esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Synthesis of Graphene Oxide  
Graphene oxide (GO) was obtained by the chemical oxidation of graphite. The process was carried out with the use of specific reagents and under precise conditions. Initially, 1 g of graphite (Sigma-Aldrich, ServyLab, Sao Leopoldo, Brazil) was placed in a 500 mL volumetric flask containing 60 mL of sulfuric acid and stirred magnetically for 15 min in an ice bath. Afterward, 3.5 g of potassium permanganate was added, and the mixture was stirred for an additional 2 h. Subsequently, 200 mL of distilled water was slowly added to the solution followed by 3 mL of hydrogen peroxide. The resulting graphite oxide was then centrifuged with distilled water, a hydrochloric acid solution (10% \(v/v\)), ethanol, and acetone. To obtain GO, the material obtained previously was subjected to 30 min of ultrasonic treatment for exfoliation [29].
# A SqueezeSAR Spatially Adaptive Filtering Algorithm Based on Hadoop Distributed Cluster Environment
## 1 Introduction
However, spatially adaptive filtering requires the filtering of N(N\(-\)1)/2 interferograms, which consumes a large amount of memory during the entire data processing process, and the use of multi-core parallelization leads to memory overflow and other problems. In this study, we: (1) focus on the limitations of spatial adaptive filtering in the implementation of the SqueeSAR algorithm, which requires a large amount of memory and significant time for pixel-by-pixel spatial averaging; and (2) build a YARN cluster based on the Hadoop framework, store the data in the dedicated storage nodes of the YARN [39] cluster, use the Spark distributed computing engine to split the data into multiple pieces and distribute them to each computing node for processing through memory interaction, and merge the results to the master node through the aggregation operator to complete the interferogram phase filtering process. The spatially adaptive filtering algorithm was tested on a YARN cluster consisting of three computing nodes with 24 cores and was compared with the conventional (serial) filtering algorithm. Spark distributed computing can achieve up to a 2.15 times improvement compared with the conventional method.
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 1 Introduction
### Validity  
Validity refers to the quality and trustworthiness of transmitted data. It ensures that data is legitimate, accurate, and conforms to predefined criteria, standards, or specifications. Various methods determine data validity, including accuracy, timeliness, and usability [44]. In addition, validity assesses the fitness of data for the desired purpose.
## 1 Introduction
[17] can be expressed as  
\[\sigma t=a_{0}K_{IC} \tag{1}\]  
where \(a_{0}\) is a constant. According to the comparisons made by [PERSON] et al. [17], \(a_{0}=15.4\) for the types of soil considered, and \(a_{0}=13.6\) for the types of soft rock considered. Further research has generated a large amount of experimental data showing a strong correlation between mode-I fracture toughness and tensile strength [18; 19; 20; 21]. These experimental data were gathered using a variety of test methods, and many types of rock were tested. Some test data [18] were obtained under dynamic loading conditions while others were obtained under static or quasi-static loading. The tensile strength of rock varies considerably when measured by different methods, and the heterogeneity of the rock tested will influence the tensile strength value. Similar issues may arise with fracture toughness tests [22]. Therefore, it is necessary to assess the effect of each factor present in the experimental setup--such as the temperature, test method, loading rate, etc.--on the relationship between mode-I fracture toughness and tensile strength. In summary, previous studies have obtained empirical formulas that are useful for estimating mode-I fracture toughness. However, these empirical formulas differ from each other. Furthermore, the results for mode-I fracture toughness calculated by these empirical formulas differ somewhat from experimental data. This is because important factors that are capable of causing significant changes in rock mechanics parameters are not considered. One such parameter is exposure to high temperature. Many underground projects are located several kilometers below the surface of the earth, and the surrounding rocks are often in a high-temperature environment. The relationship between the mode-I fracture toughness and tensile strength of rock changes due to temperature and this effect on the mechanical parameters of the rock cannot be ignored, especially where temperatures are high. It is therefore necessary to consider the mode-I fracture toughness and tensile strength of rock at different temperatures and thereby use the correlations between them to achieve more accurate predictions of the mode-I fracture toughness value. Moreover, there has been a lack of in-depth research into the reasons behind the influence of temperature on the relationship between mode-I fracture toughness and tensile strength. Therefore, this article builds on previous research by testing the mode-I fracture toughness and tensile strength of rocks after heat-treatment to different temperatures. Theoretical analysis and mesoscopic observation are then used to study the effect of temperature on the correlation between mode-I fracture toughness and tensile strength.
