esa-sceva/satcom-chunk-collection huggingface dataset:
# Poisoning Attacks against Communication and Computing Task Classification and Detection Techniques
## 3 System Model
### Dataset  
The dataset representing the 2C tasks (requests) with the three mentioned features consisted of \(l=1000\) examples. For these 2C requests, some representative cases were chosen.  
The communication- and computing-related features are described below with labels as tuples, with latency in terms of [milliseconds, PER]. The communication labels [\(\mu_{j}^{1}\),\(\mu_{j}^{2}\)] for each use case were obtained according to the QoS of 5G defined by ETSI in \"(2022-05)168 3 GPP TS 23.501 version 17.4.0\" release 17:  
* Process automation [\(50\), \(10^{-3}\)].
* V2X collision avoidance [\(5\), \(10^{-4}\)];
* Mission-critical data [\(200\), \(10^{-6}\)];
* Intelligent transportation system [\(30\), \(10^{-5}\)];
* Low latency eMBB [\(10\), \(10^{-6}\)];
* Live streaming [\(300\), \(10^{-8}\)];
* Conversational video [\(100\), \(10^{-2}\)];
* Interactive gaming [\(100\), \(10^{-3}\)].  
For the computational complexity feature, random values following a uniform distribution were selected in the range of [\(200\), \(5000\)] GFLOPs. The choice here can be supported by the diversity of requests generated. Video processing or ML model training tasks may have higher complexity than signaling tasks. In addition, the same category of tasks such as video processing, compression, and size modification may have different complexities. Moreover, same-category tasks with the same process, such as compression, may differ in their workloads. All these points make the choice highly random.  
The dataset \(D\) combining all features consisted of 1000 examples with 3 features each. The representative cases were modeled with 2D Gaussian distributions for the first two axes (end-to-end latency and reliability), with the means \(\mu^{n}\) and the variances \(\sigma^{n}=\sqrt{0.1\times\mu^{n}}\). For the last axis (computational capability), the dataset was modeled with a single uniform random distribution.  
\begin{table}
\begin{tabular}{c l l l l} \hline \hline
**i** & **End-to-End Latency** & **Reliability** & **Computational Capability** & **Representation** \\ \hline
0 & Low & Low & Low & 000 \\
1 & Low & Low & High & 001 \\
2 & Low & High & Low & 010 \\
3 & Low & High & High & 011 \\
4 & High & Low & Low & 100 \\
5 & High & Low & High & 101 \\
6 & High & High & Low & 110 \\
7 & High & High & High & 111 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The 2C links, attributes, and representation.
## Materials and Methods
### PolSAR Observables

Table 2: The identified crop fields and the number of pixels for classification training and testing. into 10 groups and given their abbreviations. First, groups 1-3 were the widely used backscattering power related parameters including SAR backscattering coefficients in linear polarization basis (HH, HV, VV), backscattering coefficients in the Pauli basis (HH + VV, HH-VV) and total backscattering power (Span). These parameters can be directly extracted or computed following simple operations from the elements in either the covariance matrix or coherency matrix. Three backscattering ratios (HH/VV, HV/HH, and HV/VV) sensitive to target characteristics were also considered, which can be obtained from the covariance matrix and classified as group 4. The polarimetric complex coherences between polarimetric channels were also selected, which have been often used for crop phenology monitoring and crop growth stage identification [24; 25; 31; 36]. For two polarization channels \(i\) and \(j\), the polarimetric complex coherence is given as [45]:  
\[\rho_{ij}=\frac{\left\langle S_{i}S_{j}^{*}\right\rangle}{\sqrt{\left\langle \left|S_{i}\right|^{2}\right\rangle/\left|S_{j}\right|^{2}}} \tag{4}\]  
where \(<\!\!>\) denotes the sample average. Four complex correlation coefficients were employed, which can be split into four amplitudes and four phases marked as group 5 and group 6. In past studies, polarimetric target decomposition has proven to characterize scattering mechanisms and been frequently used for generating polarimetric features in agricultural applications. Two mostly representative methods, i.e., the Freeman-Durden three-component decomposition [46], and the H-A-alpha decomposition proposed by [PERSON] [47], were used. In addition, a recent study has shown that a model-based decomposition method, i.e., Neumann decomposition [48], might be more effective in improving the crop classification accuracy with respect to the Cloude-Pottier decomposition [41]. Therefore, Neumann decomposition was also employed in our study. Three decomposition methods were marked as groups 7-9. In addition, the radar vegetation index (RVI) [49] is highly sensitivity to crop parameters [50; 51; 52]; thus, it was used in this study and marked as group 10. The expression of RVI is defined as [53]:  
\[RVI=\frac{4\min(\lambda_{1},\lambda_{2},\lambda_{3})}{\lambda_{1}+\lambda_{2 }+\lambda_{3}} \tag{5}\]  
where \(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\) are the eigenvalues of the covariance/coherency matrix.
