esa-sceva/satcom-chunk-collection huggingface dataset:
# Satellite-Based Wireless Sensor Development and Deployment Studies for Surface Wave Testing
###### Abstract  
Although cable-based seismic sensing systems have provided reliable data in the past several decades, they become a bottleneck for large-area monitoring and critical environmental (volcanic eruptions) sensing because of their cost, difficulty in deploying and expanding, and lack of accurate three-dimensional geographic information. In this paper, a new wireless sensing system is designed consisting of a portable satellite device, a self-sustaining power source, a low-cost computational core, and a high-precision sensor. The emphasis of this paper is to implement in low-cost hardware without requirements of highly specialized and expensive data acquisition instruments. Meanwhile, a computational-core-embedded algorithm based on compressive sensing (CS) is also developed to compress data size for transmission and encrypt the measured data preventing information loss. Seismic data captured by the accelerometer sensor are coded into compressive data packages and then transferred via satellite communication to a cloud-based server for storage. Acceleration and GPS information is decrypted by the \(\ell_{1}\)-norm minimization optimization algorithm for further processing. In this research, the feasibility of the proposed sensing system for the acquisition of seismic testing is investigated in an outdoor field surface wave testing. Results indicate the proposed low-cost wireless sensing system has the capability of collecting ground motions, transferring data, and sharing GPS information via satellite communication for large area monitoring. In addition, it has a great potential of recovering measurements even with significant data package loss.  
wireless sensing; embedded data processing; GPS; surface wave test; Rayleigh wave; compressive sampling; nondestructive testing/evaluation +
Footnote †: journal: Sensors
# Comparison Study on Coarseness Index and Maximum Diameter of Rock Fragments by Linear Cutting Tests
## 6 Conclusions  
This study attempted to confirm whether the diameter of the largest rock chip among rock fragments generated by mechanical excavation could serve as an index of excavation efficiency. The \(CI\) and maximum diameter (\(d_{\rm max}\)) were obtained from rock fragments generated under various cutting conditions using a linear cutting test.  
By comparing the relationship between \(CI\) and particle size parameters, such as median particle size (\(d_{50}\)), mean particle size (\(d_{\rm MPS}\)), and absolute particle size (\(d^{\prime}\)), and the relationship between \(d_{\rm max}\) and \(CI\), it was confirmed that \(d_{\rm max}\) was sufficient to replace the other particle size parameters.  
Changes in cutting conditions had similar effects on \(CI\) and \(d_{\rm max}\). An increase in penetration depth and spacing increased both \(CI\) and \(d_{\rm max}\), but \(CI\) had a power relationship and \(d_{\rm max}\) had a linear relationship. Regarding the relationship between the ratio of spacing to penetration depth (\(s/p\)), \(CI\) and \(d_{\rm max}\) increased until \(s/p\) reached a specific value. Subsequent increases in \(s/p\) did not significantly affect the \(CI\) and \(d_{\rm max}\).  
The relationship with the cutting results was also similar for \(CI\) and \(d_{\rm max}\). The cutting force and cutting volume increased exponentially as \(CI\) increased. The \(d_{\rm max}\) also increased, with a linear relationship. In the case of the \(SE\), a linear inverse relationship was formed with \(CI\), and an exponential inverse relationship was formed with \(d_{\rm max}\). When \(d_{\rm max}\) became larger than a certain size, \(SE\) converged to a constant value. This result suggests that \(d_{\rm max}\) can provide information regarding whether the \(SE\) reaches the minimum value.  
Therefore, the results suggest that \(d_{\rm max}\) can replace \(CI\) in determining the excavation efficiency and has an additional advantage. However, it should be noted that this study used a small-scale linear cutting machine and was an experiment using rock-like materials. Therefore, it is necessary to verify from further research on actual rocks using a full-scale linear cutting machine.  
[PERSON]-e.K. proposed the concept of the research and developed the study. [PERSON]-m.N. and [PERSON] contributed to the review of the final manuscript and made recommendations for paper revision. [PERSON]-s.K. conducted the experiments and provided suggestions. [PERSON]-k.Y. and [PERSON] supervised the research and provided important suggestions. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea grant funded by the Korean Government (NRF-2019R1A2C2003636).  
Figure 16: Relationship between maximum diameter and \(SE\): (**a**) rock of UCS 20 MPa; (**b**) rock of UCS 30 MPa; (**c**) rock of UCS 40 MPa.
