esa-sceva/satcom-chunk-collection huggingface dataset:
# A Wireless Hand Grip Device for Motion and Force Analysis
## 2 Materials and Methods
### Software Implementation
#### 2.2.1 Sending Data from Control to Server  
Data collected by the sensors is sent by the Arduino component via Bluetooth component. CSV format has been chosen among the different transmission package formats. Initially, CSV format was used for visual inspection, and it provides an easily readable format. With CSV format, debugging has been an easy job. Once the initial development tasks were completed, other binary formats were evaluated. After performance testing, we concluded that Bluetooth connection bandwidth, even low band rates, is enough to transmit in CSV text format. For this reason, even though CSV format was only considered for debugging and testing, the decision to maintain it as final transmission protocol was taken.  
Figure 4: PCB prototype.  
Data sent from device looks like:  
RotationX, RotationY, RotationZ, Pressure1, Pressure2, Pressure3, Pressure4, Force1, Force2  
15.2,4.52,7.236,0.01,0.9567,0,0,0.4599,0.2387# (1)  
Rotation (angle) is expressed in degrees, while Pressure and Force measurements are values scaled between 0 and 1. At the end of each data package, a control character (#) has been added to mark the end of package.
## 4 Discussion
### Complementarity of Structural and Biochemical Information  
Water losses through transpiration might result in a drop in turgor pressure. This induces changes in the leaf configuration, affecting the \(f_{\rm esc}\) and fPAR\({}_{\rm chl}\). The plant's iso hydricity determines whether it will rather drop its turgor pressure or slow down its photosynthesis [45]. The drop in turgor pressure and the structural changes associated with it take place at the diurnal timescale [46], which is why diurnal observations of the FCVI (or any other structure indicator) are necessary in order to link canopy structure to drought stress. As, the \(\epsilon_{760}\) is only sensitive to the biochemical component of SIF emission, FCVI and \(\epsilon_{760}\) provide independent, yet complementary information on the instantaneous stress conditions. It is interesting to note that detecting the structural reaction requires less strict requirements regarding spectral resolution compared to SIF observations, but measuring the full diurnal cycle of FCVI is needed to link it to leaf angle and water stress changes. Because of the absence of full diurnal measurements, satellite-based SIF might be more reactive than satellite-based vegetation indices [47], despite the structural and biochemical reaction taking place simultaneously.  
It is worth noting that water limitation also impedes vegetation growth, lowering its green biomass, which, in turn, affects various remote sensing metrics, including the NIR reflectance, and thus the FCVI. Short- and long-term variations in FCVI therefore represent other processes. The combined long- and short-term dynamics, affecting FCVI are visible on Figure 6; the upward trend in FCVI represents the growth of the lettuce plant, whereas the diurnal variations in FCVI are due to changes in the leaf angle.
## 2 Background  
BCH codes are cyclic block codes encoded by the generator polynomial \(g(x)\) over the \(GF(2)\). The roots of this polynomial equation reside in the extended field, also known as the splitting field, \(GF(2^{m})\). Let \(\phi_{i}(x)\) be the minimal polynomial of an arbitrary element \(\beta^{i}\), then the generator polynomial for BCH code with \(t\) error correction capability is given by the following equation:  
\[g(x)=LCM(\phi_{1}(x),\phi_{2}(x),...,\phi_{2t}(x)) \tag{1}\]  
Narrow sense BCH codes use primitive element \(\alpha^{i}\) for the minimal polynomial with \(i\) starting from one. For simplicity, the narrow sense BCH code decoder is discussed and implemented in this paper, and it could be easily extended for other general BCH codes [3]. The parity bits are then generated using the equation \(p(x)=m(x)\:mod\:g(x)\), and these parity bits are concatenated to form the message polynomial \(m(x)\). This concatenation is given as:  
\[c(x)=m(x)\cdot x^{deg(g(x))}+m(x)\:mod\:g(x) \tag{2}\]  
The generated parity bits are then stored in the spare area allocated in the page within the flash memory device. In general, the hard decision BCH decoder has three steps in the decoding process: syndrome generation, key-equation solver, and an error locator.
