esa-sceva/satcom-chunk-collection huggingface dataset:
# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 2 Intrinsic Height Errors and Location-Induced Errors
### Location-Induced Errors  
Location-induced error refers to the situation in which real topographic features are not projected to the correct location in the radar image [30]. The imaging principle of SAR systems is reliant on measuring the time delay between two different ground scatterers and determining their relative distance in radar images. Due to the inherent oblique observation geometry of all SAR imaging systems, areas where local terrain slope exists produce topographic displacement in the range direction of the SAR images [47; 48]. Whether or not ground features are distorted or are simply not recorded depends upon the relationships between incidence angle, local terrain slope, and perpendicular baseline. This distorted effect is commonly known as geometric distortion, or geo-location error. Geometric distortion includes foreshortening, layover, and shadow [49]. Figure 2 displays how radar echoes record ground information on the image plane and how they create three distinct geometric distortions, which are separately represented in red (foreshortening), green (layover), and blue (shadow).  
Figure 2: Geometric distortion (or geo-location error) of imaging radar systems (modified from [28], Figure 2.9, and [50] Figure 2.4). Radar echoes are recorded between each concentric circle segment. The two darker concentric circle dashed lines represent the near range and far range, respectively. \(\theta\) is the incidence angle and \(\alpha\) is the local terrain slope. The three types of geometric distortion are shown in red (foreshortening), green (layover), and blue (shadow) separately.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 4 Communication Systems in Oceans
### Wireless Ad Hoc Network (WANET)
#### 4.6.2 Aeronautic Ad Hoc Network (AANET)  
As mentioned in Section 3, occasionally there are also some aerial units flying in the sky, such as balloons, drones, helicopters and airplanes. They are usually equipped of communication devices, and can be used jointly with nodes running or floating on the water surface to provide opportunistic networking for delay-tolerant applications [64]. When many aerial units appear in the same airspace, they can form an AANET [65] for communication between themselves, and provide more opportunistic networking for nodes on the water surface, too. For example, a team of drones or helicopters can construct an AANET to allow them to communicate with each other, and meanwhile act as a bridge between vessels on the water surface and terrestrial BSs.
## 4 Results and Discussion  
Table 5 contains the characteristics of the designs obtained with DMFC-AUV\QpDM for the Model 1000, the Model 3000, and the Model 4500, respectively, and Figure 8 shows the corresponding sketches of such designs. These results are achieved after running the tool 20 times for each case, obtaining the same optimum value of the merit function (MF) 18 out of 20 times as a minimum, so it can be considered that the results given by DMFC-AUV\QpDM are robust enough. When using Zeolite 13X as a CO\({}_{2}\) capture agent, only the Model 1000 presents an autonomy greater than that of the reference vehicle, 29.6 h versus 24 h, that is an increment equal to 23.3%. In the case of the Model 3000, it is observed a decrease equal to 10.5% in the value of the estimated endurance, 53.7 h versus 60 h. For the Model 4500 this decrease is reduced to 3.3%, that is, 58 h versus 60 h from the reference model. In the case of Mg-MOF-74, the results are quite different. The estimated endurance values are larger that those of the reference AUVs for the three models. As in the case of Zeolite 13X, the expected endurance increases with the size of the vehicle. This effect is more influenced by changes in the diameter of the vehicle than by changes in its length. This can be explained by the cylinder volume formula, \(\pi\cdot\frac{D^{2}}{4}\), where the diameter is an order 2 magnitude while the cylinder length is an order 1 magnitude. Model 1000, using Mg-MOF-74, offers an endurance value 141.3% larger than that of the reference AUV, 57.9 h versus 24 h, while Model 3000 and Model 4500 increase their original endurance by 73.3 % and 88.0 %, respectively, 60 h versus 104.0 h in the first case and 60 h versus 112.8 h in the last case. The different endurance increment rate observed between Model 1000 and Model 3000 and Model 4500 could be attributed to the use of Al/HP semi-fuel cells. HUGIN 3000 and HUGIN 4500 use Al/HP semi-fuel cells that have a larger energy content than Li-Polymer batteries which are used by HUGIN 1000 [6; 58]. Figure 8: Sketches of the resulting Model 1000, Model 3000, and Model 4500 equipped with CO\({}_{2}\)CSs based on Zeolite 13X and Mg-MOF-74.
