esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cloud Based Disaster Management System
## 7 Simulation Results and Scenario Modeling
### Evaluation Results for the Rescue Plan  
In this section, we present the performance evaluation of the proposed market-based AHP-MTSP solution. We considered three objective functions, namely: TTD, MT, and DR. The considered global cost was therefore:  
\[\textit{Globalcost}=w_{1}\sum_{k=1}^{m}C_{k}+w_{2}\max_{k\in 1\dots m}(C_{k})+w_{3} \sum_{k=1}^{m}\left|C_{k}-C_{avg}\right| \tag{12}\]  
where \(C_{k}\), \(C_{avg}\) are as previously defined (Section 6) and \(C(T_{i};T_{j})\)--Euclidian distance between targets \(T_{i}\) and \(T_{j}\).  
The following results were obtained using three targets and the number of robots varies in nr = [35101520]. Furthermore, a target location is randomly selected from a \(1000\times 1000\) space. For each arrangement of robot and target numbers, the study randomly generated 30 possible set ups then mapped the average of the results from those 30 set ups.  
In the multi-objective approach, a weight vector of \(W\) = {0.26; 0.19; 0.54} came under consideration. In addition, to examine a single objective function, either TTD, MT, or DR, the following weight vectors came under examination: \(W\) = {1; 0; 0}, \(W\) = {0; 1; 0} and \(W\) = {0; 0; 1}, respectively. Figure 11a-c displays the contrasting outcomes of the proposed multi-objective method to the mono-objective approach, in which only TTD, MT, or DR, came under consideration, respectively. With respect to the global cost considering the three objective functions, it is evident from the outcomes that the multi-objective method outperforms the mono-objective one and provides a minimal global cost (presented by the curve in black color with circle in Figure 11).  
The single objective method provides superior results in the case of the specific objective function under consideration. For example, when examining the mono-objective approach considering TTD (Figure 11a), a lower TTD cost was obtained compared to the multi-objective approach. This is anticipated, as the mono-objective approach leans towards only one criterion and excludes the others.  
Figure 11: (**a**) AHP-MTSP comparison with mono-objective approach considering TTD. (**b**) AHP-MTSP comparison with mono-objective approach considering MT. (**c**) AHP-MTSP comparison with mono-objective approach considering DR.
## 5 Results
### Effects of Pre-Processing
#### 5.2.1 Signal Level Effects of Filtering and Scaling  
In Figure 13 we illustrate the effect of frequency filtering and scaling on the performance of our regression model. We consider low pass filtering with 12 Hz and 8 Hz (given the original rate of 50 Hz from the video signals). The low pass filtering itself was done using a butterworth low pass filter of the 6 th order and standard scaling was done by removing the mean of the sensor channel in the training set and dividing by its standard deviation. Example comparisons of a signal with no filtering and no scaling on one hand and with 8 Hz filtering and scaling on the other are also shown in Figure 7 (left and right column respectively).  
Key observations are:  
1. Overall there is no dramatic effect. In all cases the simulated signal follows the structure of the real signal with reasonable accuracy while displaying the same types of problems.
2. Without scaling (left column on Figure 13) there are some pronounced amplitude outliers in the high frequency peaks amplitudes (between sample 1000 and 2000) that  
Figure 11: Examples of the signals generated by our system in the target dataset (fitness exercises). Specifically we show the acceleration parallel to the lower arm (left) and the linear acceleration along the same axis (acceleration without the gravity component that Inertial Measurement Units (IMUs) can derive with the help of gyro and magnetic field sensors).  
Figure 12: Real signals (red) versus simulated (blue) in the regression training set (generic motions) for acceleration and linear acceleration both in the limb’s axis. All cases trained without neither filtering nor standard scaling.  
are not influenced by the frequency filtering. These disappear in the scaled versions (right column); however, at the cost of significant underestimation of the amplitude in the same area and overestimation around 3000.
3. In Figure 13 frequency filtering has little effect, except for reducing the underestimation of the components below 1 g around sample 3000 in the no scaling case (which overall seems to be the best).  
