esa-sceva/satcom-chunk-collection huggingface dataset:
# First Earth-Imaging CubeSat with Harmonic Diffractive Lens
## 4 Image Reconstruction
### Deconvolution with Nonlinear Regularization  
Due to a satellite platform limitation described in Section 2, we used the Basler daA1600-60 uc (USB 2.0) (Mannheim, Germany) onboard our satellite. The high level of gain = 4 we used for this image sensor produces significant imaging noise, as shown in Figure 5(a).  
The captured image does not contain high-frequency details, so we increased the kernel size to 71 \(\times\) 71 and upscaled it to 101 \(\times\) 101, assuming \(\lambda\) = 10,000. The resulting reconstructed image is shown in Figure 5(b).  
The deconvolution with nonlinear regularization with color constraints on gradients used in this work was described in detail in [50; 51]. The point spread function (PSF) estimation for the HDL focused to infinity was calculated on an image of a random noise template acquired by the HDL against a ground truth template image, as described in [48]. PSFs with different kernel sizes have been estimated. Small kernels are best for sharpening high-frequency details, resulting in fewer artifacts. Large kernels sharpen low-frequency details in the image, while the high-frequency details are almost lost.  
As seen in Figure 6(d), the reconstructed image shows an acceptable visual quality in restoring large image details, but the text is barely readable. Increasing the kernel size, regularization parameter \(\lambda\), or both results in more color fringing artifacts in the reconstructed image. Large-sized kernels produce color noise artifacts. Thus, upscaling from smaller-sized kernels results in almost the same sharpening quality but with a reduced amount of color artifacts.  
A larger kernel size leads to significant reconstruction artifacts due to the noise. It has been previously shown that the deconvolution-based reconstruction results in a lower sharpening compared to CNN-based algorithms (described in the next section) but is much more resistant to noise.  
Figure 6: A space image of the Suez Canal taken from an altitude of 570 km (Egypt 29\({}^{\circ}\)38\({}^{\prime}\) N, 32\({}^{\circ}\)21\({}^{\prime}\) E) (**a**) and its reconstruction using deconvolution with a 101 \(\times\) 101 kernel upscaled from 71 \(\times\) 71 kernel and \(\lambda\) = 10,000 (**b**).
## 5 Discussion  
### Possible Inducing Factors of the Xiaomojiu Landslide  
According to the time-series displacement, the Xiaomojiu landslide accelerated in about October 2018. The Baige landslide blocking the Jinsha River during this period was an important factor in inducing the landslide, and external rainfall may also have been one of the trigger factors. Therefore, we collected rainfall and water level data in the landslide area to analyze the relationship between the LOS deformation rate and possible factors (Figure 13). The NDVI data in the landslide area was also collected during a 16-day interval, which helped analyze the damage to landslide surface vegetation (Figure 14). Our results showed an apparent correlation between the deformation rate and water level  
Figure 12: Local deformation and failure characteristics of the Xiaomojiu landslide. (**a**) SBAS-InSAR results obtained from PALSAR-2 SAR data; (**b**,**c**) images of local failure; (**d**) A-A’ deformation profile; (**e**) time-series deformation at P1 and P2 (background: modified from © Google Earth 2021).  
curves, whereas rainfall seems to have had little effect on the landslide (Figure 13). In late 2018 (11 October and 3 November 2018), the Baige landslide blocked the Jinsha River and formed a high water level upstream. Beforehand, the displacement of the Xiaomojiu landslide was minor, about 2 cm/a. After the water level rose, the landslide entered a stage of accelerated deformation. Although the water level dropped to its original height, the landslide's acceleration did not slow down. However, during the peak rainfall period of July-August 2018, the landslide rate did not change significantly.  
We preliminarily analyzed the impact of rising water levels on landslides. (1) The water infiltrated and softened the rock mass during the high water level, decreasing its mechanical strength significantly. At the same time, water increased the self-weight of the landslide, thus making it prone to slope instability under gravity. (2) After the dam collapse, plunged water levels and fast flow accelerated the erosion of the slope and caused the local small-scale collapse, which greatly reduced the stability of the slope (Figure 5). The NDVI data also show that the vegetation index in the landslide area was rapidly decreasing, reflecting the aggravation of landslide surface damage. Figure 14 shows the NDVI values of the landslide in April 2018 and April 2019; the chosen time eliminates the effect of different seasons on NDVI. Our results showed that the NDVI value of the landslide area decreased  
Figure 14: Comparison of NDVI values before and after landslide-accelerated damage. (**a**) NDVI value of landslide in April 2018; (**b**) NDVI value of landslide in April 2019.  
