esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 3 5G Coverage Enhancement Techniques
### Carrier Aggregation
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author \& Ref. No.** & **Approach** & **Advantages** & **Limitations** \\ \hline \multirow{6}{*}{[PERSON], [PERSON] et al. [65]} & Theoretically analysed the high and low frequency CC aggregations: 2.5 GHz and 3.5 GHz frequency bands are aggregated to get enhanced coverage capacity. & The UL and DL coverage capacity was enhanced inside and deep inside the buildings. & This method can be extended to mmWave frequency bands. \\ \hline \multirow{6}{*}{[PERSON], [PERSON] et al. [66]} & Contiguous and non-contiguous CA methods are used in LTE-A systems that are compatible with the LTE systems to achieve the high data rates. & Achieves high data rates, better coverage, and wider bandwidths & LTE devices which will not support the LTE-A will use only 20 MHz carriers instead of 100 MHz carriers. \\ \hline \multirow{6}{*}{[PERSON], [PERSON] et al. [67]} & The impact of spectrum aggregation (SA) on LTE networks is measured. The performance of intra band SA and contiguous CA in LTE are compared. & The spectrum aggregation of LTE system provides better system performance compared to individual carrier systems. & Lower frequency bands are aggregated for a limited cell size. For an extended cell size, high frequency components need to be aggregate. \\ \hline \multirow{6}{*}{[PERSON] et al. [68]} & Intra and inter band CA techniques are analysed and the energy efficiency of single and multi-flow based relay assisted CA systems are proposed to estimate the ergodic rate of users. & The relays boost the coverage capacity of the lowerCCs and provide a better capacity fairness between the users. & There is further need to improve the capacity fairness between the users. \\ \hline \multirow{6}{*}{[PERSON] et al. [69]} & Two LC based lumped \(\pi\) networks and TL based phase shifters are used with the BAW duplexers. & Throughput and overall system performance are improved. & Interference free frequency multiplexing schemes are to be used to improve the CA performance \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of carrier aggregation-based approaches of 5G networks. * Implementation Issues: The hardware implementation of CA is very critical, as it requires oscillators, radio frequency chains, signal processing techniques, a strong battery life, etc. [70].
# A Review of Mid-Infrared Supercontinuum Generation in Chalcogenide Glass Fibers
## 3 MIR SC Generation in ChG Fibers
### SC Generation in ChG MOF Fibers
Therefore, MOFs are more exposed to the external environment compared with step-index fibers. Indeed, it has already been shown in silica MOFs that exposure to air impacts fiber transmission by an increase of the OH\({}^{-}\) absorption band at 1.4 \(\upmu\)m [145]. SC generation was demonstrated by Mouawad et al. [85] in a 25-mm-long, 7-month aged counterpart As\({}_{2}\)S\({}_{3}\) SCF with a ZDW of 2.65 \(\upmu\)m. A multistage OPA was used as the pump source, which delivered 300 fs pulses with a repetition rate of 43 MHz. When the average pump power was fixed at 190 mW with different wavelengths, the SC spectral bandwidth covered less than 1200 nm, which was obviously narrower than that of the fresh fiber which spanned almost 2000 nm (Figure 8), confirming that the typical nonlinear broadening process in the MOF can be influenced by deleterious aging phenomena. Extra losses induced by the exposure period can reduce the transmission efficiency in aged MOF, and a certain beam power is inevitably consumed during propagation [84; 146; 147]. Figure 7: (**a**) Cross-section of a ChG SCF with three bridges; d is the fiber core diameter; w is the bridge width. (**b**) Dispersion profiles versus the wavelength for suspended-core fibers (SCF) with a bridge width of 0.35 \(\upmu\)m and different values of d; and (**c**) dispersion profiles versus the wavelength for SCF with a fiber core diameter of 0.5 \(\upmu\)m and different values of w.  
From the above discussions, it is clear that ChG MOFs have the advantages of a wide dispersion adjustable range and a large \(\gamma\), but the generated SC spectra are not as wide as those from step-index fibers due to the low power coupling efficiency. In addition, the impact of optical aging in MOFs on SC generation needs to be considered for long-term applications in non-controlled atmospheric conditions [146].