## 4 Research Methodology
### Model Design  
#### 4.2.1 Random Forest Regression  
Random forest regression combines a large set of regression trees and considers as an ensemble learning algorithm. Starting from a root to a leaf of the tree, a set of conditions that are hierarchically organized represents by a regression tree [54]. From the original training dataset, the random forest trains bootstrap samples using sub-datasets. Algorithm 1 works as follows: for each tree in the forest, a bootstrap sample is selected from \(T\) where \(T^{(i)}\) denotes the \(i\)th bootstrap. Then, using a decision tree to learn a modified decision tree. The modification steps works as follow: at each node of the tree, some subset of the features \(z\subseteq Z\) where \(Z\) is the set of features are selected to examine a possible split instead of using all the features. Rather than splitting the node on \(Z\), the algorithm use \(z\) to define the split on the best features. Determining on which feature to split is the most computation expensive of decision tree learning. By averaging all the trees, the predicted value of an observation is calculated. Algorithm 1 is utilized to build the base models in Step 1(b) of Algorithm 2.  
Figure 4: Structure overview of the DMLRF model implementation using Stacking ensemble.  
```
Precondition: A training set T: \(=(x_{1},y_{1}),...,(x_{n},y_{n})\), features Z, and number of tree in forest N.
1 function Random-Forest(T, Z)
2\(H\leftarrow\emptyset\)
3 for\(i\in 1,.....,B\)do \(T^{i}\leftarrow\) bootstrap sample of \(T\) \(DT_{i}\leftarrow\) RandomizeTreeLearn \((T^{i},Z)\) \(H\leftarrow\)\(H\cup\{DT_{i}\}\)
4 endfor
5 return H
6 endfunction
7 function RandomizeTreeLearn \((T,Z)\)  At each node: \(z\leftarrow\) very small subset of Z split on best feature in z
8 return the learned tree
9 endfunction
```  
**Algorithm 1** Random Forest Base-line Model
## 4 Simulation and Result
### Fake Point Scatters Simulation  
To analyze the imaging quality of false target scattering points at different positions, a scatterer array with an interval of 1.5 km is set as a deceptive jamming template. As shown in Figure 8, the scattering points are numbered \(P_{1}\)\(\sim\)\(P_{9}\) in order. The central point \(P_{5}\) of this deceptive jamming template is set to coincide with the position of the jammer. First, the HDE algorithm is used to generate the deceptive jamming signal. The real scattering point (RS) echo signal and straightforward calculation algorithm (SA) deceptive jamming algorithm are compared and analyzed. RS and SA algorithms generate jamming signals in the time domain by directly obtaining nonlinear-trajectory slant-range point-by-point superposition, which is theoretically the closest to the echo signal of the actual scene. Figure 9 shows the imaging results in the side-looking strip mode, where Figure 9c is the jamming imaging result of HDE, and Figure 9a,b are the imaging results using RS and SA algorithm, respectively. These three algorithms can achieve approximately the same focusing effects from a macroscopic view of the image. Figure 7: SAR trajectory deviation vertical component and horizontal component. To analyze the focusing characteristics of the jamming signal in detail, a comparison of range and azimuth profiles of all scattering points is shown in Figures 10 and 11. The solid orange and dot-dashed blue lines are the range-azimuth imaging contours of the RS and SA algorithms, respectively. The results are very similar and can be used as a reference for standard scattering points. The solid red line represents the result of the HDE algorithm. As analyzed in Section 3.2, due to the residual RCM effect, the imaging results of the jamming scattering points show a small main lobe broadening in both the range and azimuth direction. The range and azimuth dimensions imaging quality parameters of each scattering point are given in Table 2, including 3 dB impulse response width (IRW), peak side lobe ratio (PSLR), and integral side lobe ratio (ISLR). The last two columns of Table 2 list the statistical indices of all nine scattering points, including mean and standard deviation. It can be concluded from the table that the IRW in the range and azimuth directions measured by RS is 0.88 m and 0.87 m, respectively. In the range direction, the maximum IRWB of the false scattering points generated by the HDE algorithm is 0.67%. In the azimuth direction, the maximum IRW broadening (IRWB) of the HDE algorithm is 0.55%. Figure 8: Deception jamming fake target template. Figure 9: Imaging results of the fake target template. (**a**) Real scatters; (**b**) and (**c**) Fake target generated using SA and HDE.