Figure 13: Relationships between landslide deformation and inducing factors.  
from 0.21 to 0.12 after one year, indicating that the destruction process of the landslide's surface vegetation was aggravated by its displacement.
# Future of IoT Networks: A Survey
## 4 Research Challenges of the IoT Network
### IoT Gateway  
The IoT gateway [68] is believed to be an important component of the IoT network. The main functionality of the conventional gateway or router is to forward packets to the destination node. The main purpose of the IoT gateway is similar to that of a conventional gateway, but there are some additional features as follows.  
First, heterogeneous network connectivity [69; 70] must be guaranteed. A short-range communication technology is definitely required to provide connectivity to small sensors or devices. These nodes must send packets to the IoT gateway first, so the IoT gateway must support all of these short-range communication protocols. Further, the IoT gateway must support wired communications, high-performance communication protocols including Wi-Fi and LTE, and existing short-range communication protocols including ZigBee, Bluetooth, and Z-wave. Second, network manageability is crucial for the IoT gateway. A conventional gateway manages the nodes in the subnet. However, the concept of network manageability is larger in an IoT gateway than in a conventional gateway. The IoT gateway is not limited to supporting conventional network management functions; rather, it should even update the affiliated devices' firmware or system. Consequently, the concept of manageability of the IoT gateway has expanded, and techniques to empower the network management is essential for the IoT gateway.  
Finally, the IoT gateway must support the protocol or platform interworking. Many novel network concepts such as platforms and protocols, are developed to make IoT environment settle into traditional networks. Nonetheless, these can cause compatibility problems with conventional devices because legacy devices, such as household appliances, are not equipped with platforms and protocols. Thus, compatibility with the conventional platform and standard competition issues must be solved by allowing the IoT gateway to support platform interworking. Table 5 shows the current research and implementations of the IoT gateway.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **IP-Based** & **Multiple Communication Card** & **Extra Storage** & **Focusing** \\ \hline [71] & O & O & X & supports various communication cards \\ [72] & O & O & X & supports various communication cards \\ [70] & O & O & X & priority-based scheduling algorithm \\ [73] & X & O & O & Machine-to-Machine communication \\ \hline \hline \end{tabular}
\end{table}
Table 5: The technological comparison of IoT gateway.
# BSM-Data Reuse Model Based on In-Vehicular Computing
## 1 Introduction
In our previous works, first, in [5] we proposed a Request-To-Receive (RTR) approach to address the blinding broadcast of BSM, to reduce bandwidth over-utilization, and decrease data loss and delay. Next, in [6] we proposed an Application-Level Flextime Aggregation (ALFA) scheme that caches BSMs on-board the vehicle and then aggregates them into new messages containing stored snapshots according to contexts for every stretch of roadway. In this paper, for broad, intelligent, and rational using of safety messages, we propose a BSM reuse model taking into account the general context of ITS, where connected vehicles are expected to boost safety, decrease traffic congestions, and reduce CO2 emissions and fuel consumption [7]. Dealing BSM as wealthy data outside its baseline design, out of safety context and beyond DSRC limits has never been considered before. Our proposed model retains collected BSMs, stores, and processes them inside the vehicle to provide a continuous data source holding saved snapshots for each roadway segment. The main innovation of our model is its participation to change the way that BSM is treated, opening up new possibilities to broadly reuse stored BSMs, making them more valued, and resulting in the creation of a reliable and useful transport data source. Moreover, given that our model relies on In-Vehicle Computing (IVC) paradigm, its strongest potential is, therefore, its capability of facilitates data processing at, or close to, their source of generation while optimizing bandwidth and reducing latency to rapidly provide data of value to drivers, pedestrians, and transportation agencies. The rest of this article is structured, as follows: Section 2 shows brief overview of the methods and techniques for connected vehicle data collection and sharing in the existing literature. Section 3 describes the availability of data in consideration of connected vehicle technology. Section 4 introduces and describes our proposed data reuse model. Section 5 presents study case, data description, and results and discussions. Finally, our conclusions, key study limitations, and future work are presented in Section 6.
