esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
With the rapid growth of Internet of Things (IoT) devices around the world, thousands of mobile users share many data with each other daily. IoT communication has been developed in the past few years to ensure direct connection among mobile users. However, wireless vulnerabilities exist that cause security concerns for IoT device-to-device (D2D) communication. This has become a serious debate, especially in smart environments where highly sensitive information is exchanged. In this paper, we study the security requirements in IoT D2D communication. In addition, we propose a novel authentication approach called Secure Key Exchange with QR Code (SeKeQ) to verify user identity by ensuring an automatic key comparison and providing a shared secret key using Diffie-Hellman key agreement with an SHA-256 hash. To evaluate the performance of SeKeQ, we ran a testbed using devices with a WiFi-Direct communication interface. The obtained results depict that our proposal can offer the required security functions including key exchange, data confidentiality, and integrity. In addition, our proposal can reach the same security performances as MANA (Manual Authentication) and UMAC (Universal-Hashing Message Authentication Code) but with 10 times fewer key computations and reduced memory occupancy.  
Internet of Things; D2D communication; WiFi-Direct; smart environments; man-in-the-middle attack; key exchange +
Footnote †: journal:  
_Future Internet_**2019**, _11_, 251; doi:10.3390/611120251
## 4 Discussion
### Water Table Variations  
SP is characterized by two well-defined aquifers: the shallower one (from soil surface to \(-20/-30\) m above sea level) separated by clayey and silty-clayey layer and the deeper one (from \(-50/-60\) m a.s.l.) [85]. Both the aquifers are characterized by intense water exploitation. The piezometric level variations of the shallow one shows 5 meters of drop between 1930s and 2002 [86; 87].  
We investigated to find possible correlation between groundwater exploitation and ground deformation comparing the spatial distribution of phreatic water table variations in the time interval 1930s-2002 [86] and the Vertical velocity distribution from Envisat and COSMO-SkyMed data in the following 2003-2013 time interval (Figure 12). In the most recent period, we have observed that subsidence is present in areas showing rise, drop and stability of the water table.  
Considering the effect of water table variation between 2002 (data from [86]) and 2013 (this work), we analyzed the distribution of the Vertical velocity (from Envisat and COSMO-SkyMed data, Figure 12). The subsidence location does not show a strict correlation with the water table drop.  
We selected 6 wells (Figure 1), with piezometric records in 2002 and 2013, and we analyzed the Envisat time series for both ascending and descending orbit (Figure 13) considering a circular buffer of 500 m radius. For the two wells No. 2 and 3, showing a groundwater drawdown, we observe a displacement trend similar to the No. 1, 4, 5 and 6 ones characterized by water table rise. Moreover,  
Figure 11: Envisat ascending and descending time series (error = \(\pm 1\) mm) comparison between urban and agricultural areas for three selected sectors (see Figure 4 for their location).  
Figure 12: Envisat and COSMO-SkyMed Vertical component superimposed respectively on the maps of the water table variations 1930s–2002 (redrawn from [86]) and 2002–2013.  
the time series show smaller displacements rates in correspondence of the well No. 2. There is no evidence of correlation between water table variation and displacement time series, probably because the progressive groundwater level stabilization from 2002 to 2013. In contrast, the groundwater level drop between 1930s and 2002 [86; 87] could have been caused by a more recent consolidation process of soft materials as observed by [88] in the Alto Guadalentin Basin.  
For the confined aquifer, the lack of historical data did not allow us to analyze the effects of its variations on subsidence.  
Therefore, we conclude that water table variations are only an incremental factor of subsidence in some areas as the Sybaris archaeological area (see Figure 2 to localize Sybaris site), where a well-points system (80 1/s) works every time to maintain the excavations above the water table. Moreover, in these areas, a water depletion in compressible soils could trigger an amplified and long-term subsidence as shown in other Quaternary basins [88; 89].
# Measurement and Model-Based Control of Solidification in Continuous Casting of Steel Billets
## 3 Results
### Vibrometer Measurements of Crater End Position at ESF
#### 3.2.1 Vibrometer Measurement Campaign with Varying Strand Positions  
Figure 1 shows the laser vibrometer mounted on a rail to measure the strand vibrations along different positions of the ESF billet caster:  
* 20 measurement points on strand from torch cutter over a length of 5 m;
* 60 s measurement time for each measurement position;  
Figure 5: Simulated temperature profile compared to measurements of strand surface temperatures with IR camera at ESF billet caster.  