## 2 Designing Nominal SAR Operational Parameters at Nominal Altitude
### Mission Requirement Definition  
Table 1 shows the mission requirements including the satellite altitude calculated using orbit repetition and eccentricity. The altitude variation is about 30 km. The required ground swath width is 5 km for the sliding spotlight mode. The noise equivalent sigma zero (NESZ), range ambiguity ratio (RAR) and azimuth ambiguity ratio (AAR) are required to be less than or equal to -20 dB, respectively, within incidence angle range between 20 and 55\({}^{\circ}\).
## 4 Experimental Results and Analysis
### Comparative Experiments with PNSP Algorithm  
In order to verify the effectiveness of the PNSP algorithm, this experiment compares the overall recognition effectiveness of the CSCL-Siamese network for known IoT devices with randomized strategy and PNSP algorithm generating positive and negative pairs of samples on the Aalto dataset, UNSW dataset, and LSIF dataset, respectively. Considering that there is an imbalance in the sample set of positive and negative pairs, this experiment uses the Accuracy rate and G-mean value as the main evaluation index. The experimental results are shown in Figure 11.  
As can be seen from Figure 11, the overall performance of the PNSP algorithm outperforms the randomized strategy on all three different scenario datasets, especially in terms of accuracy, as follows: First, the accuracy of the PNSP algorithm improves by 18.8% on the Aalto dataset, 12.3% on the UNSW dataset, and 11.1% on the LSIF dataset, which suggests that the recognition ability based on the PNSP algorithm is able to classify the majority of the sample pairs more accurately even if there is an imbalance in the data; Second, the enhancement of the G-mean value of the PNSP algorithm is smaller than the enhancement of the Accuracy rate, which is because the G-mean value integrates the ability of correctly recognizing the positive pair of samples and the negative pair of samples, whereas the small number of positive pair of samples leads to a relatively low enhancement of the G-mean value; Finally, the overall recognition improvement of the PNSP algorithm is relatively large compared to the random strategy for generating positive and negative pairs of samples, thanks to the fact that the PNSP algorithm fully and comprehensively utilizes all the samples in the dataset for generating positive and negative pairs, thus proving the effectiveness of the PNSP algorithm.
## 4 Results and Discussion
### Primary Energy Analysis  
The target of this analysis is to find the configuration that optimizes the system performance. Sensitivity analysis is presented under a different number of collectors (areas) and solar tank sizing. The number of collectors, and storage size, in the base case is 12 (30 m\({}^{2}\)) and 50 L/m\({}^{2}\) (1500 L), respectively, compared with the base case. The sensitivity analysis includes changing the surface collector area from 25 m\({}^{2}\) to 35 m\({}^{2}\); the solar tank volume varied from 1000 l to 2000 l. The results are displayed in Table 6.  
It is shown that, with a greater collector area, the best results were obtained. A 16.6% increase in collector surface area is followed by an increase in the solar fraction and relative PE saved, 12.6% and 48.3%, respectively. Regarding solar tank volume, it is clearly seen that variation of tank volume does not present a significant influence on solar fraction, electrical efficiency, and PE relative; a solar tank volume increasing of 33.3% reflects on increasing in a solar fraction of about 2.5%, and PE relative around 10.3%. From the previous results, it can be recommended to use a collector area of 35 m\({}^{2}\) and a storage tank volume of 2000 L in order to achieve better performance than that reached in the base case.
# Computational Load Analysis of a Galileo OSNMA-Read Receiver for ARM-Based Embedded Platforms +
More in detail, with respect to [16], two new contributions justify this extended version: First of all, two further ARM-based platforms have been included in the platforms comparison, thereby allowing for a more widely assessment of the performance. Secondly, a specific analysis on the computational load of the complete OSNMA-ready receiver is discussed hereafter. More in detail, after this introduction, Section 2 recalls the main characteristics of the OSNMA authentication scheme, while Section 3 describes the eNGene software receiver, along with the update to the OSNMA-ready version. The results of the software profiling analysis of the OSNMA functions are summarized in Section 4, focusing on the comparison among platforms and considering different configurations of the OSNMA parameters. Such analysis is then extended to the main functions of the complete OSNMA-ready receiver in Section 5, where the processor's computational load is also evaluated. Section 6 draws the conclusions, sketching some ideas for future activities.