In summary while both filtering and scaling do have benefits in some situations, qualitative signal examination does not indicate it to be decisive or obvious. To get a more quantitative idea, Figure 14 shows the Mean Squared Error for computed over all our samples for acceleration and gyroscope for different filter values. For acceleration the error is indeed smaller for 8 Hz (but not by much). For the gyroscope signal there is no significant difference.  
Figure 13: Examples signals generated by our method (blue) and their real counterparts (red) in the target dataset (fitness exercises) under different filter and scaling strategies.
## 4 The Proposed Distributed Image Codec
### The Decoder
#### 4.3.2 Side Information Generation  
At the decoder, after inverse quantization, the key blocks are reconstructed from their own CS measurements. For WZ blocks, reconstruction is performed with the help of side information, which is generated through a dictionary.  
Since the CS measurements of adjacent image blocks are highly correlated, those of the key blocks can be used directly as side information (SI). Starting with an empty dictionary, \(D\), it is populated with the (inverse quantized) CS measurements of the key blocks as they are received. With non-adaptive block classification, each key block is followed by one or more WZ blocks. For the WZ blocks in the same GOB, \(D\) has only one entry: \(D_{1}\). Therefore, \(D_{1}\) is used as the SI to reconstruct this WZ block. For the WZ blocks in the next GOB, the dictionary will have two entries from the two key blocks received so far. The entry in the dictionary that is chosen as SI is the one that has the minimum MSE with the current WZ block. This process continues, until all the WZ blocks are reconstructed. With adaptive block classification, the WZ blocks are reconstructed in a similar way. The only difference is that the number of WZ blocks following each key block is not fixed.  
There are several advantages with this SI generation method. Firstly, it does not require the key blocks to be decoded first. Secondly, the dictionary only consists of CS measurements of key blocks, which are already available for decoding. Therefore, it does not need to be computed or learned. Most importantly, SI obtained in this way can be directly used by the CS reconstruction algorithm without any further processing.
## 5 Conclusions  
The current research focused on examining the impact of seismicity on the evolution of the western cliffs of Lefkada Island. In light of this, medium to very high-resolution imagery were processed, while the results were associated with in situ measurements derived from the installation of a sediment trap. The main outcomes of this study are summarized in the following:  
1. The evolution of the Lefkada western coast is strongly related to seismicity.
2. Both the 2003 and 2015 earthquakes provoked inclination changes, vegetation loss, and mass movements.
3. Minor earthquakes (magnitudes < 4.1) contribute to the evolution of the cliff.
4. Since there are no wildfires or timber harvesting activities in the study area, the landform response to earthquakes is stronger than the climatic factors. Hence, heavy rain and wind are less influential in slope erosion and the cliff than the inter-seismic earthquake activity.
5. Remote sensing data and sediment trap can be combined successfully, leading to a more comprehensive understanding of the on-going processes.
6. Medium resolution GEE products and high-resolution processing results can be used as supplementary information sources.  
Figure 24: The trap sediment concentration along the four semesters of observation in accordance to the recorded precipitation and seismicity.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13169434/s1](https://www.mdpi.com/article/10.3390/app13169434/s1)]([https://www.mdpi.com/article/10.3390/app13169434/s1](https://www.mdpi.com/article/10.3390/app13169434/s1)), Figure S1: The effects of the 2003 earthquake on Komilio coast; Figure S2: The effects of the 2003 earthquake on Gialos coast.  
**Author Contributions:** Conceptualization, K.G.N., I.K.K. and A.K.; methodology, K.G.N., I.K.K. and A.K.; software, K.G.N., A.K. and D.A.; validation, K.G.N., I.K.K. and A.K.; investigation, K.G.N., I.K.K., G.P. and A.K.; data curation, K.G.N., I.K.K., G.P. and A.K.; writing--original draft preparation, K.G.N., I.K.K., D.A. and A.K.; writing--review and editing, K.G.N., I.K.K. and A.K.; project administration, K.G.N. and I.K.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are available upon request due to restrictions. The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to public safety reasons.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Model Selection and Variable Selection Using RASMR
### Link Function Selection  
For GLM (1), the link function \(g\) plays a key role in modeling the relationship between a linear combination of predictors and the expectation of the response variable. It is critical for data analysis to choose the most appropriate link function for a given dataset. An information-based approach, such as \(AIC\) or \(BIC\), requires precise estimation of the maximum likelihood. Theorem 1 and Corollary 1 provide theoretical justifications for using \(\widehat{AIC}\) or \(\widehat{BIC}\) to select the most appropriate link function.  