## 3 Results and Discussions
### Alkaline Fractionation for High Glucan Content and XMG Yield
hydrolysate as a function of fractionation time in the temperature range 150 to 170 \({}^{\circ}\)C. The xmg obtained through alkaline fractionation at 130 \({}^{\circ}\)C increased linearly as time increased, reaching a maximum of 7.7 g/L xmg with a reaction time of 60 min (Figure 3a). However, at 150 \({}^{\circ}\)C, 10.3 g/L xmg, which was the peak for a reaction time of 30 min, was degraded from XMG and gradually decreased as the reaction continued; this decrease was steeper at 170 \({}^{\circ}\)C. It was confirmed that 9.9 g/L xmg was obtained with a reaction time of 30 min, and xmg recovery decreased to 5.3 g/L with a reaction time of 60 min (Figure 3b,c). Throughout the alkaline fractionation, a very low concentration (2 g/L or less) of glucose was released, indicating that glucan is not easily degraded by NaOH, which is the main cause of sugar loss in Fr. RH and reduced selectivity in Fr. hydrolysates. By-products other than formic acid and acetic acid, i.e., 5-HMF and furfural, which are well-known decomposition products of the acid fractionation process, were barely detectable. This is in contradiction to the LAH fractionation results for other biomass sources tested in our laboratory [15; 37]. Insignificant amounts of 5-HMF and furfural were detected in the hydrolysate after fractionation, suggesting that a small amount of glucose was released, and the XMG degradation products were present in the form of oligomers rather than monomeric sugars in the Fr. hydrolysate. Figure 3: Percentage changes in compositional contents of residual solids (below) and concentration profiles of products in liquid hydrolysates (above) after alkaline fractionation of RH with 6 wt.% NaOH: (**a**) 130 \({}^{\circ}\)C, (**b**) 150 \({}^{\circ}\)C, (**c**) 170 \({}^{\circ}\)C.
# Fuzzy-Based Dynamic Time Slot Allocation for Wireless Body Area Networks
## 5 Fuzzy-Based Dynamic Time Slot Allocation
### Fuzzification
#### 5.1.1 Energy Ratio  
The energy ratio (\(ER\)) variable indicates the ratio of available energy (\(E_{r}\)) to the initial energy (\(E_{i}\)) at each node and is given as follows:  
\[ER=\frac{E_{r}}{E_{i}}. \tag{6}\]  
Equations (7)-(9) explain the partitioning of the variable energy ratio. Fuzzy rules for available energy and slot allocation are the following:  
1. If ER is high, then slot allocated value is high.
2. If ER is medium, then slot allocated value is medium.
3. If ER is low, then slot allocated value is low.  
\[u_{l}(x)=\begin{cases}1&x\in(0,0.3)\\ 1-2(\frac{x-0.3}{0.2})^{2}&x\in(0.3,0.4)\\ 2(\frac{x-0.5}{0.2})^{2}&x\in(0.4,0.5)\\ 0&x>0.5\end{cases} \tag{7}\]  
\[u_{m}(x)=\begin{cases}e^{-(\frac{(x-c)^{2}}{2\sigma^{2}})}&x\in(0.2,0.8)\\ 0&\text{otherwise}\end{cases} \tag{8}\]  
\[u_{h}(x)=\begin{cases}1&x<0.5\\ 2(\frac{x-0.5}{0.2})^{2}&x\in(0.5,0.6)\\ 1-2(\frac{x-0.7}{0.2})^{2}&x\in(0.6,0.7)\\ 1&x>0.7\end{cases}. \tag{9}\]
## 4 Experiments
### Experiment Setting  
We have designed three scenarios, as depicted in Figure 1. Figure 1 represents a small-scale scenario with distinct obstacles and landmarks, which poses a relatively straightforward challenge for SLAM algorithms. In contrast, Figure 1 illustrates a large-scale scenario that includes a lengthy corridor, presenting a more complex task for SLAM algorithms due to the increased difficulty in particle modeling. To further substantiate the universality and versatility of our approach, an additional square scenario has been incorporated as a medium-scale testing environment. This scenario simulates more generic environmental conditions, thereby bolstering our confidence in the broad applicability of the algorithm to real-world contexts. Through robustness testing in small-scale scenarios,assessment of complex environment handling capabilities in large-scale scenarios, and validation of universality and versatility in square scenarios, our experimental design comprehensively showcases the performance and advantages of the FastSLAM-PSO algorithm under various operational conditions.  