## 2 Auto-Configuration in Mobile _Ad Hoc_ Network  
The nodes of a network need some mechanism to interchange messages with each other. The TCP/IP protocol allows the different nodes from the network to communicate by associating a distinct IP address to each node of the same network. In wired or wireless networks with an infrastructure, there is a server or node which correctly assigns these IP addresses.  
Mobile _ad hoc_ networks, on the other hand, do not have such a centralized entity able to carry out this function. Therefore, some protocol that performs the network configuration in a dynamic and automatic way is necessary, which will utilize all the nodes of the network (or only part of them) as if they were servers which manage IP addresses.  
Due to the dynamic topology of mobile _ad hoc_ networks (constant movement of the nodes that can join and leave the network frequently and even simultaneously), auto-configuration protocols are faced with various problems in guaranteeing the uniqueness of IP addresses and in allowing network partitioning and merging.  
To guarantee the correct functioning of the network, the protocols strive to achieve the following objectives [2-4]:  
* _Assign unique IP addresses_: Ensure that two or more nodes do not obtain the same IP address.
* _Function correctly_: An IP address is only associated with a node for the time that it is kept in the network. When a node leaves the network, its IP address should then became available for association to another node.
* _Fix the problems derived from the loss of messages_: In case of any node failure or if message loss occurs, the protocol should operate quick enough to prevent two or more nodes from having the same IP address.
* _Allow multi-hop routing_: A node will not be configured with an IP address if there aren't any available in the whole network. Thus, if any node of the network has a free IP address, it has to associate itself with the node which is requesting an IP address, even though it is at two-hops of distance or more.
* _Minimize the additional packet traffic in the network_: The protocol must minimize the number of packets exchanged among the nodes in the auto-configuration process. In other words, control packets traffic must cause as little harm as possible to the data packet traffic, given that in the extreme case, the network performance would decrease.
* _Verify the existence of competing petitions for an IP address_: When two nodes request an IP address at the same time, the protocol must carry out the pertinent treatment so that the same IP address is not given to two nodes.
* _Be flexible to partitioning and merging of the mobile ad hoc network_: The protocol must be able to achieve the union of two different mobile _ad hoc_ networks as well as the possible partitioning into two networks.
* _Conduct synchronization_: The protocol must adapt itself to the rapid changes of the wireless network topology due to the frequent mobility of the nodes. The synchronization is carried out periodically to ensure the configuration of the network is as up to date as possible.
## 3 System Model and Problem Formulation
### System Model
#### 3.1.2 Delay Model  
In this paper, the delay is crucial for improving network performance. Specifically, the delayed response of the network depends on the switch-controller interaction and the inter-controller timing synchronization that updates the global view, the packet transmission delay, and the processing delay.  
The switch-controller interaction is represented as the average propagation delay between switch \(i\) and controller \(j\), which is calculated by Equation (1) through the shortest path length between them. In addition, the physical link length of the network is calculated by the geographical latitude and longitude positions of the nodes to combine with the actual environment. The shortest path length is the combination of the number of hops of the shortest path and the length of the single-hop physical link.  
\[D_{nceag}=\frac{1}{|N|\cdot v}\sum_{n_{i}\in N}\sum_{c_{j}\in\mathrm{C}}d_{ij} ^{nc}x_{ij}, \tag{1}\]  
Here, \(d_{ij}^{nc}\) represents the shortest transmission path length between switch \(i\) and controller \(j\), and \(v=2\times 10^{8}\) m/s represents the propagation rate of electrical signals in cables.  
The inter-controller timing update forwarding rule is expressed as the propagation delay of inter-controller synchronization. Similarly, the delay of synchronization is calculated by the shortest path length between controllers, expressed as Equation (2):  
\[D_{ccang}=\frac{\sum_{c_{i}c_{j}\in\mathrm{C}}d_{ij}^{cc}}{v}, \tag{2}\]  
where \(d_{ij}^{cc}\) represents the shortest transmission path length between controller \(i\) and controller \(j\), and \(v=2\times 10^{8}\) m/s represents the propagation rate of electrical signals in cables.  