In our simulation studies (see Section S3 in the Supplementary Materials), SMR fails to choose the logit link function from its competitors (see Table S1), due to the low quality of its representatives. The RASMR with the delta ratio split (Algorithm 2) outperforms SMR with representatives of better quality, making the corresponding \(\widehat{AIC}\) and \(\widehat{BIC}\) more reliable (see Table S2).
# Quad Element MIMO Antenna for C, X, Ku, and Ka-Band Applications
## 2 Antenna Geometry
### Proposed MIMO Antenna  
Embedding multiple antennas into the same antenna system is seen as a promising solution, which can improve both the system's channel capacity and the communication link's quality. The MIMO systems provide a technique in which multiple independent channels send and receive data concurrently in the same radio channel. In the MIMO system, antennas at each end of the communications circuit are combined to minimize errors, optimize data speed, and improve the capacity of radio transmissions by enabling data to travel over many signal paths at the same time. It also improves the radio link capacity to attain the multipath propagation.  
The proposed quad-element MIMO antenna system is illustrated in Figure 4. The size of MIMO is 31.7 mm \(\times\) 31.7 mm. In this MIMO system, the port direction of any one of the antenna is kept perpendicular to every other port direction, and the placement of antenna elements is also symmetrical to one another. Both the properties' symmetrical arrangement of elements and the orthogonality of ports lead to providing low ECC and better port isolation. Additionally, to achieve the common ground plane of the proposed MIMO system, four satire-step microstrip lines originating from each end of the rectangular microstrip line, which is present just beneath the ports in the ground plane, are connected to one common square strip. The creation of a common ground plane in this article is responsible for improving capacitance and inductance, leading to improved efficiency and gain of the MIMO antenna system. The design variable \"spc\" indicates the distance between two adjacent patches. In this article, \"spc\" is considered as 5 mm for the minimum isolation of 15 dB in the OFB1 band.  
Figure 4: Proposed MIMO antenna system.  
The fabricated MIMO system top and bottom views are depicted in Figures 5a and 5b, respectively.
# Compound Heuristic Information Guided Policy Improvement for Robot Motor Skill Acquisition
## 5 Evaluations
### Passing through One Via-Point with SCARA  
SCARA has three revolute joints \(q_{1}\), \(q_{2}\), \(q_{3}\) and one prismatic joint \(q_{4}\). In this experiment, the orientation of the end-effector of the robot arm is ignored, thus the SCARA robot arm can be regarded as a planar two-link mechanism.  
The experiment is divided into the following four steps: **(1)** Set the Cartesian coordinate of the starting position of the end-effector to \((20,0,0)^{\text{T}}\)cm, and the corresponding joint angle is \((0,0,0)^{\text{T}}\)rad. Set the endpoint \((4.5,16,0)^{\text{T}}\)cm, and the corresponding joint angle is \((0.7068,1.1796,0)^{\text{T}}\)rad. **(2)** Drive the manipulator based on the principle of minimum jitter to obtain the demonstration. **(3)** Given a new task, use PI\({}^{2}\), PI\({}^{2}\)-CMA, PI\({}^{2}\)-KCCA, and PI\({}^{2}\)-CMA-KCCA, respectively, to make the movement pass through a via-point at \(m=1.8\) s. **(4)** The four algorithms are iterated for 80 times respectively to gain new motor skills.  