# A PUF- and Biometric-Based Lightweight Hardware Solution to Increase Security at Sensor Nodes
## 6 Experimental Results and Discussion  
Since the proposed protocol provides confidentiality, integrity, and freshness (with derived keys and nonces), remote attacks are reduced. Experimental results in Sections 6.1 and 6.2 show that the proposed sensors reduce counterfeiting, impersonation, and non-repudiation attacks (from fake sensors or malicious users). Section 6.1 also shows that information leakage from Helper Data is negligible. Section 6.3 illustrates implementation results in low-cost hardware. Experimental results in Section 6.4 show that even accessing the sensor or stealing it, the attacker has a small probability to be considered as legitimate. Section 6.4 also shows that information leakage from Salted Data is negligible and that entropy of nonces is enough for the key derivation function.
# Optimal Base Station Density of Dense Network:
## 5 Numerical Results and Discussion
### Effect of SmBS Density on the Link Rate  
We conduct the Monte Carlo simulation to verify the analytical results and draw the simulation and theoretical results. The theoretical results are generated using Equation (9). As Figure 4 shows, the results match well. The figure shows the effect of \(\lambda_{\text{m}}\) on the expected link rate of the SmBS tier. As SmBS density increases, there are three phases for the link rate. In the first phase, the link rate increases with SmBS density, but the speed of growth gradually decreases. In this phase, when there are denser SmBSs, more data traffic can be offloaded to the SmBS tier. Meanwhile, denser SmBSs largely shorten the distance from a user to its associated SmBS, which offers better signal quality. Though it can also incur more interference, the gain outperforms the cost in this phase. However, since the interference accelerates, the increase speed of the link rate slows down. The second phase is the peak of the link rate, where the gain equals the interference cost. The horizontal axis of the peak point is the optimal SmBS density for the link rate. Then, in the third phase, the interference accelerates at a greater rate as SmBSs become denser and overwhelms the gain that densification brings. It indicates that superfluous BSs can degrade the network performance instead of gain. The curve fitting result is also provided in Figure 4, and it indicates that the fitting results matches the theoretical results well.  
With Equation (9), Figure 5 shows the effect of SmBS density on the link rate under different SmBS transmit powers. There is always an optimal SmBS density when SmBS transmit power varies. However, the transmit power indeed has a significant influence on SmBS deployment. As SmBS transmit power increases, the link rate increases and decreases faster, and the optimal density also decreases. The reason why the link rate increases faster is that SmBS with stronger transmit power offers better signal quality. However, when SmBS is of high density, stronger transmit power also accelerates interference at a faster rate, which degrades the link rate sharply. It is also observed that the SmBS with lower transmit power has higher optimal deployment density. It is consistent with intuition that we can deploy more BSs if we reduce the transmit power. Although denser SmBSs cannot enhance the maximum rate of each link, they can offer more SmBSs to suffer the network load, which contributes to the network performance. We will talk about the load in the next section.  
Figure 4: Comparison between the theoretical result and Monte Carlo simulation. \(P_{\rm m}/P_{\rm M}=0.001\).  
Figure 5: The effect of SmBS density on the link rate under different SmBS transmit powers.