## 3 MPTCP Congestion Control Background
### Balanced Linked Adaptation Algorithm (BALIA)  
BALIA is a congestion control algorithm that prioritizes both friendly flow behavior and responsiveness to changing network conditions. It dynamically adjusts the sending rate based on the available bandwidth and loss rate, while also balancing the sending rate across multiple subflows. BALIA has proven to be more effective than traditional TCP algorithms, especially in high-bandwidth and high-latency networks.  
The BALIA algorithm enhances the effectiveness of TCP's congestion avoidance algorithm in contemporary network settings that have multiple paths and changing network conditions. BALIA alters both the increase and decrease phases of the AIMD algorithm employed in TCP.  
The BALIA algorithm relies solely on the congestion window size and RTT of the subflow paths. Upon receiving an ACK, the increase in congestion window size for subflow \(i\) is determined using  
\[\frac{C_{i}}{\left(\sum_{j}C_{j}\right)^{2}}\cdot\left(\frac{\beta_{i}+1}{2} \right)\cdot\left(\frac{\beta_{i}+4}{5}\right). \tag{6}\]  
When a congestion event occurs, BALIA decreases the TCP-Reno CW for subflow \(i\) in the range of [1, 1.5] using (7) as  
\[\left(\frac{\text{CW}_{i}}{2}\right)\cdot\min(\beta_{i},1.5) \tag{7}\]  
where  
\[\beta_{i}=\frac{\max_{j}C_{j}}{C_{i}}. \tag{8}\]  
This reduces the size of the congestion window by a factor of 2 multiplied by the minimum of \(\beta_{i}\) and 1.5. This ensures that the rate of decreasing the congestion window size is faster for subflows with a larger \(\beta_{i}\) value, indicating a higher relative bandwidth share [39]. The value of \(\beta_{i}\) is 1 if a single path is used [39].
# A Simple Method to Estimate Weed Control Threshold by Using RGB Images from Drones
## 2 Materials and Methods  
### The Rationale  
The proposed method is organized in four successive steps: (i) capturing aerial images of fields; (ii) estimation of weed cover; (iii) applying a model for the quantification of crop yield losses; and (iv) determination of the economic threshold.  
To capture aerial images, a small UAV equipped with an RGB camera is enough. According to Italian law, a UAV lighter than 1.5 kg can be flown without a license outside of sensitive areas (e.g., inhabited areas, roads, airports, etc.). The flight height can vary in relation to the resolution of the camera sensor, but anyway, the ground size of pixels should not be larger than 15-20 mm.  
After having photogrammetrically orthorectified and mosaicked the images, they are analysed to quantify the total green cover (TGC). For this purpose, we propose the use of Canopeo, a free app developed in Matlab programming language and created by Oklahoma State University [34]. Canopeo can provide (as a percentage of the original image area) the TGC, which is the sum of the weed green cover (WGC) plus the crop green cover (CGC).  
The CGC and WGC can be estimated by using a linear least-squares fitting of the equation expressing the TGC as the sum of products of the number of weeds or crop plants by the respective average cover value of a single weed or crop plant.  
The estimation of the crop yield loss was carried out using a model based on WGC values, and therefore, by knowing the selling price of the crop and the weeding costs (e.g., herbicide, spraying and worker), we can calculate the economic threshold value (i.e., the value of the WGC beyond which the loss of earnings due to the lower yield exceeds the costs of weeding).