In this experiment, each of the four different algorithms is repeated 20 times. The best experimental results of PI\({}^{2}\), PI\({}^{2}\)-CMA, PI\({}^{2}\)-KCCA, and the five experimental results of PI\({}^{2}\)-CMA-KCCA are selected for comparison. The optimization effect of PI\({}^{2}\)-CMA-KCCA is analyzed by comparing the final cost of the system and the trajectories of joint space. It can be clearly seen from Figure 2 that PI\({}^{2}\)-CMA-KCCA converges faster, and its final cost is the lowest (as shown in Table 2).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Final Cost** \\ \hline PI\({}^{2}\) & \(2.2341\times 10^{8}\) \\ PI\({}^{2}\)-CMA & \(1.7569\times 10^{8}\) \\ PI\({}^{2}\)-KCCA & \(1.4089\times 10^{8}\) \\ PI\({}^{2}\)-CMA-KCCA(test1) & \(1.0249\times 10^{8}\) \\ PI\({}^{2}\)-CMA-KCCA(test2) & \(1.1095\times 10^{8}\) \\ PI\({}^{2}\)-CMA-KCCA(test3) & \(1.0677\times 10^{8}\) \\ PI\({}^{2}\)-CMA-KCCA(test4) & \(1.2106\times 10^{8}\) \\ PI\({}^{2}\)-CMA-KCCA(test5) & \(1.0155\times 10^{8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The final cost of one via-point task with SCARA.  
Figure 2: Cost of one via-point task.  
Figure 3 shows the movement trajectories of the three joints of the SCARA in the joint space when the number of iterations is 35. Because the joint \(q_{3}\) has no effect on the position of the end-effector, the value of \(q_{3}\) in the joint space remains constant.  
The blue line in Figure 3 is the trajectory of each joint of the robot arm under the PI\({}^{2}\)-CMA-KCCA. Figure 3 demonstrates that only the blue lines pass through an intermediate via-point.
# Interference Avoidance Using TDMA-Beamforming in Location Aware Small Cell Systems
## 5 Conclusions  
In this paper, the applicability of TDMA beamforming for future communication systems is studied and assessed. The paper addressed the concerns of future wireless communication followed by the features and the operations of beamforming. Subsequently, the SI-beamforming is proposed for line-of-sight small cell systems in LTE architecture. The modelling of the network is tailored to give insights on the technical concerns and the achievements of the proposed scheme. Empirical simulations are demonstrated to enforce the applicability of the SI-beamforming scheme whereby it is shown that the proposed scheme enhances the network SINR by about 18 dB, and user average throughput by about 10 Mbps in comparison to the NDIR schemes. It also increases the network fairness to approximately 95%. Future research directions will demonstrate the performance over non-line-of-sight scenarios, uplink communication, and ultra-dense environments. Resource scheduling algorithms should be added to the design, such as round robin and proportional fairness for more factual simulation. Furthermore, investigations over 5G communication channels and frequencies are recommended.  
Methodology, investigation, data curation, writing-original draft preparation, and writing-review and editing, K.S.M.; conceptualization, validation, formal analysis, supervision, and project administration, M.Y.A. and M.R.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
5G & Fifth Generation \\ BL & Base Line \\ CFO & Carrier Offsets \\ CSI & Channel State Information \\ GFDM & Generalised Frequency Division Multiplexing \\ GHz & Giga Hertz \\ IBFD & In-Band Full-Duplex \\ IoT & Internet of Things \\ IR & Interfered Region \\ LASO & Location Aware Self-Optimisation \\ LTE & Long Term Evolution \\ MIMO & Multiple Input Multiple Output \\ MMSE & Minimum Mean Square Error \\ mmWave & Millimetre Wave \\ \end{tabular}  
Figure 3: Network fairness in comparison to the baseline scheme.
## 3 Analysis of Accuracy and Performance
### Analysis of VLF Amplitude and Phase Characteristics
#### 3.2.1 Signal-Source Modeling  
The radiation source adopts a dipole source and is a time-harmonic field. The specific expression is,  
\[p(t)=\sin(2\pi ft) \tag{49}\]  
Among them, \(f=20\) kHz. The difference discrete formula at the dipole source is  
\[E_{r}^{n+1}=E_{r}^{n}+\frac{\Delta t}{\varepsilon_{0}}\Big{[}\
abla\times H \Big{]}_{r}^{n+\frac{1}{2}}-\frac{\Delta t}{\varepsilon_{0}V}\Big{[}\frac{dp}{ dt}\Big{]}^{n+\frac{1}{2}} \tag{50}\]  
In the formula, \(p(t)\) is the dipole moment of the electric dipole, and \(V\) is the volume of the cell. The cells divided under the spherical coordinates are regarded as cylinders. The cylindrical rods are placed in a grid. The bottom radius of the cylinder is \(R\cdot\Delta\theta/2\), and the height of the cylinder is \(\Delta r\). The size of the divided grid at the Earth's surface is \(\Delta r=R\cdot\Delta\theta\).