## 2 LoRaWAN Standard  
LoRaWAN technology provides two-way communication, but the transmission from node (also known as end devices) to gateway (also known as concentrator or base station) or uplink message is the most frequent one, compared with that from gateway to node or downlink. This is due to the purpose of the nodes, which is to collect data and then send them to the Network Server. Lastly, data will be sent to the Application Server.  
Nodes send radio frequency uplink messages to the gateway using LoRa modulation. The gateway forwards the message to a network server over an IP connection routed via Ethernet, Wi-Fi, or 3/4/5G, adding information about the quality of the communication.  
The nodes send messages in uplink to all gateways in their transmission range in broadcast mode. The Network Server takes care of the management of duplicate uplink messages and the selection of the best gateway to use if a downlink message is to be sent to the node.  
The Network Server also manages the bit rate of the nodes through the ADR (Adaptive Data Rate) mechanism to maximize the network capacity and extend the battery life of the node. For example, the TTN Network Server uses the 20 most recent uplink messages, starting from the moment the ADR bit is set, to determine the optimal data rate [9]. These measurements include the number of frames, signal-to-noise ratio (SNR) and number of gateways that collected each uplink message.  
The Application Server instead takes care of receiving and analyzing the data sent by the nodes and determining the actions that must be performed by the nodes.  
LoRaWAN is based on CSS (Chirp Spread Spectrum) technology [10]. Chirps (also known as symbols) are the data carrier. The Spread Factor (SF), i.e., the number of bits in a chirp, allows to control the length of the chirp and thus to control the data transmission rate [6]. The lower the spreading factor, the faster the chirp and the higher the data rate. Every time the spreading factor is doubled, the chirp sweep rate and data transmission rate are halved [10]. In this paper, we do not address the trade-off between the SF, bit rate, network coverage and energy consumption. LoRaWAN uses the ISM (Industrial, Scientific and Medical) frequency bands reserved for noncommercial radio communication applications, such as industrial, scientific and medical use. In particular, depending on the geographic area and related regulations, the two most common frequencies are 868 MHz in Europe and 915 MHz in North America. Figure 1 shows a picture of the LoRaWAN network architecture.
# Modeling of Dry Clutch Wear for a Wide Range of Operating Parameters
## Appendix A Mathematical Background  
A sum of \(n\) normally distributed independent random variables, \(X_{i}\sim\mathcal{N}\left(\mu_{i},\sigma_{i}^{2}\right)\), \(i\in\{1,\ 2,\ldots,n\}\), is also a normal distribution given by \(X_{sum}=\sum\limits_{i=1}^{n}X_{i}\sim\mathcal{N}\left(\sum\limits_{i=1}^{n} \mu_{i},\sum\limits_{i=1}^{n}\sigma_{i}^{2}\right)\)[32].  
In the special case of all individual random variables having the identical distributions (\(X_{i}\sim\mathcal{N}(\mu,\sigma^{2}),\;\forall i\)), one obtains  
\[X_{sum}=\sum_{i=1}^{n}X_{i}\sim\mathcal{N}\Big{(}n\mu,n\sigma^{2}\Big{)}. \tag{10}\]  
The average value of individual random variable realization is also a normally distributed random variable:  
\[X_{avg}=\frac{1}{n}\sum_{i=1}^{n}X_{i}\sim\mathcal{N}\bigg{(}\mu,\frac{1}{n} \sigma^{2}\bigg{)}. \tag{11}\]  
Hence, the variance of \(X_{sum}\) increases proportionally with the number of individual random variables \(n\), while the variance of \(X_{avg}\) diminishes proportionally with \(n\). That is, the standard deviations of \(X_{sum}\) and \(X_{avg}\) are proportional to \(\sqrt{n}\) and \(\frac{1}{\sqrt{n}}\), respectively.  