Given that our study is anchored in a simulated environment, all sensor data and environmental feedback are generated through high-fidelity simulation software. This approach allows us to replicate the functionalities and characteristics of real LiDAR sensors without reliance on physical hardware. In our simulation experiments, the SLAM problem involving LiDAR is addressed by simulating laser line scanning strategies at various time points, ensuring that the algorithm can perform effective localization and map construction across diverse environmental conditions. Additionally, other parameter settings are as follows. The inertia weight \(w\) was meticulously calibrated to 0.5, striking a balance between the exploration of the search space and the exploitation of known promising areas. The cognitive constant \(c_{1}\) and the social constant \(c_{2}\) were both set to 1.5, which are instrumental in guiding particles toward their best-known positions and the best position, respectively. The initial velocities \(v_{i}\) of the particles were initialized to zero to avoid abrupt movements at the outset, ensuring a stable commencement of the optimization process. The velocity update equation is given by Equation (12). Furthermore, the entire algorithm is constrained in such a way that the collective particle distribution does not exceed the boundaries of the map.
## 4 Corrosion Potential Behavior with Test Parameters
### Relationship between OCP and the Parameters
#### 4.2.2 Effects of the Cover Depth and \(w/c\) Ratio on the OCP with Increasing Period
Figure 13: _Cont._  
Figure 14: Open-circuit potential (OCP) contour with corrosion parameters: (**a**) wet condition, (**b**) 3.5% concentration, and (**c**) 7.5% concentration. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Condition**} & \multirow{2}{*}{**W/C Ratio**} & \multicolumn{6}{c}{_OCP = A(Time) + B(Cover Depth) + C_} \\ \cline{2-7}  & & **Multiple Correlation** & **Determination Coefficient** & **A** & **B** & **C** \\ \hline \multirow{3}{*}{**Wet Condition**} & 0.4 & 0.634 & 0.402 & \(-\)0.140 & 0.330 & \(-\)14.392 \\ \cline{2-7}  & 0.5 & 0.627 & 0.393 & \(-\)0.159 & 0.177 & \(-\)12.385 \\ \cline{2-7}  & 0.6 & 0.629 & 0.395 & \(-\)0.183 & 0.384 & \(-\)31.730 \\ \hline \multirow{3}{*}{**3.5\%**} & 0.4 & 0.905 & 0.819 & \(-\)0.528 & 1.122 & \(-\)26.206 \\ \cline{2-7}  & 0.5 & 0.887 & 0.787 & \(-\)0.501 & 1.501 & \(-\)49.861 \\ \cline{2-7}  & 0.6 & 0.789 & 0.622 & \(-\)0.357 & 4.088 & \(-\)294.175 \\ \hline \multirow{3}{*}{**7.0\%**} & 0.4 & 0.878 & 0.771 & \(-\)0.564 & 1.244 & \(-\)18.187 \\ \cline{2-7}  & 0.5 & 0.838 & 0.702 & \(-\)0.468 & 2.552 & \(-\)156.681 \\ \cline{2-7}  & 0.6 & 0.783 & 0.614 & \(-\)0.390 & 3.992 & \(-\)328.259 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Regression results of the open-circuit potential (OCP) with exposure and cover depth. \(W/C\), water-to-cement. Figure 13: Regression analysis of the corrosion parameters for open-circuit potential (OCP): (**a**) wet condition, (**b**) 3.5% concentration, and (**c**) 7.5% concentration.
# Privacy-Preserving E-Voting System Supporting Score Voting Using Blockchain
## 6 Performance Analysis
### Performance Metrics and Simulation Designs  
Several metrics are used to evaluate the performance of the e-voting scheme, in the following, we introduce these metrics.  
* _Throughput_. Throughput is the rate at which valid transactions are being added to the Blockchain. Throughput is measured in transactions per second (tps);
* _Latency_. Latency is the time between the issuance of a transaction and the receipt of a response from the Blockchain, i.e., the time it takes for the transaction data to become useful within the network. By measuring the average latency of a set of transactions, we are able to obtain an evaluation that accurately reflects the true latency that a user of the proposed protocol can expect;
* _Error Rate_. This metric is defined as the ratio of failed transactions to committed transactions. This figure indicates how much stress operation stress the Blockchain network is experiencing and how it should remain low to signify that the network is able to manage the transaction load.  