# Deploying Secure Distributed Systems: Comparative Analysis of GNS3 and SEED Internet Emulator
## 2 Background
### Emulated and Virtualized Systems
#### 2.1.2 Hypervisors and Data Privacy
[PERSON] and [PERSON] [25] highlighted the emergence of a new DoS attack vector in virtualization technology, particularly in cases where excessive resource allocation to a VM has been permitted. Such attacks can lead to the crash of the physical machine hosting the hypervisor and VM files. [PERSON] et al. [26] focused on _cross-VM side channel attacks_ that exploit vulnerabilities in VMs. They identified specific attack types, including _VM hopping_, which results in DoS by making resources unavailable to the user; _VM escape_, enabling a guest-level VM to attack itself; and _VM mobility_, allowing a VM to move between physical hosts. [PERSON] et al. [27] addressed security risks in Cloud Computing, including exploitation techniques such as footprinting, to analyze the OS platform remotely, botnets for DoS/DDoS attacks on virtualized systems, traversal attacks that compromise data integrity by modifying VM library contents, and malicious code injections through MITM attacks or redirection of communication within the virtualization management agent on the host platform. [PERSON] et al. [28] discussed cybersecurity and privacy considerations in Docker-based virtualization and containerization technology. They explored various existing techniques, such as Sockets and the API, Security Hardening, MAC policies, Type Enforcement, Multi-Category Security, and Docker Security Policies, which can mitigate vulnerabilities to cyber-attacks, including DoS. [PERSON] et al. [29] proposed a security architecture for kernel-based VMs that safeguards guest integrity against malware such as viruses and worms. They concluded that implementing their security model can serve as an effective cybersecurity measure for distributed systems based on virtualization architecture. [PERSON] et al. [30] demonstrated the Prevent Virtual Machine Escape (PVME) solution, which integrates an adaptation of the Bell-LaPadula Access Control (AC) model with virtualization technology to secure and prevent _VM Escape Attacks_. This solution protects VMs on the same host through a core component in the hypervisor, containing an Access Matrix, and another component in the host OS. The VM Escape Attack consists of two phases: \"Placement\", where attackers place malicious VMs as files on the same physical machine as the hypervisor, and \"Information Extraction\", where the attacker extracts information from other VMs using the access key permissions of the hypervisor. The malicious applications aid the attacker in privilege escalation within VMs in IaaS, SaaS, or PaaS solutions.
## 2 Methods
### Quantification of Brain Volumetric Measures and Quality Control  
We conducted whole-brain volumetric analysis using T1-weighted MRAGE scans that were processed with standard FreeSurfer pipeline (version 7.1.0, Laboratories for Computational Neuroimaging, Charlestown, MA, USA). The assessment included volumetric analyses of the following brain regions: estimated total intracranial volume (ETIV), total grey matter (GM), cortex, cerebral white matter, cingulate grey matter, parietal grey matter, entorhinal cortex, third ventricle, fourth ventricle, thalamus, putamen, and hippocampus. Briefly, the processing included signal from the non-brain tissue, transforming to standard space, segmentation of subcortical white matter and deep grey matter structures, intensity normalisation, tessellation of the boundary between grey and white matter, automated topology correction, and reconstruction of grey-white and grey-cerebrospinal fluid surfaces. Volumes of cortical and subcortical structures were then quantified.  
We then followed Enhancing Neuro Imaging Genetics through meta-Analysis (ENIGMA) Cortical Quality Control Protocol 2.0 to assure quality for the FreeSurfer results. Both internal and external quality control images were also checked visually. The unsatisfactorily segmented region volumes were excluded from further analyses. The number of removed values for each measure ranged from 0 to 15. Left banks of the superior temporal sulcus and right peri-calcarine regions had 15 poor-quality observations.
## 2 Theoretical Background
To this end, [PERSON] and [PERSON] [43] proposed the application of machine learning to predicting student performance in an online physics course at Michigan State University. For this, data derived from the tasks performed by the students were used. Ten different variables were extracted, including success rate, success on the first attempt, the number of attempts, the time between task delivery and deadline, the time involved in solving, and the number of interactions with colleagues and instructors. A principal component analysis (PCA) method was applied to transform the variables, and three different sets with two, three, or nine components were generated. After this, the Bayes classifier, I-nearest neighbor (I-NN), k-nearest neighbor (k-NN), Parzen-window, multilayer perceptron (MLP), and decision tree classifiers were applied. Then, the predictions obtained by the classifiers were combined with the genetic algorithm using 200 individuals with 500 generations. The GA proposed by the author achieved optimization of 10% to 12% depending on the number of components in the input. [PERSON] et al. [6] proposed the evolutionary algorithms Interpretable Classification Rule Mining Algorithm (ICRM) [27] and ICRM2 [6] based on grammar-based genetic programming (GBGP). In [PERSON] et al. [6], ICRM was used to predict the dropout of high school students in Mexico. The authors proposed a double-approach prediction on the same algorithm, creating two classification rules: One for identifying students who tend to complete the course and the other for students who tend to drop out. The data used included 60 attributes that range from the entrance test to research data distributed to students. As a comparison method, the algorithm proposed by the author was compared with five classifiers: Naive Bayes, decision tree, Instance-based lazy learning (IBK), Repeated Incremental Pruning (Rip), and SVM. Techniques were also used to reduce the dimensionality of the base. Using the accuracy as an evaluation metric, the results obtained by the proposed algorithm showed that it can be a valid approach, especially considering the ease of interpretation of the generated classification rules.