* 900 s measurement time at nearest and most distant position to the torch cutter;
* 2 measurement sequences with two different casting speeds.  
The pre-processing was performed by FFT of the raw vibrometer signal over measurement intervals of 1 s. Due to the mainly stationary casting conditions during the measurements, intervals of 1 to 60 s with frequency resolutions of 1 Hz to 1/60 Hz, respectively, are reasonable. This pre-processing step revealed significant differences in the transformed signal. However, these seemed to arise mainly from different characteristics of the positions themselves, e.g., due to the water jet of the descaling system evaporating on the strand surface (see Figure 6).  
In order to verify this observation, the data were analysed with supervised machine learning methods, i.e., neural networks. For this purpose, the data were scaled based on the mean values and standard deviations of each frequency. The neural network's hyperparameters were optimised using a tree-structured Parzen estimator, which is a single-objective Bayesian optimisation algorithm. The resulting structure of the chosen neural network is given by  
* Two hidden layers with 64 and 128 neurons;
* Rectifier activation function;
* Max. number of iterations = 500.  
Applying the neural network to predict the measurement position using the transformed signal of a 1 s measurement interval yielded a 56% accuracy on the training and a 35% accuracy on the test data set. Considering that the confusion mainly happens between neighbouring positions (see Figure 7), it can be concluded that every measurement position along the steel strand has a characteristic vibration footprint that could overlay the influence of the strand solidification grade and make the detection of the crater end position difficult. This conclusion is also supported by the observation of the most significant transition of the vibration spectrum for the measurement sequences with both casting speeds between positions 13 and 14, which are before and behind the descaler, respectively. Therefore, a second vibrometer measurement campaign was performed with a fixed measurement position and varied casting conditions to avoid those positional effects.  
Figure 6: Fast Fourier Transformed vibrometer data over the 60 s measurement interval at position 13 and 14 before and behind the descaler.
## 2 Related Work
How the nodes will communicate in case of failure of some nodes is not taken into account in this protocol. The objective of all the above mentioned protocols is the development of cluster-based approaches to conserve energy, but they do not consider the essential feature, scalability for the IoT [57]. Flat routing protocols represent some solutions for IoT applications. REL [58], LABILE [59], AODV [60] and EEURP [61] proposed the new schemes for the IoT, but they do not consider the load balance, energy efficiency and scalability QoS metrics. A survey of these protocols prompted us to develop a new framework for the IoT with a routing algorithm which could enhance not only the network lifetime, but also reduce the delay. Network lifetime can be enhanced by minimizing the communication distance of the nodes and by load balancing on the CHs. The connectivity of the network is also important to deliver the data in a timely way, but it is assumed in these protocols that the connectivity of the nodes can't be maintained because of failure of the nodes and this may render the network obsolete. Most of the efficient routing protocols such as LEACH, SEP, T-DEEC, MODELACH, EESAA, genetic HCR and ERP, _etc._, work on optimizing the energy efficiency, but they do not consider the other factors like delay, load balancing, scalability, _etc._ Their efficiency decreases as the network size increases so they are not suitable for environmental monitoring, military or real time IoT applications. Moreover, the energy scarcity of low price and low-powered sensor node has been a central issue for WSNs and for the future IoT. To extend its lifespan, the sensor nodes operate in a duty-cycled mode. The recent development of energy harvesting technologies mitigates the energy scarcity issue, but the sensor node still has to operate in duty-cycled mode due to the limited energy collection from the environment (e.g., light, RF, and vibration), and has to dynamically adjust its duty cycles to accommodate the availability of environmental energy. Such dynamic duty cycles pose challenges for networks with IEEE 802.15.4 MAC in terms of synchronization, packet loss, waste of channel resources and energy, and so on, therefore, standards for duty-cycling-aware middleware between MAC and power management are highly desired [62]. Based on the above discussed challenges, requirements and advances, it is identified that existing schemes lack in providing the energy efficiency, reliability, scalability and timely delivery required for WSN-based IoT applications. With this objective in mind, we propose ME-CBCCP, a chain-based routing algorithm for homogeneous WSN/IoT scenarios.