# An Efficient Quantized Message Passing Receiver Design for SCMA Systems
## 3 Proposed Quasi-Uniform Quantizer for SCMA System  
In this section, we first present the Max-log MPA detector by utilizing the Jacobi formula. The sub-factor-graph-based Max-log MPA scheme (Sub-log MPA) is then introduced by splitting the factor graph into two parts to accelerate its convergence. Next, we describe the standard uniform quantizer for the SCMA system. Finally, the quasi-uniform quantizer is proposed to enhance the range and precision of the quantized messages during iteration.
## 4 Materials and Method
### Encryption  
Cryptography is defined as a science involving advanced mathematical techniques to transmit data in a secure way that allow information to be kept secret. The data is modified using the encryption key before transmission start and the receiver converts this modified data to the original data with the appropriate decryption key. Two types of encryption methods--symmetric and asymmetric--have been successfully used for years. Asymmetric encryption is also known as public key cryptography, which is a relatively new method, uses two keys (public and private) to encrypt a plain text. The public key is used for encrypting, while the private key is used for decrypting. These two keys are created with special mathematical techniques. The public key can be known by everyone, but the secret key must be on the server-side and no one else should know it. Although the keys are different, they are mathematically related to each other. The private key is kept secret and the public key is easily shared with the target devices to which the data transfer is to be performed, because knowing this key will not contribute to solving the encrypted data. RSA (Rivest-Shamir-Adleman), DSA (Digital Signature Algorithm), Diffie-Hellman, and PGP (Pretty Good Privacy) are widely used as asymmetric key algorithms.  
Symmetric cryptography--also called'single key cryptography'--is the fastest, simplest to implement, and best-known technique that involves only one secret key to cipher and decipherthe related data. Symmetric key algorithms are primarily used for bulk encryption of blocks or data streams. There are a lot of symmetric key algorithms such as Data Encryption Standard (DES), Advanced Encryption Standard (AES), Stream Cipher Algorithm, and Blowfish. Blowfish is one of the fastest block ciphers in use and has a key length of 32 bits to 448 bits. In this study, the 128-bit key length Blowfish algorithm was preferred.
## 3 Data and Case Study  
The study used Twitter data from the Napa earthquake on 24 August 2014, at 10:20:44 UTC (3:20 A.M local time). It was the biggest earthquake in the San Francisco Bay Area after the 1989 Loma Prieta earthquake, with the maximum intensity of the Mercall VIII (extreme). The magnitude was 6.0, with 11.3 km depth [37]. The event's epicenter was south of Napa and northwest of the American Canyon (Figure 1). One individual died, about 200 individuals were wounded, and this incident resulted in more than $400 million in damage [21]. In the period 17-31 August 2014, a number of 998,791 tweets were obtained [5]. Tweets, including keywords relevant to the earthquake, were kept and 26,942 tweets have remained after the keyword filtering. For keyword filtering, words such as earthquake, quake, etc. were considered. Population information, including population density per kilometer according to the NASA website ([[https://beta.sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-adjusted-to-2015-unwpp-country-totals/data-download](https://beta.sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-adjusted-to-2015-unwpp-country-totals/data-download)]([https://beta.sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-adjusted-to-2015-unwpp-country-totals/data-download](https://beta.sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-adjusted-to-2015-unwpp-country-totals/data-download)), accessed on 18 October 2019), was also used in order to remove the population effect while preparing the damage map. In other words, the resolution of this layer was 1 km, which was calculated by dividing the population by area. Figure 1 provides an overview of the area of study on the Napa earthquake with tweet locations.