In considering a real network scenario, the time for network traffic to be sent from the switch or controller is defined as the transmission delay, which is calculated by Equation (3). Each controller processes the packets received from the switch, and this phase is accompanied by processing delays related to the hardware devices. The packet processing in the controller is modeled by the M/M/1 queuing model [37], which is computed through Equation (4).  
\[D_{trans}=\frac{p_{send}}{B_{l}}, \tag{3}\]  
\[D_{proc}=\frac{1}{k}\sum_{c_{j}\in\mathbb{C}}\frac{1}{Ca-\sum_{s_{l}\in N_{cf_{ j}}\in C}\lambda_{i}x_{ij}}, \tag{4}\]  
Here, \(p_{send}\) is the packet size sent by the switch or controller, \(Ca\) is the average rate at which the controller processes packets, and \(\lambda_{i}\) is the average flow request rate for switch \(i\).  
Therefore, in SDN, the delayed response of the network is defined as the end-to-end delay, which is calculated by the round-trip time of the packets in the network [34; 36], represented by the following formula:  
\[D=2D_{trans}+D_{proc}+2D_{ncapg}+2D_{ccapg}, \tag{5}\]
## 3 Silicon Germanium Waveguides and Nonlinear Applications
### Wavelength Conversion of 16-QAM and 64-QAM Signals:
Hydex offers both relatively high nonlinearity and extremely low loss at the telecom wavelength region [24], however very long waveguides are typically required to obtain the desired conversion efficiency ( of the order of meters). Both a-Si and SiGe platforms offer remarkable nonlinear performance as shown in this manuscript, also offering the additional flexibility to tune the linear and nonlinear optical properties of the platform by changing the material deposition parameters. We believe that the two proposed platforms (a-Si and SiGe) are now sufficiently mature, facilitating the realization of reliable and efficient devices for the future all optical settings. Figure 10: BER curves and constellation diagrams for the: 16-QAM (Quadrature Amplitude Modulation) (**a**); and 64-QAM (**b**), before and after the wavelength conversion process. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Device** & **Signal Type** & \begin{tabular}{c} **Conversion** \\ **Efficiency (dB)** \\ \end{tabular} & \
\begin{tabular}{c} **3 dB Conversion** \\ **Bandwidth (nm)** \\ \end{tabular} & **OSNR 1 Penalty** &
\begin{tabular}{c} **CMOS 1** \\ **Compatible** \\ \end{tabular} \\ \hline a-Si (this work \\ reported in [26]) & 10 Gbaud DPSK 1 & –26 dB & –25 nm & \textless{}1 dB & Yes \\ \hline Si-Ge (this work \\ reported in [57]) & 10 Gbaud 64 QAM 1 & –9.7 dB & –30 nm & \textless{}1 dB & Yes \\ \hline c-Si [18] & 28 Gbaud 16 QAM & –17.5 dB & –30 nm & 4 dB at BER 1 = 10\({}^{-5}\) & Yes \\ \hline AlGaAs on SOI [47] & 10 Gbaud 256 QAM & –13 dB & –28 nm & 2.5 dB & No \\ \hline Hydex [58] & 10 Gbaud 16 QAM & –40 dB & –10 nm & 0.5 dB & Yes \\ \hline \hline \end{tabular}  
* OSNR: Optical Signal to Noise Ratio; CMOS: Complementary Metal Oxide Semiconductor; DPSK: Differential Phase Shift Keying; QAM: Quadrature Amplitude Modulation; BER: Bit Error Rate.  
\end{table} 
Table 5: Integrated wavelength converter technology comparison.
# Development of an Electronic Smart Safe Box Using Private Blockchain Technology
## 4 Results
### Hardware Tests
#### 4.1.4 GPS Test  
This test was performed to determine the ability of the smart box and whether it can send a warning to the administrator upon moving it without administrator permission. The proposed GPS must be able to send the data to the Arduino and the map to the app registered on each subject's device. The results are shown in the following Figure 6.  