## 3 Convergence Analysis
### Local Convergence of the First Kind
If \(Re(\alpha)<1/2\), then, from the first inequality of (32) and \(|A_{i}|\leq\gamma(E(x))\) (see the last identity in the proof of Lemma 4 and the penultimate inequality in the previous case), we obtain_  
\[|\sigma_{i}|\leq\frac{|1-\alpha|(|A_{i}-1|^{2}+|A_{i}|^{2}\,|B_{i}|)+|\alpha| \,|A_{i}|\,|B_{i}|}{|1-\alpha|-|\alpha|\,|A_{i}|\,|1-B_{i}|}\leq\frac{|1- \alpha|\,\mu(E(x))+|\alpha|\,\gamma(E(x))\,\eta(E(x))}{g_{\alpha}(E(x))}=\phi _{\alpha}(E(x)). \tag{33}\]  
_If \(Re(\alpha)>1/2\), we use Lemma 3 with \(y=\xi\) and \(\lambda=0\) to obtain the inequality_  
\[|A_{i}|=\prod_{j\
eq i}\left|\frac{x_{i}-\xi_{j}}{x_{i}-x_{j}}\right|\geq(1-E (x))^{n-1}\]  
_which, together with the above used upper estimates of \(|A_{i}|\) and \(|B_{i}|\), leads us to_  
\[g_{\alpha}(t) = |1-\alpha|-|\alpha|\,\gamma(t)\,(1+\eta(t)),\]0 \[g_{\alpha}(t) = |1-\alpha|-|\alpha|\,\gamma(t)\,(1+\eta(t)),\]1  
_This completes the proof of the lemma. _  
The following is our first main result in this paper. **Theorem 3**. : _Let \(f\in\mathbb{C}[z]\) be a polynomial of degree \(n\geq 2\) with only simple zeros and \(\xi\in\mathbb{C}^{n}\) be a root-vector of \(f\). Suppose that \(x^{(0)}\in\mathbb{C}^{n}\) is an initial approximation satisfying_  
\[g_{\alpha}(t) = |1-\alpha|-|\alpha|\,\gamma(t)\,(1+\eta(t)),\]2  
_where the function \(\phi_{\alpha}\) is defined by (28). Then, the iteration (10) is well defined and \(Q\)-cubically convergent to \(\xi\) with the following error estimates for all \(k\geq 0\):_  
\[g_{\alpha}(t) = |1-\alpha|-|\alpha|\,\gamma(t)\,(1+\eta(t)),\]3_where \(\lambda=\phi(E(x^{0}))\). In addition, we have the following estimate of the asymptotic error constant:_  
\[\limsup_{k\to\infty}\frac{\|x^{(k+1)}-\xi\|_{p}}{\|x^{(k)}-\xi\|_{p}^{3}}\leq \left\{ \begin{array}{ll}\frac{a\left(|1-\alpha|+|\alpha|\right)}{ \left(|1-\alpha|-|\alpha|\right)\delta(\xi)^{2}}&\mbox{for }\ Re(\alpha)<1/2,\\ \frac{a}{\delta(\xi)^{2}}&\mbox{for }\ Re(\alpha)=1/2,\\ \frac{a\left(|1-\alpha|+|\alpha|\right)}{\left(|\alpha|-|1- \alpha|\right)\delta(\xi)^{2}}&\mbox{for }\ Re(\alpha)>1/2.\end{array} \right. \tag{37}\]  
Proof. : The proof straightforwardly follows from Lemma 5 and Theorem 1.
# The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review
## 4 Critical Applications
### Asset Management
#### 4.1.3 Extending Asset Life and Optimizing Replacement Strategies  
The strategic use of Digital Twins can significantly extend the life of smart grid assets. By continuously monitoring the assets' health and predicting future performance, Digital Twins help in making informed decisions about maintenance, upgrades, and replacements. This not only extends the assets' operational life but also optimizes replacement strategies by ensuring that assets are only replaced when necessary, thus reducing costs and resource consumption.
