esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Potential Research Directions or Applications of High-Resolution Permafrost Mapping  
The high-resolution mapping of permafrost can help to identify and define permafrost regions, providing the clear boundaries of permafrost. The potential applications of high-resolution permafrost mapping in the fields of ecological conservation, water resource utilization, and geological hazard prediction are significant. In terms of ecological conservation, it can be utilized for identifying and safeguarding ecologically sensitive areas, monitoring changes in species habitats, and preserving wetland ecosystems within permafrost regions. Moreover, it aids in optimizing water resource management by accurately predicting the extent and timing of permafrost thawing, which has a substantial impact on water availability. In the realm of geological hazard prediction, high-resolution permafrost mapping assists in identifying risk areas for landslides and soil erosion, enabling proactive measures to be taken, and offers considerable insights into the potential impacts of climate change on geological hazards, thereby enhancing our capacity to respond to environmental and climatic challenges. These applications collectively provide robust tools and information support for environmental conservation and natural disaster management.  
By providing detailed underground permafrost information, high-resolution permafrost mapping helps planners to better understand the stability and sustainability of land in land use planning. This information can be used to determine appropriate locations for buildings, infrastructure, and agricultural activities to minimize interference with permafrost, thereby promoting sustainable land use and reducing environmental risks. For example, in highway engineering, understanding the clear distribution of permafrost can help to effectively avoid and handle permafrost sections, thus reducing the cost of highway geological surveys, construction processes, and maintenance after completion.
## 3 Datasets and Methods Used in SATIDS
### LSTM Network  
DNN is a type of neural network which has a hierarchical structure with many hidden layers. The network becomes denser when hidden layers increase. DNN has become trendy among cybersecurity researchers in the last few years. LSTM is a RNN network that uses the input information to predict the next sequence according to the detected pattern. The recurrence of the network delay, which represents the complex system output, is the RNN's most essential characteristic. Furthermore, RNNs maintain an activation vector that makes the RNN an intense neural network. However, due to explosion and gradient difficulties, it is also difficult to teach RNNs to rely on data from time series for a long time [20].  
The LSTM architecture makes a good choice for solving the RNN extinction gradient problem. LSTM uses a memory cell, which in sequential data may represent long-term dependencies. Figure 1 shows the internal description of the LSTM memory cell. The four internal memory gates are responsible for regulating the interlinkage between memory units. The input gate decides whether the input signal will alter the memory cell's status or not. On the other hand, the output gate determines which memory status can be changed depending on the previous input. The forgotten gate can decide to forget its status (or remember it), under the following equations, at time \(t\):  
\[i_{t}=\sigma(W_{xt}x_{t}+W_{hi}h_{t-1}+W_{ci}c_{t-1}+b_{i}) \tag{1}\]  
\[f_{t}=\sigma\Big{(}W_{xf}x_{t}+W_{hf}h_{t-1}+W_{cf}c_{t-1}+b_{f}\Big{)} \tag{2}\]  
\[o_{t}=\sigma(W_{xo}x_{t}+W_{hb}h_{t-1}+W_{co}c_{t}+b_{o}) \tag{3}\]  
\[c_{t}=f_{t}c_{t-1}+i_{t}\tan\text{h}(W_{xc}x_{t}+W_{hc}h_{t-1}+b_{c}) \tag{4}\]  
\[h_{t}=o_{t}*\tan\text{h}(c_{t}) \tag{5}\]  
where \(i_{t},f_{t},o_{t}\), and \(c_{t}\) are the input, forget, output and cell gate activations, respectively, at any time \(t\).  
\(\sigma\): Logistic sigmoid function.  
\(W_{x}\): Weight matrices.  
\(b_{x}\): Variable biases.  
\(h_{t-1}\): Hidden state at time step \(t-1\).  
\(c_{t-1}\): Cell state step at time \(t-1\).  
Figure 1: LSTM internal architecture structure surroundings “Reprinted/adapted with permission from Ref. [20]”.
## 3 Results and Discussion
### XPS Analysis  
This analysis helps to determine elemental composition, identifying the elements present in a sample, its chemical state (oxidation state and chemical bonding), surface chemistry, and binding energies of the electrons of the atoms composing the sample [68]. Overall, XPS provides valuable information on the surface and bulk composition, chemical state, and bonding of inorganic materials.  