# Area-Efficient Universal Code Generator for GPS L1C and BDS B1C Signals
## 3 Previous Code Generator Structures
The generated Legendre sequences are stored in the RAM, and the data channel code \(C_{data}(t)\) and the pilot channel code \(C_{pilot}(t)\) are generated through the calculation process shown in Equations (4)-(6). Finally, Figure 4 shows the architecture of Weil-generation universal code generators (WG UCG) [17]. Legendre-generation universal code generators [16] use a small ROM but require RAM to store the Legendre sequences generated on the fly. In general, RAM, which simultaneously supports read and write functions, requires more hardware resources and has longer latency compared to ROM, which provides only a simple read function. In addition, whereas ROM does not require the initialization time to generate the initial value, the initialization time is essential in the case of RAM because it can perform reading only after writing the initial value first. In the case of the Weil-generation universal code generators [16] shown in Figure 4, the RAM in the Legendre-generation universal code generator shown in Figure 3[15] was completely removed. Instead of removing the RAM memory, the Legendre ROM memory sequences by signal (GPS L1C, BDS B1C) and by channel (Data, Pilot) are added, and the Weil sequences are generated on the fly according to Equation (4). Through the foregoing, the complexity of the RAM memory is reduced, while the time required for initialization is completely eliminated.
## 2 Design and Working Principle  
Figure 1 illustrates the schematic diagram of the proposed tunable multi-frequency HEH. It consists of a cantilevered piezoelectric oscillator with a cylindrical magnet as a proof mass and a magnetic oscillator attached on the frame. Two oscillators are coupled through the magnetic interaction, which behaves as the repulsive force. An induction coil is attached around the magnetic oscillator. \(R_{1}\) and \(R_{2}\) are load resistances connected to piezoelectric patches and induction coil, respectively. When the HEH is subjected to the external excitation, the mechanical strain is converted into voltage in the piezoelectric layers. Meanwhile, the relative movement between the magnet and coil induces a current in the wire, based on [PERSON]'s law. Note that the resonant frequencies of two oscillators are higher than their own natural frequencies due to the repulsive magnetic force. The natural frequency of the magnetic oscillator is designed to be lower than that of the piezoelectric oscillator, so that it can be used to scavenge the low-frequency vibration energy. Meanwhile, the piezoelectric oscillator can harvest the discrete high-frequency vibration energy.  
The advantages of this design are as follows: (1) the output power is enhanced, because the kinetic energy can be simultaneously harnessed through piezoelectric effect and electromagnetic induction; (2) it is capable of simultaneously responding to excitation from multiple frequencies; (3) its resonant frequencies can be tuned by changing the separation distance between two magnets; (4) it has a wider bandwidth in the low-frequency range.
# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 4 Review on Energy Consumption and Efficiency Model
### Multi-Hop Network
Another key finding from the review is that there are significant changes or developments on sensor node technology, but there is a lack of a new energy consumption model to adopt those changes. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Ref (Yr)** & **Model / Network** & **Application Scenario(s)** \\ \hline
[68] (01) & Web based JouleTrack for energy consumption/Specific to sensor node SHAS & Energy consumption based on StrongARM SAA-1100 and Hitachi SH-4 microprocessors \\ \hline
[36] (03) & Energy efficiency concept (uncoded and coded with BCH and convolutional code)/A single hop WSN model & Rayleigh fading channel, binary orthogonal non-coherent frequency shift keying modulated data on frequency non-selective. \\ \hline
[63] (04) & Energy efficiency of DC-balanced error correcting codes (BCH, Golay and SCDED)/A single hop WSN model & Extension work of [36] \\ \hline
[63; 65] (04) & The energy consumption per information bit neglecting startup, encoding and decoding energy cost/Multi-hop linear sensor model & Rayleigh fading channel with Frequency Shift Keying (FSK) modulation with non-coherent detection \\ \hline
[70] (05) & DEB / Not specific to any network SHAS & Without considering transmission, electronics and encoding cost \\ \hline
[67] (06) & Transmission cost model (uncoded and convolutional code with Viterbi decoding)/A single hop WSN model & Experimental based on Mica2 sensor node at outdoor environment \\ \hline
[69] (07) & Power estimate based on specific devices/Specific to sensor node & Power consumption based on FPGA and ASIC \\ \hline
[66] (08) & The energy efficiency (ARQ and BCH code)/A single hop WSN model & Log-distance path loss channel model, result based on Mica2 sensor node, ATmega128L processor and CC1000 radio module \\ \hline
[56] (10) & The energy consumption by HARQ system incorporating re-transmission factor (convolutional code)/Modified multi-hop WSN model & Log-distance path loss channel model \\ \hline
[71] (13) & Energy efficiency calculation based on packet error probability/Single hop and multi-hop & Rayleigh fading channel, Offset Quadrature Phase Shift Keying (QQPSK) modulation \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of energy consumption and efficiency model.