## 6 Discussion
### Governance
#### 6.3.4 Port State Controls  
Port State Controls (PSCs) are an important element in enforcing safety in shipping, and they will play an important role in maritime cybersecurity. As a representative of the Netherlands ship-owners association informed \"we have members who have gone to the US for example who tell us that they have been questioned for two hours about their cybersecurity on arrival in the port. In other countries it might be quite basic. You have the port state control system and then you have all kinds of reasons for a deficiency, but there's not a specific rule for cyber security.\" [74] Some port states are clearly implementing stringent inspections on cybersecurity. However, to date it does not appear that there has been a detention based on a cybersecurity deficiency. For a Port State Control Officer (PSCO) to detain a ship requires \"clear grounds\" of a breach. For physical systems like fire safety, or lifeboats, where clear requirements are set out, this is much easier to prove or show with photographic evidence for example. Ship cybersecurity breaches are much more difficult to prove, and the regulations and overall expertise from both sides, i.e., the inspection side and the ship operator side, is not yet sufficient. A PSCO who detained a ship based on alleged cybersecurity defects would be at risk of legal challenge by the ships owners and the possibility of liability for losses arising from the ship's detention [74]. For that reason, it is unlikely that ship detentions will occur as part of PSC unless there is very clear evidence of a deficiency. Further, a detention would seem unlikely until there are clearer practical guidelines on what exactly would constitute clear grounds of a cyber deficiency.
## 4 High Average Power Short-Pulse Thin Disk Lasers at HiLASE
### Beamline C
#### 4.3.2 Mid-IR Generation  
The mid-IR wavelength range (between 2 and 8 \(\upmu\)m) is important for applications, such as minimally-invasive neurosurgery and cardiosurgery, plastic and polymer processing and trace gas monitoring. Science-relevant applications include, e.g., high harmonic generation [42], dielectric laser acceleration [43], or research in silicon modifications [44]. The mid-IR can be obtained by optical parametric generation (OPG) and amplification (OPA), where a powerful pump beam of the shortest wavelength generates signal and idler beams of longer wavelengths.  
In the proposed scheme, the powerful beam of a 100-kHz repetition rate at a 1030-nm wavelength is converted to the wavelengths tunable between 1.6 and 2.1 \(\upmu\)m for the signal and 2.1 \(\upmu\)m and 3 \(\upmu\)m for the idler beams. The seed beam is produced by optical parametric generation in the PPLN (periodically poled lithium niobate) crystal. The wavelength is tuned by the PPLN temperature and a spectral filter behind it. The series of subsequent parametric amplifiers is based on a KTA (potassium titanyl arsenate) crystal, which allows covering of the required wavelength range by angular tuning of \(\pm 4^{\circ}\) only and is highly transparent up to 3.5 \(\upmu\)m. The walk-off-compensated crystal pair will increase the efficiency and beam quality. The signal beam is amplified in these stages, and the idler beam is taken from the last amplifier. This is due to a more affordable diagnostics below 2 \(\upmu\)m. The output power about 10 W in the mid-IR pulses of a few picosecond pulse width should be obtained for the 100-W pump at the 100-kHz repetition rate. Even a higher output power could be achieved at the expense of worse beam quality [45].  
The experiments were conducted with a modified setup. The continuous-wave seeding at 1.94 \(\upmu\)m decreased the generation threshold of the OPG stage and increased the generated output up to two orders of magnitude. The single-stage amplifier fixed to the seed wavelength was based on a KTP (potassium titanyl phosphate) crystal, which is easily affordable in good quality. The proper crystal setting and pulse synchronization were successfully tested at a lower pump power. The beam shapes and spectra were determined by the seed laser diode. The experiments will follow an increased pump power and another amplifier stage. The numerical calculations show that the thermal issues leading to a phase mismatch should not decrease the output power significantly due to the high temperature acceptance of the KTP crystal of about 80 \({}^{\circ}\)C (FWHM) [46].
###### Abstract  
Mask alignment of photolithography technology is used in many applications, such as micro electro mechanical systems' semiconductor process, printed circuits board, and flat panel display. As the dimensions of the product are getting smaller and smaller, the automatic mask alignment of photolithography is becoming more and more important. The traditional stacked XY-Oz stage is heavy and it has cumulative flatness errors due to its stacked assembly mechanism. The XXY stage has smaller cumulative error due to its coplanar design and it can move faster than the traditional XY-Oz stage. However, the relationship between the XXY stage's movement and the commands of the three motors is difficult to compute, because the movements of the three motors on the same plane are coupling. Therefore, an artificial neural network is studied to establish a nonlinear mapping from the desired position and orientation of the stage to three motors' commands. Further, this paper proposes an image-servo automatic mask alignment system, which consists of a coplanar XXY stage, dual GIGA-E CCDs with lens and a programmable automatic controller (PAC). Before preforming the compensation, a self-developed visual-servo provides the positioning information which is obtained from the image processing and pattern recognition according to the specified fiducial marks. To obtain better precision, two methods including the center of gravity method and the generalize Hough Transformation are studied to correct the shift positioning error.  