## 3 Results
### Tensile Test Results  
Figure 12 shows the stress-strain curves and the histogram comparing the yield strength (YS), ultimate tensile strength (UTS), and elongation in tension of the S1, S1-HT, S2, and S2-HT specimens.  
Note that the curves of the S1/S1-HT and S2/S2-HT specimens are similar. As noted earlier, the mechanical properties of the initial pure AI, i.e., specimen S1, are low enough (YS-18.5 MPA, UTS-37.1 MPa), but simultaneously elongation, which determines the plastic property of the material, is high enough (elongation is \(\sim\)64.3%). This corresponds to  
Figure 12: Stress–strain curves (**a**) and the histogram comparing the values of yield strength (YS), ultimate tensile strength (UTS), and elongation (**b**) of the S1, S1-HT, S2, and S2-HT specimens.  
the purpose of this study. Notably, HT and/or minor reinforcements affected the tensile strength behavior of the S1, S1-HT, S2, and S2-HT specimens. The YS and UTS of the specimens showed an increasing trend. As in the case of hardness, the positive effect of B4C particles and selected HT on improving the mechanical behavior of the specimens can be argued. If we consider the main UTS characteristics, compared to the initial specimen S1, the increases are 19.13%, 71.05%, and 140.16% for specimens S1-HT, S2, and S2-HT, respectively. The data agree well with the hardness data and also indicate that, first, the selected HT was effective in increasing the strength of the specimens, and second, that it is more effective in increasing the UTS of the reinforced specimens than for the unreinforced ones. An increase of 71.05% characterizes the effect of minor reinforcement with B4C particles on the increase in UTS, and an increase of 140.16% indicates the combined effect of minor reinforcement with B4C particles and HT on UTS. The elongations of the S1-HT, S2, and S2-HT specimens, compared to the S1 specimen taken as an initial specimen, decreased by 16.67%, 51.98%, and 42.38%, respectively. The 16.67% decrease indicates the effect of HT on the degradation of pure Al ductility; however, compared to specimens S2 and S2-HT, the negative effect of this degradation is lower by ~2.5-3 times. In specimens S2 and S2-HT, the elongation values fell below 40%, with the lowest value in S2. Compared to this specimen, the S2-HT specimen shows an increase in the elongation value by ~20%, which proves that the combined effect of reinforcement and HT is preferable to reinforcement alone.
# Provisioning of Fog Computing over Named-Data Networking in Dynamic Wireless Mesh Systems
## 2 Background and Related Work
### NDN in a Multi-Hop Wireless Environment
#### 2.1.2 Related Work
In recent developments within the paradigm of Named-Data Networking (NDN), a significant focus has been on enhancing the efficiency of forwarding mechanisms. [PERSON] et al. [36] pioneered a broadcast-based self-learning approach in NDN, presenting a paradigm where nodes autonomously learn the most efficient paths for data packet routing. This work laid the groundwork for further advancements in self-learning strategies within NDN, especially relevant for environments with dynamic topologies. Expanding on these ideas, [PERSON] et al. [37] explored the application of NDN forwarding mechanisms in edge networks. Their study addressed the challenges of deploying NDN forwarders in scenarios where configuration and maintenance pose significant barriers. By proposing an \"out-of-the-box\" solution for NDN forwarders, they made strides towards simplifying the deployment of NDN in diverse network environments, including wireless mesh systems. A significant portion of research in Information-Centric Networking (ICN) advocates for an application layer implementation as an overlay on the standard TCP/IP transport [38; 39]. Using TCP/IP enables unicast communication in mesh networks and similar setups by default. However, the implementation of ICN as an overlay over the TCP/IP brings notable processing overheads. In the network's relay nodes, each packet undergoes a process of decapsulation and is then directed to the application layer. This procedure results in extra latency and can adversely affect the energy efficiency of the system. The authors in [40] consider a novel perspective on cache management in ICN. [PERSON] et al. introduce a per-face popularity approach to cache robustness, aiming to reduce cache pollution and improve hit rates. Their method represents a significant advancement in optimizing cache utilization in ICN within frameworks. [PERSON] et al. in [41] delve into the application of Named-Data Networking (NDN) in satellite communication. They propose a coded caching strategy specifically for satellite networks, addressing unique challenges like limited bandwidth and high latency. Their approach signifies a crucial step in enhancing data dissemination efficiency in space-based networks. The Dynamic Unicast (DU) approach [42; 43] has been proposed for Content-Centric Networking (CCNx), which is a framework for Information-Centric Networking. DU uses broadcast communication only until a content source has been found (single-hop discovery) and then retrieves content directly via unicast from the same source. However, even for the discovery phase, unicast communication can be more beneficial, enabling more sophisticated learning-based approaches in the forwarding plane and facilitating multi-hop resource discover.