## 4 Experimental Results
### Proposed JD-TV vs Standard CS Video Compression Schemes  
In order to analyze the performance of the proposed JD-TV methodically it is compared with conventional CS-based schemes i.e., MS-residual [12], k-t FOCUSS [13], MC-BCS-SPL [17] as referred in Section 2. The results presented in Table 7 (gain of proposed and conventional schemes w.r.t independent BCS) are the average of the total frames obtained at a block size of \(16\times 16\) and GoP of 8.  
Figure 6: Visual quality analysis of different video sequences at multiple subrates using independent BCS-TV-AL3 and the proposed JD-TV.  
The results in Table 7 clearly show that the proposed JD provides considerable gain for various motion content video sequences at lower subrates than conventional CS-based schemes. Further, is can also be observed that the gain decreases as the subrate increases, which is due to impact of F\({}_{\text{K}}\) subrate as discussed earlier. In addition, the focus of the proposed scheme is to provide better reconstruction at lower subrates.
# UY-NET: A Two-Stage Network to Improve the Result of Detection in Colonoscopy Images
## 3 Method  
### UY-Net  
UY-Net consists of two main components: (1) image segmentation and (2) object localization and detection. In the present study, image segmentation was first performed, followed by object detection. In the first stage, U-Net (with the Adam optimizer and ResNet as the backbone) was applied to segment images. Figure 2 presents sample images of segmentation.  
In the second stage, YOLOv4 (DarkNet as the backbone) was utilized to detect colorectal polyps. Its application resulted in the bounding box localization information of polyps, including xcenter, ycenter, yolov, and yoloh. Figure 3 illustrates the framework of UY-Net for image segmentation, localization, and detection.  
As shown, xcenter represents the proportion of the center x-coordinate of the bounding box relative to the length of the entire image's x-axis. Similarly, ycenter represents the proportion of the center y-coordinate of the bounding box relative to the length of the entire image's y-axis. On the other hand, yolow represents the proportion of the width of the bounding box relative to the width of the entire image, while yoloh represents the proportion of the height of the bounding box relative to the height of the entire image.
# A Stigmergy-Based Differential Evolution
## 4 Experimental Results
### Benchmark Used in the Experiments
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{2}{c}{**Execution Time \(\mu(\sigma)\) for 256 Individuals**} & \multicolumn{2}{c}{**Execution Time \(\mu(\sigma)\) for 512 Individuals**} \\ \hline
**Function** & **DE** & **SDE** & **Speedup** & **DE** & **SDE** & **Speedup** \\ \hline F1: Sphere & 9.2106 (0.0782) & **8.7910 (0.0708)** & **1.05** & 10.3554 (0.0606) & **9.2490 (0.0638)** & **1.12** \\ F2: Step & 11.4135 (0.0608) & **10.6315 (0.0899)** & **1.07** & 12.6158 (0.0241) & **11.3259 (0.0703)** & **1.11** \\ F3: Sum Powers & 9.2367 (0.0949) & **8.8988 (0.0745)** & **1.04** & 10.0024 (0.0833) & **9.4303 (0.0487)** & **1.06** \\ F4: Sum Squares & 9.2517 (0.1358) & **8.7713 (0.0808)** & **1.05** & 10.0787 (0.0611) & **9.3388 (0.0978)** & **1.08** \\ F5: Dixon and Price & 14.4088 (0.1586) & **13.9450** (0.0560) & **1.03** & 15.7625 (0.1041) & **14.8474 (0.0390)** & **1.06** \\ F6: Rosenbrock & 19.2443 (0.0919) & **18.8281** (0.0662) & **1.02** & 21.1980 (0.1622) & **19.9684** (0.0738) & **1.06** \\ F7: Schwefel 2.22 & 6.1812 (0.0072) & **5.7724 (0.0417)** & **1.07** & 6.3052 (0.0066) & **6.0008 (0.0449)** & **1.05** \\ F8: [PERSON] & 9.6917 (0.1798) & **8.8270** (0.0528) & **1.10** & 10.3362 (0.0354) & **9.4783 (0.0824)** & **1.09** \\ F9: [PERSON] & 9.