automatic optical inspection; pattern recognition; artificial neural-network; generalized Hough Transform; mask alignment; XXY stage +
Footnote †: journal: Applied Physics Letters
## 4 Discussion  
In this work, a new design of a wideband and wide beam-width circularly polarized 2 \(\times\) 2 antenna array covering the full Ka-band, from 27.5 to 31 GHz, has been proposed. The proposed design is well suited for integration with cost-effective silicon beamformer chips, and it is aimed at implementing wide scanning angles in Ka-band transmit antennas in mobile platforms.  
The antenna array has been fabricated and a complete experimental characterization has been provided in this paper. The results are in good agreement with simulations, although there are some discrepancies caused by fabrication tolerances, measurement errors, and the uncertainty of the material properties in the Ka-band.  
With the aim of reaching a compromise between price and performance, in a reduced area and a low profile, the design has been implemented in a planar technology using a standard multilayer PCB manufacturing process. Several techniques to improve performance have been applied, such as stacked patches, aperture coupling, cavity backing and a sequential rotation configuration. Thus, with a radiating element that yields a bandwidth of 16.5%, the 2 \(\times\) 2 antenna array provides a wide beam-width ranging from 53.4\({}^{\circ}\) to 42.1\({}^{\circ}\), while the maximum directivity varies between 10.7 and 12.9 dBi. Mutual coupling less than \(-\)20 dB and an axial ratio below 3 dB at all frequencies are obtained.  
Conceptualization, methodology, 3D modelling, multi-layer PCB stack-up definition, investigation, simulations, and experimental validation was carried out by [PERSON], [PERSON] and [PERSON]; [PERSON] carried out the original draft preparation and [PERSON] contributed to the writing of the paper. [PERSON] and [PERSON] were in charge of the project supervision and funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by Spanish MINECO-FEDER project RTC2019-007039-7.  
Data Availability Statement: Data is contained within the article.  
Conflicts of Interest: The authors declare no conflict of interest.
## 3 The Proposed Circuits
### Results Circuit Cost and Quantum Cost  
The number of AND, XOR, and NOT operation gates, which were used to create and build each reversible gate or circuit, was referred as the circuit cost. Circuit cost for the FG and TR gates were \(1\alpha\) and \(2\alpha+1\beta+1\gamma\), respectively [22]. Where \(\alpha\), \(\beta\), and \(\gamma\) show the number of XOR, AND, and NOT gates, respectively. As shown in Figure 11, the proposed 1-bit reversible comparator circuit was designed with two TR gates, one FG gate, and one NOT gate. Thus, the proposed reversible comparator with two TR gates, one FG gate, and one NOT gate had a circuit cost of \(5\alpha+2\beta+3\gamma\) and quantum cost of \(((2\times 4)+1)=9\). Thus, 4 was the quantum cost for the TR gate and 1 was the quantum cost for the FG gate. Because two TR gates were used, the cost of the proposed one-bit comparator circuit for the two TR gates and one FG gate was 9. Figure 15 shows the quantum cost required for  
Figure 13: Simulation results for TR gate.  
the performance of the proposed comparator with traditional technology and QCA-based technology, in exchange for our implementation design and [2]. Figure 15 confirms that QCA-based design was less costly than current technology design. Table 5 shows the quantum cost for the proposed reversible circuit. The following equation was used to calculate the quantum cost in QCA technology, where the area is the consumption and occupation area of the QCA circuit and latency is the delay of the QCA circuit.  
\[\text{Cost}=\text{Area}\times\text{Latency}^{2}\]  
Figure 14: Simulation results for the proposed 1-bit comparator.  
Figure 15: Quantum cost for the proposed circuits [2].  
