esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvements of the xAAL Home Automation System
## 5 The xAAL Communication Protocol
### Security Considerations  
#### 5.6.1 Local Communications  
By design, home automation protocols over IP do benefit off the security offered by the home network, which is made of physical wires or of WiFi networks with WPA security. It is therefore at least as hard to get the control of a home automation from outside as to be able to join the home network. A protocol with messages in clear could have been fine.  
However, the main threats may probably not come from outside. Nowadays, end users (happy eye balls, RFC 8305) are used to bring many smart devices at home, to install many promising applications, embedding times-to-times suspicious code. If the home automation protocol does not have any security code, a malicious application could take control of it (e.g., deactivate the alarm, unlock the door for thieves). More probably, such a third part application could spy on the home automation activity, monitor users' habits and preferences to feed ads and recommendation systems for consumers.  
Such a situation is not desirable, or else, at least, with a clear and informed opinion of the end user and its agreement. This is why a certain level of security should be added to home automation systems. Messages should not be in clear, nor ciphered with a hard-coded static key; secret keys must differ from one home to another; secret keys must not be preconfigured by companies, but chosen by the end user.  
In xAAL versions 0.5 and later, messages are ciphered using the Poly1305/Chacha20 algorithm. According to experts, it is at least as much stronger than others (e.g., AES). It is now in the cipher suits for TLS (RFC 7905 [35]). Due to its communicating bus principle, xAAL devices cannot negotiate cryptographic parameters prior to sending useful data as with client-server communication principles (selecting a cipher algorithm, a nonce, Diffie-Hellman keys, etc.). xAAL messages must be self-content, also regarding security parameters. xAAL uses a pre-shared symmetric key. This is the only parameter that the end user has to configure. The nonce is a timestamp with a small acceptance window to allow clock drifts.  
The security proposed by xAAL is a compromise. Of course, this can be criticized. However, a higher security level will break simplicity and efficiency of xAAL. The security proposed by xAAL is high enough to protect data carried by home automation communications, and fits requirements to address the threats that the model considers.
## 4 Variable Deployment Models  
This section first presents the concept of Variable Deployment Models in detail followed by a formal definition of the corresponding metamodel in Section 4.1, which provides the basis for realizing Step **C** of our method. In Section 4.2, we introduce algorithms for resolving the variability to generate concrete executable deployment models. These algorithms are implemented by the Variability Resolver as described in Step **C** of the method.  
A Variable Deployment Model is a deployment model which has _conditional elements_. These are components or relations which have at least one Variability Condition assigned. Only if all Variability Conditions of a conditional element evaluate to true is the associated component or relation present in the deployment. This significantly eases handling multiple variants of a deployment since the human operator does not have to select one out of many deployment models implementing different variants, but only has to provide the required Variability Inputs.  
Figure 3 shows this concept based on the motivating scenario introduced in Section 2. Instead of having two different deployment models for the two variants, the shown Variable Deployment Model contains all components and relations of both variants but associated with the Variability Conditions under which they are present in the respective deployment variant. The component \"Web Component\" should always be present and, thus, has no conditions assigned. However, the components \"Dev Database\", \"Dev Runtime\", \"Private VM\" and \"Private OpenStack\" along with their respective relations are conditional elements which should be only present in the development variant. Therefore, they have a condition assigned that evaluates if the desired deployment is intended for development. In contrast, the components \"Prod Database\", \"Prod DBMS\", and \"Prod Runtime\" along with theirrespective relations are conditional elements having a condition assigned that evaluates if the desired deployment is intended for production.
## 5 Conclusions  
In this work, a novel transition from MS to RESIW based on the equations of the superellipse is proposed. A design procedure is presented and initial values for the design parameters are provided. The simulated reflection coefficient of a back-to-back transition is less than 20 dB in an 87% fractional bandwidth and less than 26 dB in an 84% fractional bandwidth. The measured return losses are greater than 14.5 dB and the insertion losses are lower than 1 dB in a 83% fractional bandwidth. The new wideband transition presents improved (simulated and measured) responses, with better return losses and lower insertion losses, when compared with the previous MS to RESIW transition.  