## 2 Composition, Availability, and Technological and Safety Constraints of Coffee By-Products
### Parchment  
The coffee parchment is the yellowish fibrous endocarp covering and dividing the two hemispheres of the coffee seed. The coffee parchment is produced at 39 kg per 100 kg of fresh coffee cherries [51]. In the wet processing, the coffee parchment is separated from other by-products after the beans have been dried and dehulled (Figure 5). This process allows for the collection of the parchment and its use separately from other by-products. In contrast, in the dry process, the coffee parchment is removed from the green bean, along with the skin, pulp, and mucilage, and is part of the coffee husk.  
Figure 4: Coffee pulp (wet coffee husk) and de-pulped coffee cherry showing the mucilage covering the coffee bean and partially removed.  
Figure 5: Coffee parchment flakes as obtained by the wet method.  
In terms of composition, the coffee parchment primarily consists of a high proportion of carbohydrates (~56%), predominantly in the form of dietary fiber. The main components of the parchment's dietary fiber are cellulose (40-49%), hemicellulose (25-32%), and lignin (33-35%) [26; 34; 55; 56; 57]. It also contains low levels of fat (~0.3%), proteins (~3%), minerals (0.5-1%), and a variable moisture content (~8-10%). Caffe and total phenolic compounds may account for approximately 1% and 2%, respectively [36; 58].  
As it represents 6% of the cherries' dry weight, it is produced at a rate of approximately 39 kg per 100 kg of fresh coffee cherries [51]. Therefore, it is an abundant by-product of coffee processing. Technological constraints associated with the coffee parchment include its fibrous nature, which can make it challenging to process and incorporate into various applications [59; 60]. Further research and development efforts are needed to explore efficient and cost-effective methods for the extraction, purification, and utilization of the valuable components present in the coffee parchment [61]. This will help maximize its potential as a sustainable resource and enhance its value as a functional food ingredient [35].  
The coffee parchment is generally considered safe for consumption, with studies confirming the absence of pesticides and mycotoxins, except for a minimal content of ochratoxin A (OTA) below the European Commission limit [14; 62]. A toxicity analysis was performed to complete the safety assessment, showing no adverse effects or mortality following the administration of 2 g/kg body weight of parchment to rats [14]. However, it is important to note that the coffee parchment, like other coffee by-products, should undergo rigorous quality control measures to ensure safety and minimize potential contaminants. Regular testing for pesticide residues, mycotoxins, and other contaminants should be conducted to ensure compliance with food safety regulations [63]. This is especially important as the coffee parchment is often used as a raw material to produce functional food ingredients and nutraceuticalals, where the safety and purity of the starting material are crucial for the final product quality.
# A Dual-Polarized and Broadband Multiple-Antenna System for 5G Cellular Communications
###### Abstract  
This study presents a new multiple-input multiple-output (MIMO) antenna array system designed for sub-6 GHz fifth generation (5G) cellular applications. The design features eight compact trapezoid slot elements with L-shaped CPW (Coplanar Waveguide) feedlines, providing broad bandwidth and radiation/polarization diversity. The antenna elements are compact in size and function within the frequency spectrum spanning from 3.2 to 6 GHz. They have been strategically positioned at the peripheral corners of the smartphone mainboard, resulting in a compact overall footprint of \(75\) mm \(\times\)\(150\) mm FR4. Within this design framework, there are four pairs of antennas, each aligned to offer both horizontal and vertical polarization options. In addition, despite the absence of decoupling structures, the adjacent elements in the array exhibit high isolation. The array demonstrates a good bandwidth of \(2800\) MHz, essential for 5G applications requiring high data rates and reliable connectivity, high radiation efficiency, and dual-polarized/full-coverage radiation. Furthermore, it achieves low ECC (Envelope Correlation Coefficient) and TARC (Total Active Reflection Coefficient) values, measuring better than \(0.005\) and \(-20\) dB, respectively. With its compact and planar configuration, quite broad bandwidth, acceptable SAR (Specific Absorption Rate) and excellent radiation characteristics, this suggested MIMO antenna array design shows good promise for integration into 5G hand-portable devices. Furthermore, a compact phased-array millimeter-wave (mmWave) antenna with broad bandwidth is introduced as a proof of concept for higher frequency antenna integration. This design underscores the potential to support future 5G and 6G applications, enabling advanced connectivity in smartphones.  
5G; broadband communications; CPW-fed slot antennas; dual-polarized antenna; mmWave phased arrays; smartphone applications +
Footnote †: journal: Sensors