# Translating Speech to Indian Sign Language Using Natural Language Processing
## 9 Results and Discussion  
**Case 1:**  
Input: New Delhi is the national capital of India  
ISL sentence: New Delhi national capital of India  
Videos shown: [New Delhi, n,a,t,i,o,n,a,l, c,a,p,i,t,a,l, of, India]  
In Case 1 (as shown in Figures 7 and 8), the user enters the sentence \"New Delhi is the national capital of India\". The sentence/keywords after applying NLP and ISL grammar rules is \"New Delhi national capital of India\". In this, \"is\" is removed, as it is a stop word. The following videos are shown to the user.  
1. \"New Delhi\" is shown in one video, as the video for the same is present in the database. This is an example of how the system identifies multiple words/phrases in the sentence for which videos are present in the database.
2. The keywords \"national\" and \"capital\" are broken down into letters and a sign language video for each letter is shown to the user as the video, for neither \"national\" nor \"capital\" is present in the database.
3. The videos for keywords \"of\" and \"India\" are shown to the user, as the sign language videos for both are present in the database and there is no need to further break them into letters.  
Figure 7: Case 1.Available Video for ’New Delhi’.
# Global IoT Mobility: A Path Based Forwarding Approach
## 5 Simulation and Cost Evaluation
### Packet Delivery Cost Evaluation for Global vs. Micro Mobility Support  
Figure 9 presents the results of a simulation run of 1800 s for micro vs. global mobility in IP-over-ICN. Fifty MNs were simulated to move freely and randomly within each network domain (in case of micro mobility) and across domains (in case of global mobility). Due to the high dependability of the mobility costs on the network topological aspects, the latter are taken into account when evaluating the Packet Delivery Cost (PDC) and Signalling Cost (SC) of the solution. Therefore, PDC and SC are evaluated in Bytes \(\times\) Hops/Sec over the simulation period. The figure shows that the packet delivery cost (PDC) for IP-over-ICN to support global mobility is approximately \(4\times 10^{6}\) Bytes \(\times\) Hops/Sec compared to \(3\times 10^{6}\) Bytes \(\times\) Hops /Sec for micro mobility support. The slight increase shown in the packet delivery cost is due to the inevitable fact that path lengths (and hence packet delivery costs) increase when communication is established inter-domain rather than intra-domain.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Size in Bytes** \\ \hline Unpublish with Implicit Unsubscribe (UPubiUnsub) & 166 \\ \hline Publish Request & 160 \\ \hline Start Publish & 166 \\ \hline Publish with Implicit Subscribe (PubiSub) & 166 \\ \hline ICN payload packet header & 96 \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of mobility messages and their sizes [13].  
Figure 9: Packet delivery cost (PDC) for IP-over-ICN micro and global mobility support.
# Big Data-Driven Deep Learning Ensembler for DDoS Attack Detection
## 4 Simulation and Results
### Evaluation Metrics  
This study's assessment metrics match DDoS detection problems. The structure was evaluated from several aspects to fully understand the experiment using these metrics:  
1. Accuracy, precision, recall, and F1-score: These measures assess the model's DDoS attack detection reliability. Recall measures the model's capacity to find all genuine positives, whereas precision measures positive prediction accuracy. Precision and recall are balanced in the F1-score.
2. Specificity and G-mean: G-mean shows how well the model handles positive and negative situations, while specificity shows how well it identifies negative cases to avoid false alarms.
3. Latency and throughput: These measures assess real-time processing. Latency, or input response time, is the average sample categorization time. The model's throughput reveals its data processing capability and network traffic capacity.
4. Wilcoxon and ANOVA tests verified the model's stability and scalability across datasets.  
These metrics guarantee that the review process tackles the introduction's issues, such as reliable DDoS detection, limiting false positives, and real-time flexibility.