Conceptualization, D.H., S.C., and H.E.; methodology, D.H. and H.E.; software, D.H.; validation, D.H., V.N., and J.A.M.; formal analysis, D.H.; investigation, D.H. and H.E.; resources, A.B. and V.E.B.; writing--original draft preparation, D.H.; writing--review and editing, H.E.; supervision, A.B., V.E.B., and H.E.; project administration, A.B. and V.E.B.; and funding acquisition, A.B. and V.E.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministerio de Ciencia e Innovacion, Spanish Government, under Research Projects PID2019-103982 RB-C44 and PID2019-103982 RB-C41.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SIW & Substrate Integrated Waveguide \\ PCB & Printed Circuit Board \\ ESIW & Empty Substrate Integrated Waveguide \\ RESIW & Ridge Empty Substrate Integrated Waveguide \\ FBW & Fractional Bandwidth \\ MS & Microstrip Line \\ CST & Computer Simulation Technology \\ WR & Rectangular Waveguide \\ RL & Return Loss \\ \end{tabular}
# Stable Evaluation of 3D Zernike Moments for Surface Meshes
## 3 Algorithms for Computing the 3D Zernike Moments for a Surface Triangular Mesh
### Two Algorithms for Computing Zernike Moments
#### 3.5.1 Exact Zernike Moments for Shapes Described by Surface Triangular Meshes  
As described on the previous subsection, the 2D quadrature rule of strength \(N\) will define exact Zernike moments of order up to \(N\) on any triangle of the surface mesh. For a given \(N\), if a quadrature rule \(R\) with this strength exists, it is then sufficient to use the procedure TRIANGLE defined in Algorithm 1 on all triangles of the surface mesh and then accumulate the results. As described below, we were able to generate quadrature rules for \(N\) up to 101. The corresponding algorithm has a complexity of order \(O(M\times N^{5})\), where \(M\) is the number of triangles in the mesh and \(N\) the Zernike order. This can be derived as follows. The computation is performed independently on all triangles of the mesh, hence the factor \(M\). For each facet, we need a quadrature rule \(R\) of strength \(N\), whichitself requires a number of points \(N_{R}\) that is empirically of order \(N^{2}\) (see for example [60]). As \(N^{3}\) functions \(g_{nl}^{m}\) need to be evaluated for each point in the quadrature rule, we get the overall time complexity \(O(M\times N^{5})\).  
```
Input: The triangular mesh with \(M\) facets supposed to fit inside the unit ball. The maximum order \(N\) for the 3D Zernike moments. The tolerance TOL for finite precision computation. Initialize: Set \(c_{nl}^{m}=0\) for\(k=1,\ldots,M\)do (1) Define \((A,B,C)\) the three vertices of triangle \(k\). (2) Compute \(c_{nl}(T)=\text{TRIANGLEREC}(N,A,B,C,TOL)\) (3) Update \(c_{nl}^{m}=c_{nl}^{m}+c_{nl}^{m}(T)\). endfor Output: The finite precision Zernike moments \(c_{ln}^{m}\) of the shape.
```  
**Algorithm 3**_Finite precision Zernike moments for surface triangular meshes
# Efficient Next-Hop Selection in Multi-Hop Routing for IoT Enabled Wireless Sensor Networks
## 5 Results and Analysis
### Residual Energy  
Figure 4d elucidates the amount of residual energy left after a number of routing operations are performed. It highlights that EMRP contains more residual energy as compared to counterparts. We utilized the energy model to obtain these energy values in the trace files generated after simulation. Afterward, AWK script files were used to extract these values. Results show that 85.2456 uJoules, 90.3518 uJoules, 93.4127 uJoules, 96.6095 uJoules and 97.3356 uJoules were residual for PEDAP, LEACH, PEGASIS, DADCNS and L-VH, respectively. EMRP dominated with the maximum residual energy of 99.7463 uJoules. In existing schemes, more energy is consumed during messages for routing operations for data collection and retransmissions. In many cases, packets are either lost or traversed on a long path due to wrong neighbor selection. L-VH and DADCNS identified better tracks and consumed less energy as compared to the other base schemes.
## 3 The Proposed Scheme
### Communication  
After a device goes through the registration process, it must establish a secure communication channel with other devices. When two drones want to communicate directly, all the initiating party has to do is look up the recipient's details in the ARP registry structure table. The receiving party looks up the decryption key for the received encrypted message. Then, using the appropriate symmetric key, the encrypted message is created using the AES-GCM block cypher algorithm. It is essential that AES-GCM is an authenticated encryption that ensures the confidentiality, integrity and authenticity of the message between the parties. During communication, the symmetric key needs to be updated, which can be done, for example, by using a challenge and response protocol besides the encrypted message to exchange a new key.  
This method prevents UAV connection with unauthorized platforms, and each device pair will have its symmetric key, preventing unauthorized access. Even if one communication channel is compromised, the others remain secure. The algorithm also offers a lightweight and authenticated way of encrypting messages for added authenticity. The AES-GCM encryption method is ideal for its compatibility and scalability, even for weaker MCUs.