Figure 5 shows the XPS spectra for the IJ (Figure 5A) and decomposed IJ (Figure 5B).  
In Figure 5A, the presence of O, S, Fe, Na, and Pb is observed, which form part of the chemical composition of jarosite. Similarly, the presence of Zn is noted, which is part of the franklinite found in DRX, and the presence of As is again noted (0.67% in this case), which, as mentioned earlier, comes from minerals associated with sphalerite, such as arsenopyrite, and which were leached and incorporated into the jarosite structure during its precipitation in the leaching circuits.  
Figure 5: XPS spectra obtained for (**A**) IJ and (**B**) decomposed IJ.  
On the other hand, spectrum B no longer shows the presence of As and Pb, and the counts per second for S and Na have decreased, which did not happen for Fe and Zn. This confirms the decomposition of the industrial jarosite.  
For the case of the XPS results obtained for the IJ with As, IJ with Cr, and IJ with As+Cr, Figure 6 shows these spectra, demonstrating the incorporation of As and Cr into the jarosite structure and also showing the presence of the elements found in spectra 5A.  
Figure 6: XPS spectra obtained for (**A**) IJ with As, (**B**) IJ with Cr, and (**C**) IJ with As+Cr.
# Biometrics for Internet-of-Things Security: A Review
## 7 Potential Solutions and Opportunities
### Biometrics with Other Technologies  
Biometrics for IoT applications can overcome drawbacks of traditional password-based authentication and thus enhance the security of IoT networks; however, biometrics alone cannot be the cure to all the security issues of the IoT. The use of biometrics together with other technologies (e.g., machine learning, blockchain, and edge computing) can bring more benefits to IoT security [115]. For example, machine learning is considered a powerful tool. Research works, such as [34; 41; 69], apply machine learning to improve system performance or reduce biometric uncertainty. Blockchain, referred to as a decentralized ledger maintained by a number of independent users, is an emerging technology to protect transactions against forgery by adding a digital signature [116]. In a system that uses both blockchain and the IoT to generate blockchain transactions, the verification of a reliable creator is a challenge, but the use of biometrics can ensure that the creator of a transaction is the correct owner of the private key [117]. As an extension of cloud computing, edge computing has experienced fast development. The use of edge computing in the IoT may solve issues of cloud computing (e.g., processing of a huge amount of data and/or a long distance between servers and users) by placing small edge servers between the users and the cloud. In this way, the communication cost is low, and data are safer without being transmitted to the remote cloud server. It has been proven that using biometric authentication in edge computing-based IoT applications can improve system security [118]. It will further strengthen the security of IoT devices and the sensitive information therein if the privacy-preserving technique can be applied to protect biometric data themselves.
# Preparation and Lubricating Properties of Polystyrene Composite Microspheres
## 4 Conclusions  
Three kinds of polystyrene (PS) composite microspheres were synthesized by suspension polymerization, which were EGR/PS, OMMT/EGR/PS, and PTFE/PS. The surface morphology, chemical composition, particle size and tribological properties of these composite microspheres were carefully studied. The conclusions are as follows.  
1. EGR/PS, OMMT/EGR/PS, and PTFE/PS were successfully prepared. The particle size of various composite microspheres was counted and compared, and the particles of PTFE/PS were the smallest, averaging 49 \(\upmu\)m. The experimental operation of composite microspheres prepared by suspension polymerization was relatively simple. However, the particle size distribution of the prepared microspheres was very broad, and it was not easy to control the particle size of the microspheres in a very accurate range.
2. Compared with pure water, PS, OMMT/EGR/PS, EGR/PS and PTFE/PS all improved the lubrication and wear resistance. PTFE/PS has the best lubricating and anti-wear properties, which can effectively reduce the friction coefficient and reduce the wear volume. Under a load of 10 N, the friction coefficient of PTFE/PS was about 62% lower than that of pure water, and the wear volume was 86% lower than that of pure water.