When referring to wear rate modeling, based on Equation (1) one obtains  
\[w=\frac{1}{\rho}\frac{\sum_{i=1}^{n}\Delta m_{i}}{\sum_{i=1}^{n}E_{dis,i}}, \tag{12}\]  
where \(\Delta m_{i}\) and \(E_{dis,i}\) are the friction plate worn mass and the dissipated energy during \(i\)th closing cycle, respectively, and \(\rho\) is the friction plate density. The worn mass \(\Delta m_{i}\) is assumed to be a random variable with normal distribution. By assuming that the corresponding dissipated energies are equal over a sequence of cycles for the same operating point/parameters: \(E_{dis}=E_{dis,1}=\dots=E_{dis,n}\), Equation (12) can be rewritten as:  
\[w=\frac{1}{\rho E_{dis}}\frac{1}{n}\sum_{i=1}^{n}\Delta m_{i}, \tag{13}\]  
where \(\frac{\sum_{i=1}^{n}\Delta m_{i}}{n}\) can be related to the general random variable \(X_{avg}\) from Equation (11). Thus, the accuracy of wear rate reconstruction is increased in terms of variance by increasing the number of closing cycles. This finding was used as a basis for setting the lower limit on the number of closing cycles in the design of experiments in Section 3.
# Attack Categorisation for IoT Applications in Critical Infrastructures, a Survey
## 4 Attack Categorisation
### Use Case Specific
#### 4.8.1 Cyber-Physical System  
A Cyber-Physical System, or CPS, is defined by NIST as a device possessing interactions between both engineered components and various processes through the use of integrated physics and logic circuits [75]. Such devices can be employed in multiple areas and domains, mixing the physical world with the cyber world. For example, these devices could be employed in healthcare in elderly peoples homes to assist in every day life or could be controlled and maintained in an intensive care unit.  
Due to the inherent nature of such devices, attacks directly targeting sensors or actuators can cause significant issues, making the detection of such attacks a priority [76]. An examples of such an attack is the Stuxnet worm [77] attack against the industrial control systems in a uranium enrichment plant in Iran [78]. Due to the importance of data reliability between sensors and actuators, attacks that target the integrity and authenticity of such exchanges can cause false readings, leading to actions taken under false pretences.  
A sub category of CPS is the notion of Networked Control Systems, or NCS, which are highly used in various different types of Critical Infrastructures. The specially distributed systems rely on various shared networking infrastructures to allow communications between their different components, such as sensors and actuators. As such, in using communication networks, they inevitably inherit their strengths but also their weaknesses [79]. In this case, the authors of [80] decided to separate the attacks on NCS' by _attacks on physical components_ or _attacks on the communication network_.  
Figure 5: Use case categorisation methods.  
Similar to a previous approach, this method created a clear distinction between attacks that target a system device and network activities themselves. This allows the separation between attacks aiming to impact a single device, versus those whose objectives concern the communication network between all devices. However, this distinction is insufficient to clearly categorise certain attacks. This is explained in [80] where attacks on sensors can either be categorised as physical, tricking the device into transmitting incorrect data, or network related, by transmitting a false signal on behalf of the sensor. An example of an NCS attack is the assault against the Maroochy Water Services in Queensland, Australia in 2000 where a wireless network link to the wastewater pumps was exploited resulting in untreated waste being released [81].
# Ionospheric TEC Prediction in China Based on the Multiple-Attention LSTM Model
## 2 Data and Proposed Model
### Our Proposed Model  
#### 2.5.1 The Long Short-Term Memory (LSTM) Network  
LSTM [28] is a recursive neural network, which is composed of several LSTM units. One LSTM unit includes three gate structures, as shown in Figure 6, namely, input gate, forgetting gate and output gate. These three gate structures are connected by memory cell units to realize purposeful selection of features in the network. The calculation formula of each module of LSTM unit type are as follows:  
\[i_{t}=\sigma(W_{i}\times[h_{t-1},x_{t}]+b_{i}) \tag{6}\]  
\[f_{t}=o_{t}(W_{f}\times[h_{t-1},x_{t}]+b_{f}) \tag{7}\]  
\[\widetilde{c}_{t}=tanh(W_{c}\times[h_{t-1},x_{t}]+b_{c}) \tag{8}\]  
\[o_{t}=\sigma(W_{o}\times[h_{t-1},x_{t}]+b_{o}) \tag{9}\]  
\[c_{t}=f_{t}*c_{t-1}+i_{t}*\widetilde{c}_{t} \tag{10}\]  
\[h_{t}=o_{t}*tanh(c_{t}) \tag{11}\]  
where [ ] represents vector connection, \(\times\) represents matrix dot product, \(*\) represents Hadamard product, \(\sigma\) represents sigmoid activation function, and \(tanh\) represents hyperbolic tangent activation function.\(W_{i}\), \(W_{f}\), \(W_{c}\), \(W_{o}\) are weight matrix and \(b_{i}\), \(b_{f}\), \(b_{c}\), \(b_{o}\) indicate bias matrix. The weight matrix and bias matrix can be learned during the training process.\(x_{t}\) represents the input of the network at time \(t\), \(\widetilde{c}_{t}\) represents the updated value of memory cell state at time \(t\).\(h_{t}\) represents the state of the hidden layer at the time \(t\).\(i_{t}\) determines how much information in \(\widetilde{c}_{t}\) is used to update \(c_{t}\). \(f_{t}\) determines how much information is retained in the memory cell unit at time \(t\)\(-\)1. The state of the hidden layer at time \(t\) is determined by \(h_{t}\),\(o_{t}\) and \(c_{t}\).