# Use of Phase Change Materials for Food Applications--State of the Art in 2022
## 2 Food Applications of PCM
### Food Storage and Transport
#### 2.2.10. Compartments That Are Fixed on a Van, Truck, or Trailer
The results showed an improved temperature control performance, and also significantly lower energy costs compared with the conventional refrigeration. Ref. [75] (see [50]), presents another modification, where the external part for cold production, diesel driven, is replaced by a cold storage using PCM which can be charged by connecting to an external refrigeration system through valves once the truck is for example in a warehouse or depot. The PCM tested was a water-salt solution with melting point of \(-26.8\,^{\circ}\)C, macro-encapsulated in flat plastic capsules in the storage. Prototype testing revealed the need of some 360 kg of PCM, having a weight comparable to that of an onboard conventional refrigeration system, and that if an external refrigeration system with a COP between 1.0 and 1.5 is used, the new PCM system would cut down cost by more than 50% at Australian ambient conditions. In a follow-up work [76] a numerical model was used to simulate the performance of the entire system. Each of the approaches in R&D, just adding PCM to the overall system, changing the means of cold production, or even its complete shift off the van or truck to external facilities and non-driving times, has individual advantages. Installing PCM inside the refrigerated compartment combined with cooling by an external facility brings many of these advantages together. The approach is commercially used, and successful for several years. An example is a system for PCM cooled vans etc., which uses PCM encapsulated in metallic plates, developed by PLUSS Advanced Technologies Ltd. As described in [45], the plates are mounted inside the transport compartment (Figure 11), and are charged by an external electrically driven compression cooler during non-operational hours, e.g., at night. Figure 11: PCM filled metallic plates mounted in a van for cooling (source: both PLUSS Advanced Technologies Ltd.). During transport of products the PCM assures that the required temperature is maintained, for up to 16 h, thereby enabling the transportation of goods like food, while minimizing the dependency on diesel/petrol for continuously running cooling units, and reducing operational costs [40] as well as emissions. According to [45], the payback period is estimated to be about 4 to 6 months, and there were more than 200 trucks running on roads at the time of publication. To develop the application of the plates in more kinds of trucks, PLUSS is partnering with other companies. More information is available at the company's website ([[https://pluss.co.in/logistic/](https://pluss.co.in/logistic/)]([https://pluss.co.in/logistic/](https://pluss.co.in/logistic/)), accessed on 27 September 2021). Similar commercial solutions are also available from other manufacturers in India (Ice Make), Turkey (Eutectic car), Lithuania (Carlsen Baltic), and the USA (Cold Car USA), according to their websites.
## 2 The Study Area
### Structural Geological Context of the Studied Area  
A deterministic seismic risk map was realized from 21 capable geological faults near the Chone canton (Table 2). The most appropriate approach to estimate the maximum magnitude of an earthquake is a relationship with the fault rupture length [38]. The first  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sector (Epicenter)** & **Date** & **Magnitude (Mw)** \\ \hline Bahia de Caráquez & 3 May 1896 & 7.1 \\ Esmeraldas & 31 January 1906 & 8.8 \\ Pedernales-Muisne & 1 June 1907 & 7.4 \\ Pedernales & 14 May 1942 & 7.9 \\ Esmeraldas & 23 October 1944 & 6.7 \\ Bahia de Caráquez & 16 January 1956 & 7.4 \\ Esmeraldas & 19 January 1958 & 7.6 \\ Bahia de Caráquez & 4 August 1998 & 7.1 \\ Pedernales & 16 April 2016 & 7.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Seismic subduction events in coastal Ecuador [30; 38].  
Figure 2: Study area within an active fault map.  
analysis consists of applying equations proposed by [PERSON] in 2008 [39], relations for each type of capable fault:  
\[\text{Strike slip faults: Mw}=5.56+0.87\text{Log}(\text{L}_{\text{f}}) \tag{1}\] \[\text{Normal faults: Mw}=6.12+0.47\text{Log}(\text{L}_{\text{f}})\] (2) \[\text{Reverse faults: Mw}=4.11+1.88\text{Log}(\text{L}_{\text{f}}) \tag{3}\]  
where \(\text{L}_{\text{f}}\) is the length of the geological fault.  
A second analysis is to obtain the peak ground acceleration of the rocks from geological faults, applying the equation proposed by [PERSON] and [PERSON] in 1990 [40] by using the proposed equation:  
\[PGA_{rock}=\frac{(10^{0.41 Me-log_{10}(Hf+0.032\times 10^{0.41 Mc})}-0.0034 Hf+1.3)}{980} \tag{4}\]  
where \(Hf\) is the depth of fault and \(Me\) the estimated magnitude.  