3. Increasing the PTFE content can improve the lubricating properties of PTFE/PS. PTFE/PS with 4.0 wt% PTFE addition can reduce the friction coefficient from 0.825 to 0.213, which is nearly 74% lower than that of pure water, and the wear volume of PTFE/PS with 4.0 wt% PTFE is 92.4% lower than that of pure water.
4. The spherical structure of PTFE/PS as a ball bearing transforms the sliding friction into rolling friction, and a low shear transfer film composed of PTFE is formed on the wear surface. The combination of rolling friction and low shear transfer film results in excellent lubrication performance of PTFE/PS.  
Conceptualization, W.Z. and B.G.; methodology, H.S. and W.H; validation, W.Z., B.G. and Y.S.; formal analysis, W.Z. and B.G.; investigation, W.Z. and H.S.; resources, Y.S.; data treatment, W.Z. and W.H.; writing--original draft preparation, W.Z. and B.G.; writing--review and editing, W.Z., B.G. and Y.S.; supervision, H.S. and W.H.; project administration, H.S. and W.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflict of interest.  
Figure 10: Schematic diagram of (**a**) friction performance test and (**b,c**) lubrication mechanism for the PTFE/PS water-based lubricant.
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 5 Simulation Results Analysis
### Performance of Uplink Communications with No Hidden Nodes
#### 5.3.3 Energy Consumption  
Energy consumption results have been calculated from average current consumption by transceivers in devices during the entire simulation time. Due to present unavailability of VLC transceiver consumption specification data, simulations have been carried out with consumption values corresponding to the IRMT6452 [40] optical infrared transceiver. This transceiver conforms to the specifications defined by the Infrared Standard Associated [41] and combines an LED, a photodiode and associated circuitry. Absolute values for energy consumption obtained this way should be reasonably representative of real consumption experienced in a VLC network. Nevertheless, according to the objectives of this paper, energy consumption comparisons can be made depending on whether or not there are hidden nodes in the network.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**N** **(Number of Devices)**} & \multicolumn{2}{c}{**Successfully**} & \multicolumn{2}{c}{**Frame**} \\  & **Network** & **Transmitted** & **Channel Access** & **Transmission** & **Collisions** \\  & **Load** & **Frames** & **Failures** & **Failures** & \\ \hline \multirow{3}{*}{4} & **10\%** & 93.2\% & 6.8\% & 0\% & 0.5\% \\  & **50\%** & 71.8\% & 28.2\% & 0\% & 2.9\% \\  & **200\%** & 38.2\% & 61.8\% & 0\% & 11.0\% \\ \hline \multirow{3}{*}{16} & **10\%** & 91.1\% & 8.9\% & 0\% & 0.8\% \\  & **50\%** & 67.4\% & 32.6\% & 0\% & 3.8\% \\ \cline{1-1}  & **200\%** & 32.8\% & 67.2\% & 0\% & 14.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Carrier sense multiple access with collision avoidance (CSMA/CA) statistics with no hidden nodes.  
Figure 9: Message loss rate as a function of network load, without hidden nodes.  
Figure 10 shows average values of accumulated current consumption by transceivers in each device during simulation time. Energy consumption in each device is higher in scenarios with fewer devices because network load is evenly distributed among all network devices. Thus, with a given load value, the number of transmissions performed by each device is higher in scenarios with fewer devices. Figure 10 also shows that energy consumption increases with network load, but not linearly with it. As indicated before, in the absence of hidden nodes, the time spent by devices doing backoffs and calling the CCA function increases with network load. In these states, the transceiver consumption is lower than in the transmission state.
# mmSight: A Robust Millimeter-Wave Near-Field SAR Imaging Algorithm
## 5 Discussion and Future Work
### Future Work  
Although mmSight is superior to general imaging algorithms, there are still some problems. The present algorithm, like other conventional imaging algorithms, requires a large aperture array with uniform sampling interval to capture data. If a real aperture array is used, the hardware cost will be high and it is too complex to maintain, while using a synthetic virtual aperture, the capturing time is long and cannot meet the real-time requirements of many applications. In addition, the equipment required for both approaches would occupy a certain amount of space and would not be portable. A lot of work has been undertaken to address the miniaturization of imaging equipment with real-time and irregular sampling intervals. However, some work is done in exchange for reduced resolution, and some work needs to rely on outside devices like tags and positioning devices. Therefore, how to take into account the miniaturization of equipment  
Figure 17: Point Spread Function: (**a**) Back Projection; (**b**) Matched Filter; (**c**) Analytic Fourier Transform; (**d**) mmSight.  