# Analysis of 3D Printed Dielectric Resonator Antenna Arrays for Millimeter-Wave 5G Applications
## 2 3D Printed DRA at Microwave Frequency
The FDM 3D-printed DRA was then fabricated on a PCB with an integrated connector, as shown in Figure 5. The \(S\)-parameters were measured using a Keysight P9377B network analyzer (Keysight Technologies UK, Wokingham, UK), and the results are shown in Figure 6. A good agreement between the simulated and measured results was observed with just a slight shift from 13.2 GHz to 13.5 GHz (2.3%). To identify the possible reasons, the measured dimensional parameters (Figure 3) were applied to the simulation model. After adjusting the radius and height of the DRA to match the measured values, the resonant frequency shifted downward, which is expected due to the increased dimensions. However, the presence of an air gap may result in a lower equivalent permittivity. By reducing the permittivity of the material in the simulation model by 8%, the simulated \(S\)-parameters of the DRA align more closely with the measured results. Other potential errors unrelated to 3D printing, such as the placement of the DRA or gaps between the DRA and feeding slot, may also have affected the resonant frequency. The simulated and measured Q factors are 23.57 and 37.50, respectively. Overall, the performance of the FDM 3D-printed DRA meets the requirements for microwave frequency applications within an acceptable error range. The 13 GHz frequency band is commonly used in satellite communications, radar systems, and point-to-point communication links. However, the focus of this study is to investigate the 3D printing technique; therefore, the specifications are not strictly governed by any standard. Considerations such as increased size after printing and potential reduction of permittivity due to air gaps need to be taken into account at the Ku-band and higher microwave frequency bands. Figure 4: 3D side view and 2D cut surface profile of the FDM-printed DRA. Figure 3: 2D cut surface profile of the FDM-printed DRA.
## 2 Related Work
### Privacy-Preserving Public Auditing (PPPAS)  
For the \"Privacy-Preserving Public Auditing for Secure Cloud Storage\" method, the users can have resort to a third-party auditor (TPA) to check the integrity of the outsourced data and increase confidence between the stakeholders. They are proposing a secure cloud system that supports public auditing while preserving privacy [15]. This approach further extends the results to empower the TPA toaudit multiple users simultaneously. It utilizes a public key technique using HLA (homomorphic linear authenticator), which allows the TPA to do the auditing without having recourse to access the data.  
The proposed scheme is based on the following algorithms: KeyGen, SigGen, GenProof, and VerifyProof. [16; 17; 18] In this scheme, the authors have compared their results to Compact Proofs of Retrievability and found that their scheme's response is close to the method they have compared to, even though their scheme supports privacy preserving while the Compact Proofs of Retrievability does not support it [10].  
For their testbed, they are using a Linux-based computer with 2 GB of Memory, a 250 GB HDD, and an Intel Core 2 processor. They have compared, between batch auditing (c = 300, and c = 460) and individual auditing, the auditing time per task (ms) versus the number of auditing tasks (varying from 0 till 200) as well as the auditing time per task (ms) versus the fraction of invalid responses; from 0 till 18 (increasing by 2), using the same setup. They have compared their results to Compact Proofs of Retrievability and found that their scheme's response is close to the method they have compared to, even though their scheme supports privacy preserving while Compact Proofs of Retrievability does not. However, in this proposed approach, they have based their method on the homomorphic nonlinear authenticator algorithm, which is known for leading to additional overhead. The communication overhead is inherent in how the algorithm works, as it starts by generating a random real number. Then, based on the generated random number, binary random sequences are generated.