## 3 System and Model Description
### System Model  
NVP is one of the core redundant techniques to enhance the reliability of critical software or services [22; 59]. The requirements and tasks are performed in parallel by functionally-equivalent variants, so-called SCVs. Then, their outputs will be voted to determine an ultimate result provided to users according to the predetermined voting rules, such as majority voting, Byzantine voting, threshold voting, and plurality voting. Similar to [22], in this paper, we adopt a threshold voting, and the outputting threshold is denoted as \(M\).  
When receiving a request, the RMS assigns it to \(N\) online SCVs denoted as \(SCV_{1}\), \(SCV_{2}\), \(\ldots,SCV_{N}\). After a while, each online SCV has one chance to send output vectors as responses. The RMS waits until receiving enough result vectors. The identical vector counted more than or equal to \(M\) is regarded as the correct one and is outputted to the users. Standby SCVs will replace other SCVs that sent different vectors in the next round to prevent the attack pervasion among multiple blockchain nodes. When all SCVs complete their tasks, if the number of identical results is less than \(M\) times, the RMS will return an error message prompting subsequent processing. To avoid more than one result reaching this threshold simultaneously, we defined \(M>N/2\).  
VM migration is prevalent among ACD technologies for securing cloud computing. The targets to be protected are moved across diverse platforms randomly. Various techniques are available for container migration [23; 40; 60] and VM migration [23; 24; 26] between heterogeneous physical hosts.  
In the ACD network, each node randomly migrates with probability \(\omega\) in each fixed migration period \(T\). The corresponding RMS migrates to a new physical host, and the old VMs are taken off. Then the current state of the application, along with the associated service, is migrated to the new VMs [14]. VM migration invalidates sniffed information and inserted back doors, so time is an essential constraint for attackers. In this way, the diffusion of attacks in the network is disrupted.
## 7 Simulations and Results
### Simulation Results  
Figure 15 shows the performance analysis of CRT, NI, AEC, and NLT against one to five consumer-producer pairs. The value of the x-axis is consumer-producer pairs; y-axis values are CRT, NI, AEC, and NLT. This performance graph illustrates that the performance of EPF was better than the performance of GIF and flooding. The value of CRT also increased with more consumer-producer pairs because of the access in traffic load and network congestion. The CRT of EPF was lower than GIF. The NI and AEC in EPF were less than both comparative schemes because of efficient use of the SDN controller and energy-efficient forwarding. Low energy consumption led to lower NLT in EPF against GIF and flooding. The energy efficient mechanism based on threshold that prevents the node energy for high priority packets results in better performance as compare to flooding and GIF. Increased Consumer/Producer pairs results in more traffic in the network. Any potential forwarder with energy below the threshold value will drop the LP packets. Efficient energy consumption will lead to the better NLT. As, only HP packets can travel through the nodes with energy less than threshold value, so so so the NLT will also increase. The proposed forwarding mechanism uses controller and energy efficient mechanism, that results in better CRT as compared to other schemes. Controller involvement  
Figure 14: Threshold selection against the number of total interests and average energy.  
makes sure the fast and in-time packet delivery. While the limitation on low priority packets also results in better CRT and NI.  
Figure 16 shows the evaluation of the performance according to the number of high-priority interests (HPI), number of retransmissions (RTx), AEC, and NLT against the number of nodes. The value of the x-axis is the number of nodes; y-axis values are HPI, RTx, AEC, and NLT. This performance graph illustrates that the performance of the EPF is far better than the performance of GIF and flooding. When the number of nodes increased, the value of the HPI increased in EPF because of the efficient priority forwarding mechanism. The priority interest satisfaction rate in EPF is far better than others, because of energy aware priority forwarding mechanism. Network availability is more reliable in EPF. In our proposed scheme, decreased NI and increased satisfaction ratio results in lower RTx in EPF than GIF because of the limitation on low priority packets and exact location mapping of the content through controller. The AEC in EPF is lower than both comparative schemes because of the efficient use of the SDN controller and energy-efficient forwarding. Low energy consumption led to lower NLT in EPF against GIF and flooding.  