###### Abstract  
Ultra-wideband (UWB) impulse radar is widely used for through-wall human detection due to its high-range resolution and high penetration capability. UWB impulse radar can detect human targets in non-line-of-sight (NLOS) conditions, mainly based on the chest motion caused by human respiration. The automatic detection and extraction of multiple stationary human targets is still a challenge. Missed alarms often exist if the detection method is based on the energy of the human target. This is mainly because factors such as the range of the target, the intensity of the respiratory movement, and the shadow effect will make a difference between the energy scattered by targets. Weak targets are easily masked by strong targets and thus cannot be detected. Therefore, in this paper, a multiple stationary human targets detection method based on convolutional neural network (CNN) in through-wall UWB impulse radar is proposed. After performing the signal-to-clutter-and-noise ratio (SCNR) enhancement method on the raw radar data, the range-slow-time matrix is fed into a six-layer CNN. Benefiting from the powerful feature extraction capability of CNN, the target point of interest (TPOI) can be extracted from the data matrix. The clustering algorithm is used to simplify the TPOIs to achieve accurate detection of multiple targets behind the wall. The effectiveness of the proposed method is verified by the experimental data.  
convolutional neural network; multiple stationary human targets; through-wall detection; ultra-wideband (UWB) impulse radar +
Footnote †: journal: Applied Sciences  
Article
## 6 Conclusions  
This study proposes a production efficiency model-based algorithm for estimating crop yield in the Midwestern US. The yield estimates have shown to agree well with the NASS survey data in rainfed counties for both corn (R\({}^{2}\) = 0.77; RMSE = 0.89 MT\(\cdot\)ha\({}^{-1}\)) and soybeans (R\({}^{2}\) = 0.66; RMSE = 0.38 MT\(\cdot\)ha\({}^{-1}\)). Different from previous studies, we found that the MODIS GPP algorithm was capable of making reasonable yield estimates with certain considerations. Our analysis suggests using field-measured RUE values instead of biome-wide RUE values in the production efficiency models when estimating the yield for a specific crop type. Moreover, it is important to consider the productivity contribution from other vegetation in mixed pixels. The proposed algorithm does not require a retrospective analysis that constructs the empirical relationships between reported yields and remotely sensed data, and it has the potential for monitoring crop yields over large areas. Future work on yield estimation based on production efficiency models should focus on investigations that consider the subpixel spatial heterogeneity and irrigation effects.
# Spectrum Modeling of Out-of-Band Intermodulation for Dual-Band RF Amplifiers in OFDM Modulation
## 1 Introduction  
To address the increasing demands of modern wireless communication terminals that cover different standards, multiband (or multi-standard) RF transmitters and receivers are the key elements for the latest and future wireless transmission systems [1], such as cognitive radios (CRs) [2; 3; 4; 5], 4G LTE-advanced [6; 7], and 5G [8; 9; 10]. In our previous work, the intermodulation (IM) for single-band signals caused by the nonlinearity of RF amplifiers was first studied [11; 12]. The in-band intermodulation and cross-modulation (CM) for the amplified dual-band spectrum then was later derived in [13; 14].  
However, when the spectrum gets more crowed, especially in 5G, more frequency bands are used, which implies the out-of-band intermodulation [13] from the dual-band amplifiers must be considered, because it will cause the interference to other users' channels, and degrade the modulation quality of the transmitter and the sensitivity of receiver [15]. Figure 1 illustrates the amplified dual-band spectrum including out-of-band intermodulation (red dot lines). The dual-band signals which are divided into the carrier frequencies via \(\Delta f(\Delta f=\left|f_{2}-f_{1}\right|)\) are transmitted simultaneously. For the dual-band amplified signals, the power spectra at the RF amplifier output can be classified into two parts. The first part (blue solid lines) is attributed to linear amplification, IM and CM, which has been derived in [14]. The second part (red dot lines) is referred to as out-of-band IM in Figure 1, located at \(\Delta f\) away from the lower or upper carrier frequencies, which is discussed in this article. Taking 5G NR (new radio) [16] for example, if the amplifier is used to amplify the concurrent signals centered at 1870 MHz (in downlink operating band n3) and 1935 MHz (in downlink operating band n2), the out-of-band intermodulation products will center at 1805 MHz and 2000 MHz, which are located in downlink operating band n3 and band n70 respectively, and will affect the designated downlink transmissions in those two bands. Thus, the interference caused by out-of-band intermodulation will affect the designated downlink transmissions in those two bands. Bandpass filtering or digital predistortion (DPD) techniques are often used to linearize the out-of-band intermodulation such as [17].  