To consider different cases where voters interact with the Blockchain, we measure the performance of the proposed protocol on the Blockchain network considering the following scenarios.  
* _Case I_. This case executes a fixed number of transactions (1000 tx) while varying the transaction rate from 100 tps to 300 tps, and incrementing by a value of 50 tps. The goal of this case is to find an optimal upper bound for the transaction rate without producing significant negative impact on the network performance;
* _Case II and Case III_. These two cases bear the responsibility of investigating the impact of varying transactions with the ideal transaction rate determined in _Case I_. We test the total transaction amounts of 1000, 5000, 10,000, and 20,000 using a rate of 100 tps found in _Case I_ with the purpose of discovering the impact increasing the transaction amounts on throughput and latency in _Case II_. _Case III_ analyzes the impact on the error rate;
* _Case IV and Case V_. These two cases investigate the impact of varying transaction numbers and varying transaction rates on overall performance. Specifically, these tests can give an insight in the impact of varying the transaction rates on performance as the number of total transactions are increased. _Case IV_ analyzes the impact on latency and throughput while _Case V_ examines the impact on error rate.
# Security Analysis of the MQTT-SN Protocol for the Internet of Things
## 2 MQTT-SN Protocol
### Format of the Main Packets
#### 2.1.2. Publish Message  
Publish packets are used when a client sends a message on a topic.  
Table 3 shows the format of a _Publish_-type packet, the different fields, and the octets they use. The function of these fields is as follows:  
* **Flags**: The most relevant ones for a _Publish_ packet:
* _DUIP_: It indicates that the message is a duplicated message, which is sent when no acknowledgment has been received after sending the original one. This behavior occurs with a Quality of Service (QoS) value greater than 0.
* _QoS_: It determines the QoS level of the Publish message.
* _Retain_: When a message is published with the _Retain_ flag set, the broker saves it as a reference one. Consequently, when a client subscribes to the topic, it automatically receives this message, so it is not necessary for the original sender to publish it again.
* _TopicIdType_: It indicates the type of identifier found in TopicId. This can be a short name, formed by two characters, or the topic Id formed by 2 bytes.
* **TopicId**: It contains the topic identifier in either of the two formats mentioned above.
* **MsgId**: It uniquely identifies a message when the QoS is greater than zero. It is encoded with 16 bits.
* **Data**: It contains the data of the message to be published.
## 3 Results and Discussion
### Adsorption Kinetics
#### 3.4.4 Temkin Isotherm  
The Temkin model [52] assumes that the free energy of adsorption is a function of the surface coverage due to adsorbent-adsorbate interactions, and is expressed as Equation (14):  
\[q_{e}=B\ln K_{T}C_{e} \tag{14}\]  
where \(B\) is involved with the heat of adsorption. \(K_{T}\) is the Temkin isotherm equilibrium binding constant (L/mg), calculated as Equation (15):  
\[B=\frac{RT}{b_{T}} \tag{15}\]  
where \(b_{T}\) is the Temkin isotherm constant [53]. The plots of \(q_{e}\) versus \(\ln C_{e}\) are given in Figure 8e. The Temkin isotherm shows good agreement with \(r^{2}>0.90\). In Table 1, \(K_{T}\) increases with increasing temperature (293-313 K) and then decreases at 323 K, indicating stronger interaction between P and sorbent with an increase in temperature (293-313 K), followed by reduced interaction. It can be observed from \(B\) that the heat of adsorption is maximum at 313 K and minimum at 323 K.  
The B values obtained from the Temkin isotherm were: 7.93 kJ/mol, 6.80 kJ/mol and 6.63 kJ/mol at the temperature of 293 K, 303 K, 313 K, respectively, which indicates that adsorption of P on Al-Ti oxide composite sorbent takes place by physisorption. The binding energy value was \(<8\) kJ/mol, thus physical adsorption is the mechanism involved. In the physical adsorption process, adsorbates adhere to the adsorbent through weak van der Waals interactions, and, thus, this process is associated with relatively low adsorption energies. The B value at 323 K was \(>8\) kJ/mol, suggesting that the chemisorption occurs at relatively high temperatures [54].