The seismic risk map indicates that the maximum magnitudes expected from the full activation of a tectonic segment of geological fault is in the order of \(6.17\leq\text{Mw}\leq 7.18\) and rock acceleration is between \(0.28\leq PGA\leq 0.36\). A second scenario considers the activation of \(60\%\) of the length of the geological fault, where the expected magnitudes are in the order of \(5.79\leq\text{Mw}\leq 6.79\) and the acceleration in the order of \(0.22\leq PGA\leq 0.31\). The Ecuadorian Construction Standard named NEC-11 [41] considers \(60\%\) of the potential of a seismogenic structure, as these values may be closer to the recurrence and seismic values expected on the coast of Ecuador. Using data from seismic stations and environmental vibration measurements, it is possible to compare the spectral ratio technique with respect to a reference site with that of the spectral ratio (H/V) obtaining consistency in the fundamental frequency values [42] (Figure 3). The phenomenon of seismic amplification in soft sediments near the surface has been studied for several decades, so knowing the characteristics and behavior of soil deposits in high seismic activity is fundamental in order to determine the amplifications of seismic movements and identify resonance conditions in buildings [43].  
Figure 3: Maps of capable geological faults determining PGA (**A**) and magnitudes (**B**) for the province of Manabi.
# Comparison of Fatigue Performances Based on Shape Change of Rail Fastening Spring
## 2 Finite Element Analysis  
Using ABAQUS, a commercial finite element analysis program, nonlinear finite element analysis was performed to derive the tensile stress value during the initial fastening process as well as the stress fluctuation value owing to the vertical deflection of the rail caused by train use. The model configuration of the KR-type fastening spring shown in Figure 2a was constructed using a 3D Scan and a hexahedral element, C3D8R, was applied. Figure 2b shows the trilinear material properties of the model based on the tensile coupon test results of the SPS9 spring steel. Partially fixed boundary conditions were assigned to the interfaces between the fastening spring-to-rail and fastening spring-to-guide plate in the rail fastening assembly. The x-, y-, and z-axis of the coordination in Figure 2a represent the transverse, vertical, and longitudinal directions of the rail, respectively. In the fastening spring-to-rail boundary region, the z-axis displacement and the rotation around the x-axis direction were restrained while the other degrees of freedom were released. In the fastening spring-to-guide plate boundary region, the x- and y-axis displacements and the rotations around the x- and y-axis directions were restrained, while the other degrees of freedom were released. These boundary conditions for the analysis were assumed to simulate the structural behavior at the interfaces with the fastening spring.  
Figure 1: KR-type rail fastening system: (**a**) assembled configuration; (**b**) geometry of fastening spring.  
Two steps of displacement-controlled loading were applied to simulate the installation of the fastening spring and vertical displacement during train use, as shown in Figure 3. Step 1 simulated the initial fastening process by torque tightening. The top surfaces of the two fastening springs at the free ends were tied to the center, and displacement was applied to the center control point to simulate screw spike tightening. The displacement level in Step 1 will be discussed in Section 3. In Step 2, a 1 mm vertical displacement level in the y-axis direction for the fastening spring was applied to the center of the fastening spring-to-rail contact area based on the field measurements to simulate the fastening spring movement during train use.  
Figure 3 shows the contours of the maximum principal stresses from the finite element analysis. As shown in Figure 3a, two stress concentration locations are identified and designated as \"Mode 1\" and \"Mode 2.\" In Step 2, the stresses at these locations were slightly released with the downward vertical displacement caused by train use. This observation indicated that the two stress concentration points could potentially be the fatigue crack initiation points. Figure 4 shows the failure patterns from fatigue testing of the KR-type fastening spring, which are consistent with the two critical locations of stress concentration from the finite element analysis [16].
