore, the Quality of Service is not guaranteed for this technology [88]. However, it enables afaster data transmission speed than other short range communication technologies and many facilities along a supply chain already have a Wi-Fi connection [89]. The high data rate and therefore also higher power consumption can be unfavorable for direct connection to sensor devices that depend on battery power. The same is applicable for cellular communication technologies such as 2G, 3G, 4G and 5G which enable a high data rate and thus have a high power consumption [81]. IoT-enabled sensors for ambient parameter monitoring require only a low data rate, shifting the preferences towards a communication technology that is more energy efficient [80]. Nevertheless, applications that transmit larger amounts of data can benefit from cellular communication because they are not duty cycle limited [90]. An advantage of cellular networks is their large coverage for long distance data transmission of several kilometers due to an already existing infrastructure [80; 90]. Moreover, modern specifications such as 5G offer a very high Quality of Service and the possibility of connecting a massive number of devices [88].
# Research on Optimization Design of Tunnel Blasting Scheme Adjacent to Buildings
## 3 Numerical Simulation to Verify the Feasibility of the Solution
### Blast Pressure Induced by Rock Blasting  
The blasting equivalent load method and the EOS-JWL method [25; 26] have been adopted by many scholars as the main means of rock blasting simulation, of which the blasting equivalent load method has higher computational efficiency compared with the EOS-JWL method. In this study, the equivalent dynamic load of triangular blasting was used for calculation, and the load time curve is shown in Figure 4. The boost time is taken as 100 us, the positive pressure action time is 600 us, and the duration of the blast seismic waves is taken as 0.6 s. The average burst pressure of the explosive is as follows:  
\[P_{D}=\rho_{e}D^{2}/2(1+\gamma) \tag{1}\]  
where \(P_{D}\) is the average initial pressure of the explosive blast; \(\rho_{e}\) is explosive density; \(D\) is explosive blast speed; and \(\gamma\) is the isentropic index of the explosive.  
The initial average pressure \(P_{0}\) of the hole is calculated using the following formula:  
\[P_{0}=\left[\rho_{e}D^{2}/2(1+\gamma)\right](d_{c}/d_{b})^{2\gamma} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & _E_/**MPa** & _μ_ & _γ_/**(kN\(\cdot\)m\({}^{3}\))** & _c_/**kPa** & _γ_/(c\({}^{\circ}\)) \\ \hline Vegetative fill & 6 & 0.4 & 17 & 10 & 15 \\ Powdery clay & 15 & 0.37 & 19 & 21 & 18 \\ Strongly weathered granite & 65 & 0.29 & 25 & 50 & 40 \\ Mediumly weathered granite & 450 & 0.25 & 27 & 200 & 50 \\ Slightly weathered granite & 2500 & 0.21 & 29 & 310 & 55 \\ Building & 15 & 0.16 & 22 & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 1: Formation and building parameters.  
Figure 3: Numerical model.  
Equivalent calculations of the blasting load on the hole wall were carried out based on [PERSON]'s principle, which has also been adopted by many scholars [6, 27, 28, 29]. The equivalent form of the blasting load is shown in Figure 5. Explosive properties in this project are shown in Table 2.  
\[P_{e}=(2r_{0}/a)P_{0} \tag{3}\]  
where \(P_{e}\) is the equivalent load, \(a\) is the distance between adjacent holes, and \(r_{0}\) is their diameter.