In this article, we derived an explicit expression of the spectrum regrowth for the amplified dual-band orthogonal frequency-division multiplexing (OFDM) signals including the out-of-band intermodulation. This result expands the previous spectrum model [14] to a larger range outside the passband. While the dual two-tone test in [13; 18; 19] could be useful for locating the frequency range of the out-of-band interferences, the proposed out-of-band model of dual-band power spectrum could locate and quantify the out-of-band distortions/interferences.
# Comprehensive Review of Storage Optimization Techniques in Blockchain Systems
## 5 Summary and Future Directions
### Summary
#### 5.1.2 Query Efficiency  
In the process of optimizing blockchain storage, the six methods--pruning technique, IPFS optimization, data sharding, erasure coding, deduplication, and data compression--each impact query efficiency with different trade-offs. Pruning reduces storage space requirements by removing old blocks, enhancing synchronization speed for light nodes, but it decreases efficiency when querying historical data. IPFS optimization improves retrieval speed for large-scale data through decentralized storage and content addressing, yet its efficiency depends on the distribution of data across the network and node availability; if data becomes unavailable or unevenly distributed, query efficiency may be compromised. Data sharding divides the blockchain into multiple shards, with each node handling only a portion of the data, significantly improving query performance, especially in high-throughput scenarios; however, inter-shard coordination and verification may add complexity.  
Erasure coding enhances storage efficiency and fault tolerance by adding data redundancy, but this approach requires substantial computation during decoding, potentially reducing efficiency for real-time queries. Deduplication reduces storage by eliminating redundant data blocks and conserving resources in certain cases, but it requires data reconstruction during queries, which adds complexity and latency, making it less efficient in scenarios with frequent data access. Data compression optimizes storage by reducing data size, but decompression is needed during querying, increasing computational overhead and potentially reducing query speed in high-frequency access applications.  
Overall, data sharding and IPFS optimization can significantly enhance query efficiency in scenarios requiring efficient data processing and rapid data retrieval. Pruning, erasure coding, deduplication, and data compression, while effective in reducing storage redundancy, introduce potential bottlenecks and challenges for query efficiency. Thus, reducing the complexity of queries within blockchain systems represents a trend for future research.
# Sustainable Microalgae and Cyanobacteria Biotechnology
## 2 Applications of Biotechnology on Microalgae and Cyanobacteria Utilization
### High Valuable Compounds from Microalgae
#### 2.1.1 Pigments
Carotenoids play three major roles in the photosynthetic organism: (i) as accessory light-harvesting pigments by transferring electrons to chl a [23]; (ii) structural entities within the light-harvesting and reaction center pigment-proteincomplexes [24], and (iii) molecules involved in the protection against oxygen reactive species [25]. Carotenoids have two profiles: a primary activity involved in photosynthetic processes and a secondary activity produced by microalgae under the following stress conditions [26]. There are over 400 known carotenoids, with -carotene and astaxanthin being the most common carotenoid produced on a large scale. _Dunaliella salina_, which grows under high salinity and light intensity conditions, produced 14 percent of the dry biomass weight of carotenes [27]. In addition to their biological role, carotenoids have piqued the industry's interest in health applications due to their bioactive potential as antioxidants, anti-inflammatory agents, and anti-tumor agents when compared to others. Consumption of a diet supplemented with -carotene has been linked to cancer prevention in some tissues [28]. The freshwater microalgae _Haematococcus pluvialis_, which contains more than 3% dry weight biomass, is the best source of astaxanthin production on a large scale [29]. Cyanobacteria and microalgae can grow in a variety of environments. The ability to modify the metabolism process in cyanobacteria or microalgae under different growth conditions has resulted in the improvement and optimization of a specific compound over two or three industrial bioprocess stages. Cyanobacteria could also produce a wide range of terpenoids and carotenoids via the carotenogenesis biosynthetic pathway. _Anabaena_ and _Nostoc_ species, for example, converted phytoene to lycopene via a reaction catalyzed by a phytoene desaturase [30]. Lycopene is an important molecule in the synthesis of \(\beta\)-carotene from lycopene, which is catalyzed by lycopene cyclase [31], while \(\gamma\)-carotene is an intermediate in the synthesis of \(\beta\)-carotene in two steps catalyzed by lycopene cyclase [32]. The genes involved in carotenogenesis are conserved in photosynthetic aquatic organisms. However, carotenoids are only expressed and produced by a few cyanobacteria and microalgae species. In the biotechnological industry, the expression of certain genes during carotenogenesis is critical to increase carotenoids productivity. Genetic engineering was used in another study to improve carotenoids production in cyanobacteria [33]. The approach of genetic alteration has received a lot of attention in recent years, and a few examples have already been described in cyanobacteria. As a result, effective genetic engineering for other microalgae or cyanobacteria can be explored further by emphasizing the bioactivities of compounds they produce.