and real-time, high resolution, no outside device-dependent, is still a future research direction of RF imaging.  
mmSight is still a modification of the conventional algorithm, and the imaging results are essentially a distribution map of the target reflectivity, so it is still material-dependent. For targets with high reflectivity, such as metal, there will be good imaging results, while for targets with low reflectivity, such as carton, the imaging results are not ideal. Therefore, how to find a new feature to replace reflectivity and achieve material-independent imaging is also a future research direction for RF imaging. We can see the future direction from Table 5.
## 5 Automatic Signal Recognition
### Validation
#### 5.2.3 Analysis of Variable \(T\)  
Figure 12 depicts the true positive rate of each technology with respect to the total observation time \(T\) for \(N=160\). \(T\) is varied exponentially, according to \(T=(\frac{1}{2})^{n}\) second, where \(n=0,1,...,9\). Generally speaking, the recognition accuracy decreases when the observation time is shortened. When a comparison is made among technologies, the recognition rate of Wi-Fi is most sensitive to the variation of \(T\). This is logical, as the Wi-Fi signal consists of random bursts, and during a shorter interval, the chances to capture Wi-Fi packets decrease, hence the higher probability of being categorized as noise. The shrinking of observation time does not seem to hinder LTE's recognition until it hits 2 ms, which is equal to the duration of the two LTE subframes and represented by merely 12 RSSI data points--for \(N=160\), \(round\) (\(2\,\text{ms}/160\,\mu\)s) = 12. In this case, the RSSI sequence does not have sufficient diversity to make a distinction between LTE and DVB-T any more. DVB-T's recognition is rather independent of the observation time, again thanks to its stable feature in the time domain. However, there are some local fluctuations, due to the fact that it is more difficult to make a reliable prediction based on a smaller amount of RSSI data points in a short duration.  
Figure 11: The true positive rate versus the average interval of RSSI measurements for \(T=1\) s. Because the IQ samples are acquired at 1 MHz, the interval \(\frac{N}{\text{TMHz}}\) has the same numeric value as the window size \(N\) in \(\mu\)s.
# Salient Region Detection Using Diffusion Process with Nonlocal Connections
## 2 Proposed Approach
### Single-Layer Updating and Multi-Layer Integration
#### 2.5.1 Single-Layer Updating  
[PERSON] et al. [18] and [PERSON] et al. [14] defined saliency detection as a binary segmentation problem to separate the salient regions from the background. Inspired by this, we design the single-layer updating scheme to obtain a more homogeneous salient region. First, we binarize the saliency map at each scale with an adaptive threshold \(T_{s}\) generated by OTSU [40], and then, the pixels are divided into two sets: the initial foreground and the initial background regions via the adaptive threshold. Naturally, the pixels in the initial foreground regions need to be highlighted, while the pixels in the background should be suppressed. Based on the above analysis, we design the single-layer updating rule as:  
\[S_{s}^{i+1}=S_{s}^{i}+sign(S_{s}^{i}-T_{s}^{i}\cdot 1)\cdot\varepsilon\ i=0, \ 1,\ 2\cdots M \tag{28}\]  
where \(sign\) is the sign function to decide the type of the pixels, \(i\) is the number of iterations, \(S_{s}^{i}\) denotes the saliency values of all pixels at the scale \(s\) after \(i\)-th updating process, \(S_{s}^{0}\) represents the initial saliency map \(S_{init}\) at scale \(s\) generated by the Equation (27). \(T_{s}^{i}\) denotes the adaptive threshold of the saliency map at the scale \(s\) after \(i\)-th updating process, \(1\) is the ones matrix with the image size of \(W\times H\), and \(\varepsilon\) is the updating weight. In this section, we empirically set \(\varepsilon\ =\ 0.08\). After \(M\) times iterations, the finial saliency map \(S_{S}^{M}\) at scale \(s\) can be obtained. As shown in Figure 8d, compared to the initial saliency maps at each scale, the foreground regions are more highlighted, and the background regions with the low saliency values can be well suppressed.