## 4 Implementation and Evaluation
### Implementation
#### 4.2.1. State Search and Insertion Time
The time required to search the state is depicted in Figure 10(a). The state search speed is the fastest when the offset bytes are aligned (offset is a multiple of 8). In the worst-case scenario for non-aligned offsets, the state lookup time increases by 5.3 ms for 1,000,000 times, with an average time of about 5.3 ns. The time required for the inserted state is depicted in Figure 10(b). Similarly, the update speed is fastest when the offset bytes are aligned. For non-aligned offsets, the time for 1,000,000 state updates increases by 6.3 ms in the worst case, and the average time is about 6.3 ns. Figure 11: Time required to search/insert the global state with different offsets and the 8 bits length 1,000,000 times. (**a**) Search state. (**b**) Insert state. Figure 10: Time required to search for and insert various types of states into MSSA. The offset's position has an effect on the state's search/insert time. This effect can be avoided by using a hash table to organize the data in the state area at the expense of space. However, according to this article, the search time (5.3 ns) and insertion time (6.3 ns) of the state added by the misaligned offset are very short in comparison to the 13.78 us packet forwarding delay in Experiment (1). Furthermore, the programmer can actively select the alignment offset to avoid this issue. Length of stateThe duration of this experiment examines the impact of state search/insertion time. The time required to repeatedly search/insert global variables with offset 0 bits and length 1-64 bits is tested 1,000,000 times in the experiment. The time required to find the state is depicted in Figure 12a. The length of the state can be seen to be aligned with the byte less time. The time required to update the state is depicted in Figure 12b. The longer the length, the longer the time. Similarly, the time required for byte alignment is modest. For example, the time required to update a state with a length of 64 bits is less than that required to update a state with a length of 63 bits, and even less than that required to update a state with a length of 48 bits. This is due to the fact that the bit width of the CPU we use is 64 bits. Although the length of the state affects its search/insertion time, the programmer can avoid the time overhead caused by the unaligned length for the platform characteristics.
# Durability and Safety Performance of Pavements with Added Photocatalysts
## 4 Discussion
Conversely, in paving tile T2 the photocatalyst had an adverse effect on all the parameters indicative of durability except freeze-thaw resistance, which has been attributed to the increase in the air porosity of the photocatalytic sample.
## 3 Test Scheme and Result Analysis
### Uniaxial Dynamic Impact Compression Test
#### 3.2.4 Stress-Strain Curves  
In order to more intuitively show the dynamic strength and mechanical properties of C40 plain concrete and optimally mixed S1 PP0.2 HyFRC, the SHPB processing software was used to plot the stress-strain curve according to the test results (see Figures 10 and 11). By comparing the stress-strain curves of C40 plain concrete and S1 PP0.2 HyFRC in the six temperature groups (25 \({}^{\circ}\)C, 100 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, 400 \({}^{\circ}\)C, 600 \({}^{\circ}\)C, and 800 \({}^{\circ}\)C), it can be observed that with the increase in the impact air pressure, the peak stress and peak strain of the concrete in the same temperature group gradually increase, demonstrating the reinforcing effect of the impact air pressure. At the same impact rate, the dynamic stress-strain curves of C40 plain concrete and S1 PP0.2 HyFRC in the same temperature group are relatively similar. The change rule of the stress-strain curve of the specimen after temperature treatment is obviously different from that of the normal temperature specimen, and the higher the temperature is, the more significant the difference is. With the increase in the temperature gradient, the plastic stage of the curve continues to extend, showing plastic damage characteristics, and the peak stress continues to decline. The macroscopic performance shows that the mechanical properties of concrete specimens continue to decrease, and the degree of damage continues to increase. On the other hand, the effect of high temperature enhances the plastic deformation capacity of concrete upon damage, and the HyFRC exhibits a more significant enhancement in its ductility. It can also be observed from the figure that the stress-strain curve shows the phenomenon of maximum ultimate strain rebound, i.e., the maximum ultimate strain gradually converges with the peak strain. The reason for this phenomenon is that after the dynamic load compacts, the microcracks in the concrete material during the dynamic impact test, recovery rebound occurs under the action of inertia as the dynamic impact load decreases [26].  
Figure 9: Dynamic impact peak strain of C40 plain concrete and S1 PP0.2 hybrid fiber reinforced concrete under impact pressure and temperature.  
Figure 10: Dynamic impact stress-strain curves for the C40 concrete in different temperature groups.