Figure 15: Performance analysis against number of consumer/ producer pairs.
# The Evaluation Method of Particle Clustering Phenomena in RAP
## 4 Results and Discussion
### Validation of Clustering Characteristics  
To verify the applicability of other mixture types, RAP materials were collected from OGFC and SMA asphalt pavement. Figure 11 shows the extraction loss rate before and after crushing with respect to OGFC and SMA RAP material. Three particles sizes were compared, which are 4.75 mm, 9.5 mm and 13.2 mm. As shown in Figure 11, the extraction loss rate increases with the increase in particle size no matter of OGFC or SMA mixture type, and each loss rate of size beyond 4.75 mm exceeds 30% for all sizes, indicating a large clustering degree and relatively poor stability. This is consistent with the cases in dense grade mixtures of AC type asphalt mixture. Additionally, materials of each particle size decrease in extraction loss rate after a second time crushing, it is understandable because the clustering materials went through crushing process tended to cluster fewer aggregates.  
Figure 12 shows the comparison of breakage loss rate between OGFC and SMA. On one hand, for both types, the loss rate increases as the particle size increases, especially for 13.2 mm aggregates which has a distinct increase in breakage loss compared with others. On the other hand, the increase rate of 4.75 mm size aggregates in both OGFC and SMA materials is very small. The breakage loss changed little as rotate number increases, indicating a stable status of 4.75 mm old clustering materials. For the other two size materials, the breakage rate increases as rotate number increases, and the rate of OGFC materials is higher than that of SMA materials. This is probably because OGFC is open grade mixture and SMA is semi-open grade mixture, and the air voids in OGFC materials is larger and thus OGFC materials are easier to be separated.  
Figure 11: Percentage Loss (_PL_) Rate of RAP material.  
From the breakage rate results, it was found that OGFC suffered from a greater loss in RAP materials. In Figure 13, the stability index between OGFC and SMA was compared. As shown in Figure 12, with the increase of rotate number, the stability index of both materials decreases. Although the decrease trends are similar, the OGFC materials presented a lower stability index than SMA materials at all rotate number. It could be concluded that old OGFC materials are more difficult to reuse due to their low stability and high clustering degree.  
Figure 12: Crushing Loss rate of old (**a**) Open Graded Friction Course (OGFC) materials; (**b**) Stone Mastic Asphalt (SMA) materials.  
Figure 13: Stability Index of old OGFC and SMA materials.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 3 Overview of Telemedicine
### Components of Telemedicine
#### 3.1.4 Medical Professional Unit  
This unit refers to the personnel that provide the medical or caregiving services remotely, who may include physicians, fospital facilities staff who carry out data interpretation, ambulance staff, nurses, and other healthcare professionals.
# Potassium Recovery from Potassium Solution and Seawater Using Different Adsorbents
## 3 Results and Discussion
### Regeneration Study with KCl Solutions  
Figure 7 shows the changes in the amount of K sorbed on the natural zeolite and ammonium acetate-treated zeolite with respect to the number of repeated sorption tests. The average amounts of K sorbed on unit mass of both zeolites tend to decrease with the increasing number of repeated sorption tests (Figure 7). However, the reduction in the K sorption capacity was not statistically significant up to the third sorption test with the natural zeolite (Figure 7a), while the first and the fourth tests showed a statistically significant difference (\(p\)-value = 0.013). With the ammonium acetate-treated zeolite, the K sorption capacity of the first test was significantly greater than the following sorption tests (\(p\)-value \(<\) 0.05), but the K sorption capacities of the second and the third sorption tests and the third and the fourth tests were statistically similar (\(p\)-value \(>\) 0.05) (Figure 7b).  
The decreasing trend in the average K sorption capacity may be explained by the partial desorption of K during the regeneration process [3]. The K sorption capacity was greater for the ammonium acetate-treated zeolite than the natural zeolite (Figure 7); however, the natural zeolite tends to maintain its K sorption capacity up to three times of reuses, while the ammonium acetate-treated zeolite tends to reduce its K sorption capacity after the first use. This may be due to the loss of the effect of ammonium acetate treatment after repeated regeneration processes.