As seen in Figure 6, the smart safe box starts sending its location to the registered users by moving it from one place to another. These results also reveal that the integrity of the smart safe box is compromised by moving it from its current location.
## 3 Structured Sparse Bayesian Approach for Channel Estimation
### Pilot Placement and Pattern Design  
The power of the pilot symbol in the embedded pilot channel estimate approach is frequently substantially larger than that of the data symbol, which is unrealizable in actual applications. Only inserting one pilot symbol will result in a high PARP issue [17]. Meanwhile, unlike [20], we have set up a protection interval symbol to avoid interference between data and pilot symbols. Here, we set the pilot symbol power to be the same as the data symbol power, transforming the channel estimation problem into a sparse channel recovery problem. This not only effectively solves the problem of high pilots power but also avoids interference between data that affects estimation results.  
We arrange the pilot and data symbols in the delay-Doppler grid for OTFS frame transmission, as shown in Figure 2.  
We select \(k\in[k_{p}-k_{\max},k_{p}-k_{\max}]\) and \(l\in[l_{p},l_{p}+l_{\tau}]\) as the pilot symbols. The other fields are guard symbols and data symbols.  
\[x[k,l]=\left\{\begin{array}{ll}x_{p}[k,l]&\quad k_{p}-k_{\max}\leq k\leq k_{p}- k_{\max}\\ &\quad l_{p}\leq l\leq l_{p}+l_{\tau}\\ \end{array}\right. \tag{12}\]  
Usually, \((k_{p},l_{p})\) are placed onto \((\frac{N}{2},\frac{M}{2})\). \(l_{\tau}\) is used to control the proportion of pilots in the whole transmission symbol. The final channel estimation region is set to \(k\in[k_{p}-k_{\max}-Q,k_{p}-k_{max}+Q]\) and \(l\in[l_{p},l_{p}+2l_{\tau}]\). In other words, the size of the channel estimation section \(S=(4k_{max}+2Q+1)(l_{max}+l_{\tau}+1)\), as shown in Figure 3.  
Figure 3: Rx symbols pattern.  
Figure 2: Tx symbols pattern.
## 3 The Main Sources of Bacterial Stress in the Intestinal Microbiota
### Stress Resulting from Microbial Interactions
#### 3.7.2 Stress by Bacteriophages and Microbial Predators  
Bacterial viruses (bacteriophages) have been postulated to be the most abundant microorganisms in the gut. However, most of these phages are probpages, or lysogenic phages that replicate with the host bacterial strain. The \"free\" (extracellular) phages, which are able to infect new organisms, are comparatively smaller in abundance but can locally increase in number and evolve into a bacteriolytic state when induced (activated) by stressful conditions [76]. The most abundant viral families include _Myoviridae_, _Podoviridae_, _Siphooviridae_, and _Microviridae_. Most bacterial stress produced by phage invasions derives from envelope (cytoplasmic membrane) stress, fostering a phage-shock-protein (Psp) system, occurring both in Gram-positive and Gram-negative microbes [77]. Classic mechanisms of resistance to phage invasions are alterations in bacterial surface epitopes acting as phage receptors and restriction-modification systems. There is also the production of proteins interfering with the phage infection cycle, and these include variable and evolving CRISPR sequences. There are also phenotypic mechanisms of resistance, which change the metabolic status of the cell and are similar to antibiotic persistence in bacteria [78]. Comparative stress by bacterial predators, such as protozoa, appears to have less importance. However, commensal protozoa, such as _Entamoeba_ or _Blastocystis_, eating bacteria, might be important for microbiome stability in low-income human populations, particularly in the proximal gut [79]. Bacterial resistance to protozoa is analogous to resistance to phagocytosis and survival in phagocytosis [80]. The classically described environmental predators, such as _Bedlovibrio_, can also be abundant in the gut. they penetrate the cell and multiply in the periplasm, killing the prey bacterium; the process is probably too rapid to produce a significant population of stressed bacteria [81].