Tables 5 and 6 show the quantum cost for QCA implementation and layout of the proposed FG gate, TR gate, and one-bit comparator circuit, as well as a comparison for the proposed QCA reversible circuits by previous work layout. As represented in Tables 5 and 6, the quantum cost for the comparator circuit was 3.087 for [2], 9 for traditional implementation, and 2.133 for our QCA implementation. As a result, our proposed circuit was 76.3% and 30.9% superior, in terms of the quantum cost of its implementation than with traditional technology and [2], respectively as.
# Object-Based Approach for Adaptive Source Coding of Surveillance Video
## 2 Related Works
An adaptive method based on an optimized effort strategy that can achieve constant surveillance video transmission quality in consideration of video content should be investigated. Precise and rapid dynamic adjustment of the image quality of moving objects in videos transferred over a wireless network is the key concept of this study. Based on our literature survey, frame rate control source coding parameters, MB size prediction, ROI, and QP determination can be used to adjust the bitrate and enhance video quality. However, a high-level metric using an ROI to adjust source coding parameters has not been well studied to date. Frame layer rate control is a high-level adaptive quality control method. It uses frame selection approach to reduce the overall amount of video transmission, which enhances meaningful information to improve video quality. [PERSON] et al. [11] created a cooperative framework that uses semantic filtering to select frames, which can achieve better image quality monitoring results over limited bandwidth. [PERSON] et al. [12] proposed a content-adaptive traffic prioritization strategy for H.264/SVC communications over IEEE 802.11e wireless networks. This strategy estimated the perceptual impact of data losses in the different types of enhancement layers for a large set of videos first and proposed a content-adaptive traffic prioritization strategy based on the identification of the most important parts of the enhancement layers of the video sequence by means of a low complexity macroblock analysis process. If a layer, temporal or spatial, has motion detected, it should have high priority to preserve than others. The above two studies pointed out that by scalable coding and by dropping different types of scalability layers or frames based on the content characteristics could achieve post-encoding to adapt content dynamics. High-level quality control methods adopt content dynamics and picture complexity to help the coding of bitrate, which has less computational demanding than VBR scheme. Dynamics of moving objects is a kind of high-level metrics that can greatly improve the high-level quality control with low-level metrics, including the motion detection used in [12].
# Open Collaborative Platform for Multi-Drones to Support Search and Rescue Operations
## 3 Open Collaborative Platform
### Open Collaborative Platform for Multiple Drones
#### 3.2.1 Register Drone to the Open Platform  
We assume that people (e.g., volunteers or SAR teams) who want to join the SAR operation already have the Krypto module ready attached to their drones. As previously  
Figure 4: Architecture of the Krypto module. (a) Upload the location of drone to the platform. (b) Upload the estimated location of victims to the platform. (c) Download the searching path if necessary from the platform.  
discussed, all of Krypto's hardware is off-the-shelf components, and its source code or executable file can be downloaded and installed into Raspberry PI. Initially, the Krypto module will register itself with its current location information to the Open Collaborative Platform's registration service through any common wireless connection (e.g., WLAN or Cellular Network). Additional information can be entered by the user, if necessary, e.g., drone's power level, drone model number, etc. In most drones on the market, APIs are usually provided to access information from a drone or give flight path instructions, e.g., DJI Onboard SDK [6], Mobile SDK [7], or Ground Station Control [38]. After the Krypto module is registered, the platform will assign a public key (i.e., applicationServerKey) and a private key that allow the push service to authenticate the user/application (i.e., Krypto module). Once the registration process is completed with the platform, an ID and an _initial search path_ are assigned to the registered Krypto.  
The Open Collaborative Platform is open to anyone/module who is registered with the platform and can be accessed by an HTTP request using methods like GET and POST to describe the actions, e.g., basic information from Krypto or searching path information from the platform. For those drones that did not provide any APIs for control and status information, users can register their modules using a mobile phone. The open platform will register the Krypto module's current location during the registration process. Once the user is registered, an email with the assigned initial search area (or initial search path) is sent to the drone operator. For ease of discussion, we assume APIs are accessible by the Krypto module. This allows dynamic update drone's searching path. Notice that the search area or searching path can be updated by email (or message) if necessary for those drones that did not provide APIs.