# Zeroize: A New Method to Improve the Utilization of 5G Networks When Running VoIP over IPv6
## 3 Zeroize Method Design
### Core Idea  
The Zeroize method uses the superfluous fields of the VoIP packet header to increase the call capacity. The superfluous fields are used to keep the digital voice data of the packet. The studies in [14; 15] considered several fields of the VoIP packet as superfluous for VoIP applications on the basis of certain rules. Several fields throughout the RTP, UDP, and IPv4 meet the rules and are deemed superfluous, including the source IPv4 address. However, the Zeroize method is intended for the VoIP over IPv6 protocol. The size of the source IPv6 address is 128-bit (16 bytes), which is greater than any common codec frame, as shown in Table 1. Therefore, only the source IPv6 address is deemed a superfluous field and used to keep the digital voice data of the packet.
# A Review on the Lifecycle Strategies Enhancing Remanufacturing
## 3 Strategies Enhancing Remanufacturing
### Product Service System (PSS)  
The product service system is a system by which OEMs market the services provided by their products, instead of selling the product itself [107; 108; 109]. This system comes with many advantages, including the fact that the usage mode of the product can be set and closely monitored. The disadvantage is that users might be careless with the way they handle the products because it does not belong to them. This method is very effective because the inspection of the products at any time will be very easy since OEMs rather than end users are responsible for the recovery options for EoL products [54]. This opens the door for remanufacturing being the main route at the EoL stage. With this system, predictive remanufacturing can also be used if the products are equipped with data carrying devices (DCDs) which signal when the product is suitable for repair or remanufacturing [110]. One example is the bike-sharing system used in China and some other countries all over the world. The users use the bikes but they do not own them; they instead pay a certain amount every time they use them and after use they park them properly. This makes collection a lot easier when they arrive at their end-of-life stage, and it also provides for the possibility of the product being upgraded [111]. There are many more examples currently in use, like public power banks, electric sharing vehicles and others.
# True Random Number Generator Based on Fibonacci-Galois Ring Oscillators for FPGA
## 3 True Random Number Generators Design and Evaluation on FPGA Technology  
### Methods for FPGA Technology-Based TRNGs Analysis  
As addressed in previous sections, a TRNG exploits various noise sources to produce a random output. This characteristic implies that the testing functionality of these devices is not straightforward. Register Transfer Level (RTL) functional simulations are not suitable, since their incapability of resolving a combinational loop which is present in almost every state of the art TRNG. Post-layout simulations can do this, but metastability events, which are a fundamental part of the entropy harvesting process, lead to the propagation in the sampling chain of an undefined logic state (X). Analogue simulation tools provide functionalities for time-domain noise analysis and it is possible to find in literature articles where these are used for Application-Specific Integrated Circuit (ASIC) simulation of a TRNG [19]. Nevertheless, our main target for the implementation is the FPGA and no models are provided by vendors to test the dynamical behaviour of a noise dependant combinational loop. For these reasons, testing has been necessarily carried out in hardware. Our testing strategy focused on the direct extraction of a set of random values on the FPGA, which are then transmitted to an external computer for processing and evaluation.  
In the test campaign reported in this section, we carried out a test on the already presented RNG architectures (TERO, Meta-RO, FIRO, GARO and FiGaRO). The objective of this test campaign is to find the best architecture for FPGA, where the figure of merit is the randomness of the outputs. Working on the FPGAs, we tried to test different placement techniques for the hardware, to find an architecture whose entropy was not affected by the placement of the hardware within the FPGA. The evaluation of distribution uniformity is carried out quantitatively at first examination. The selected RNG will then be tested with the NIST entropy assessment test suite to evaluate precisely its quality. Performances of some devices may be influenced by different placing and routing in the FPGA device. Where necessary, constraints have been created in the FPGA development tool to address these issues: employing Design Partition and LogicLock features the placing and routing of a single TRNG element mapped into a restricted number of Logic Array Blocks (LABs) have been saved, to be recallable multiple times and positioned in different cells controlled by the user. Such constraints can be easily ported in any FPGA development tool. By doing so, it has been possible to test different floor-plan configurations and extract more accurate results.