## 4 Simulations
### Performance Comparison
#### 4.3.1 Comparison with MM-JAD (the Method in [29])
At 750 Hz, the main lobe of the MM-JAD method is approaching that of the desired beampattern, but the sidelobes still deviate significantly. Meanwhile, the main lobe and sidelobes of the proposed method are both approaching that of the ideal beampattern. At 1 kHz, the two methods are both robust enough to approximate the target beampattern when the errors occur. The above indicates that, although Figure 6 shows that the MM-JAD method approaches the desired beampattern more closely than the proposed method below 750 Hz when disregarding element imperfections, the directivity of the MM-JAD method significantly decreases once there are element inconsistencies. Therefore, in practical applications, the proposed method will have an advantage over the MM-JAD method. Table 2 lists comparisons with the averaged WNG and DI performance of the two methods. At 250 and 500 Hz, the WNG of the MM-JAD method below \(-15\) dB and the corresponding DI value below 0 dB indicate that the energy radiated by the line array to the broadside is lower than the averaged energy radiated by the array to the front half-plane. At 750 Hz, as the WNG of the MM-JAD method is above 0 dB, the DI reaches 7.3. At 1 kHz, due to the WNGs, the two methods are all above 12 dB, and their DI values are also relatively close. Comparing Tables 1 and 2, it can be observed that with the perturbations in the spatial responses of the loudspeakers, the WNG and DI of the proposed method remain almost consistent. This also indicates that the beamformer with Tikhonov regularization can mitigate the impact of transfer function errors on the beamformer. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Freq (Hz)**} & \multicolumn{2}{c}{**WNG (dB)**} & \multicolumn{2}{c}{**DI (dB)**} \\ \cline{2-5}  & **-MM-JAD** & **Proposed** & **-MM-JAD** & **Proposed** \\ \hline
250 & \(-61.8\) & \(-1.1\) & 8.5 & 6.4 \\
500 & \(-24\) & 5.8 & 8.5 & 8.3 \\
750 & \(-0.1\) & 13.6 & 8.4 & 9.2 \\
1000 & 12.1 & 12.4 & 8.4 & 8.5 \\ \hline \end{tabular}
\end{table} 
Table 1: The WNG and DI performance (dB) of -MM-JAD and the proposed method at 250 Hz, 500 Hz, 750 Hz, and 1 kHz. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Freq (Hz)**} & \multicolumn{2}{c}{**WNG (dB)**} & \multicolumn{2}{c}{**DI (dB)**} \\ \cline{2-5}  & **-MM-JAD** & **Proposed** & **-MM-JAD** & **Proposed** \\ \hline
250 & \(-15.6\) & \(-1.1\) & \(-1.8\) & 5.6 \\
500 & \(-15.4\) & 5.8 & \(-2.1\) & 8.1 \\
750 & 0.3 & 14.0 & 7.3 & 9.2 \\
1000 & 12.6 & 12.8 & 8.3 & 8.4 \\ \hline \end{tabular}
\end{table} 
Table 2: The averaged WNG and DI performance (dB) of MM-JAD and the proposed method with the perturbation added to the frequency response of the loudspeakers at 250 Hz, 500 Hz, 750 Hz, and 1 kHz.
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 5 LoRaWAN Analysis
### Single Device Maximal throughput and MTU  
The goal of this experiment is to evaluate the maximal throughput that a single device can obtain. This depends more on the physical layer than on the MAC protocol, but it gives an idea of what is possible when using LoRaWAN. The experiment was conducted by having a device send data as soon as the channel limitations and the protocol allow it. Tests were conducted with six channels of 125 kHz and using spreading factors from 7-12. No MAC commands were sent, so the size of the MAC header was always 13 bytes. The results, depending on the payload size, are visible in Figure 9, which are measured over about 100 packets transmitted in each test. Fifty-one bytes are the maximum payload size allowed by the implementation used for the tests.  
This experiment revealed that at low packet sizes, the limiting factor was not the channel duty cycle limitations, as could have been expected, but the duration of the receive windows. Indeed, the device has to wait for the two downlink receive windows following the transmission to be over before sending another packet. However, this situation is not the use case LoRaWAN was designed for: the goal of LoRaWAN is rather to manage large quantities of devices that send a few bytes of data from time to time.  
In the tests above, the MAC header is always 13 bytes. However, in practice, the LoRaWAN header can be a variable size between 13 and 28 bytes. Moreover, the maximum size of the frame depends on the data rate used [23], and LoRaWAN does not have a mechanism to split large payloads over multiple frames. As of the current specification, the application above LoRaWAN has no way of knowing what the maximal size of the packet that it will be able to send in the next transmission is, which might be problematic. A conservative approach is to never try to send more than the smallest maximum payload size, which is 36 bytes, but this results in a loss of capacity if a large amount of data has to be sent, as well as lower throughput, as shown in the results in Figure 9. This is relatively easy to address in a future LoRaWAN specification revision, either by adding a fragmentation mechanism or by informing the upper layer of the MTU from MAC protocol.