## 3 Results
### Performance and Test of Methods Applied for Hypotheses I and II
Figure 7: Maps of aquatic vegetation developed from the image acquired on 16 August 2013, based on thresholds modified from the traditional CT model for 11 July under hypothesis I (**A**) and thresholds modified from tradition CT model of 11 July under hypothesis II (**B**). Note we did not map the emergent in graph A, as there was no correlation between image dates (see Figure 6A) and therefore no support for generating modified CT model thresholds with this approach. We applied the traditional and modified classification tree models developed under each hypothesis to generate maps for 16 August 2013 (Figure 7). There was little similarity in the distributions of aquatic vegetation types mapped with modified models developed under hypothesis I with the results from the traditional CT model (Figure 4B) developed with the image acquired for 16 August and corresponding field samples (sample group II). The spatial distributions of aquatic vegetation types based on hypothesis II, however, were consistent with the results from the traditional CT model (Figure 4B). Areal extents of aquatic vegetation types in the four sections mapped with the modified CT model under hypothesis I were substantially larger than the areas of aquatic vegetation types mapped with the traditional CT model, confirming that this approach was not appropriate for adjusting a CT model to map aquatic vegetation types for a point in time. Spatial extents of vegetation types mapped with CT thresholds modified under hypothesis II were fairly consistent with the areas mapped with the traditional CT model (Figure 8). We assessed the accuracy of the three classification maps with the ground-truth samples from sample group II (Table 8). Results were highest for the traditional CT model generated for August, and the overall accuracy of modified CT model based on hypothesis II is higher than that based on hypothesis I.
# Application of Directed Energy Deposition-Based Additive Manufacturing in Repair
## 4 Industrial Case Studies
However, to implement this approach, several challenges should be considered and addressed, for instance, an adaptive and automated digitizing, adaptive surface reconstruction, and precise machining strategy, for individual blade geometry to obtain a reliable and cost-time effective repair approach. Integral rotors of an aircraft engine, which are commonly known as bladed disks or \"blisks\", are made of titanium alloys. In general, these blisks get locally damaged over their larger areas (Figure 17a). It is reported that due to their aerodynamic profile that must be repaired precisely, the repair of these blisks faced several challenges. Figure 15: Process for generating a parameterized geometric model required for DED-based defect repair: (**a**) acquire point clouds and generate mesh, (**b**) extract Prominent Cross Section (PCS) from non-defective region and extrapolate missing section, (**c**) reconstruct repaired model in CATIA(tm), and (**d**) extract Boolean difference between (**a**) and (**c**) to obtain the repair volume [18]. Another limitation in their repair was the high-reactivity of the titanium that resulted in undesired hard phases in the final microstructure. The solution was to carry out the repair process in a closed inert gas chamber, and consequently, the undesired reaction could be reliably suppressed. The cross-section of a compressor blade repaired by DED, as indicated in Figure 17b, demonstrated that a dense part of the plant is not completely completely destroyed. Figure 16: Thin-curved compressor blade before and after repairing [91]. Figure 9:0 (**a**) Repairing for damaged titanium blisk, (**b**) microstructure of the laser deposited titanium build-up, (**c**) High-cycle fatigue (HCF) results of laser-generated titanium alloy compared to the reference material [101]. with fine crystalline microstructure was achieved. Thereafter, the high-cycle fatigue behavior of those specimens was evaluated, and it was found that the fatigue strength of the deposited sample was slightly higher than that of the reference material, as visible in Figure 17c.