esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To suppress the broadband noise, we design a three-stage filtering system to compress the noise bandwith to 11.7 MHz, which is slightly larger than the natural line width 6 MHz. In our filtering scheme, a band-pass filter and a fiber Bragg grating (FBG) are used to limit the noise range to 1 nm. Then, the noise bandwidth is further narrowed to 11.7 MHz by combining two cascaded etalons with different lengths and a confocal Fabry-Perot (F-P) cavity with a finesse of 28.7. The cascaded etalons and F-P cavity reduces the noise level by three orders of magnitude. As far as we know, this bandwidth of filtering system is the narrowest in experimental reports of frequency conversion. The optical components included in our filtering system are easy to use, which can be widely used in frequency conversion process. Based on the strong noise suppression, we show that the SNR of converted field is good enough to reduce the input photon number below 1 under the condition of low external device conversion efficiency (0.51%) and large duration of input pulse (250 ns). This work is a meaningful attempt to realize the frequency conversion interface between quantum memory at \({}^{87}\)Rb D1 line and communication band, which enriches the toolbox of fiber quantum networks.
## 4 Discussion
There is some evidence that this is leading to a change in the phenology of the phytoplankton with a shift from spring to summer maxima [61]. Summer is the period with maximum growth and abundance for macrophytes, so such changes to the light climate during this period can be expected to negatively affect the macrophytes. There is also an increase in intensity of storms predicted with climate change [99; 100], and this will likely alter nutrient cycling as well as increase physical exposure pressure and, if accompanied by significant rainfall, increase the lake level increasing light attenuation. The higher level of nutrients in the hypolimnion also represents a threat in the event of an overturn event if a cold windy winter occurs. The amount of nutrients released could lead to bloom events, less macrophytes, and less foraging for herbivorous birds. While the number of tourists did not appear to be a significant factor in the analysis of the whole study area, it is likely to be a hidden significant driver, perhaps less through the addition of nutrient load within the catchment [47] than through increased boating pressure around the peninsula as revealed by examining specific transects subject to boating traffic, as discussed above. In this context, the observed drop in tourist numbers by 56% in 2020 owing to the coronavirus epidemic may have reduced such pressure and contributed to the recovery in macrophytes observed. Similar results emerged in [PERSON] et al., 2020 [101], where the impact of lockdown on the Venice lagoon was studied. In detail, the drastic decrease in boats led to a reduction in erosion and particle resuspension, consequently promoting an increase in water transparency (a key factor for macrophyte growth). Adequately understanding and describing the contribution macrophytes make to lake food webs has long remained a challenge to limnology, not least because of the large diversity of macrophytes of various growth forms and stages as well as grazing being carried out by multiple taxonomic groups [92]. This study demonstrated the dynamic seasonal and interannual variation in macrophytes, attributable also to physical and chemical factors as well as grazing, but more importantly demonstrated that such data can be adequately extracted from freely available satellite archives. Further research could extend this approach to cover larger areas to obtain holistic estimates for lakes, and, if coupled with additional data on fish and macroinvertebrates, it could significantly advance knowledge on structural and functional relationships essential for conservation of such internationally important sites.
## 2 Methodology
### Laboratory Model Pile Test  
Here, we describe the laboratory model pile test conducted by the author to validate the numerical analysis results using DEM analysis. Ottawa F-55 sand was used as the test soil, which was prepared by dry pluviation to achieve 80% relative density in a cube-shaped soil chamber. The Unified Soil Classification System (USCS) classifies Ottawa F-55 silica sand as SP: poorly graded sand. More details on the test material are summarized in Table 1. In addition, Figure 1 also illustrates the particle size distribution curve of the test material and the granular material in PFC.  
The open-ended model pile used in the experiment had an external diameter of 88.9 mm and an internal diameter of 49.3 mm. It was installed using manual hammering with an 8 kg hammer dropped from a height of 800 mm, penetrating to a depth of 670 mm. Measurements were taken of both the penetration depth and the soil plug length during the impact-driving of 96 blows in total. Through this laboratory model pile test, we evaluated the drivability and load-carrying capacity of the OEP. The double-wall system was utilized to isolate and assess the internal shaft friction on the inner circumferential surface induced  
\begin{table}
\begin{tabular}{c c} \hline
**Soil Properties** & **Values/Classification** \\ \hline \(D_{10}\) (mm) & 0.157 \\ \hline \(D_{30}\) (mm) & 0.211 \\ \hline \(D_{50}\) (mm) & 0.256 \\ \hline \(D_{60}\) (mm) & 0.276 \\ \hline Coefficient of uniformity, \(C_{t}\) & 1.76 \\ \hline Coefficient of curvature, \(C_{c}\) & 1.03 \\ \hline \(e_{min}\) and \(e_{max}\)[36] & 0.47 and 0.78 \\ \hline Peak friction angle [37] (\(D_{r}\) of 80–90%), \(\phi_{p}\) (degree) & 38 \\ \hline Critical-state friction angle, \(\phi_{c}\) (degree) & 32.8 \\ \hline \(G_{s}\) & 2.65 \\ \hline Particle shape & Subangular to subrounded \\ \hline Unified Soil Classification System & Poorly graded sand (SP) \\ \hline \end{tabular}
\end{table}
Table 1: Soil properties of the test material (modified from [PERSON] et al. [32]).  
by the soil plug. This enabled a study of how the soil plug affects the pile's drivability and bearing capacity. However, experiments are time-consuming and labor-intensive, which limit the ability to explore various conditions. Therefore, it is advantageous to use numerical methods for additional parametric studies.
# Advancement of the DRPE Encryption Algorithm for Phase CGHs by Random Pixel Shuffling
## 4 Results Discussion and Performance Analysis
### Sensitivity vs. Robustness of Information  
The very nature of CGHs makes them highly robust to attacks that aim to partially modify the stored information. In other words, small variations in the 3D scene (understood as plaintext) do not significantly disturb the result stored in the CGH. Likewise, small modifications in the CGH do not lead to the loss of substantial information.  
Processes that partially modify the number of shuffled pixels in which their phases have been altered can provide insights into how the CGH behaves under progressively larger disturbances.  
A method to quantify this statement is by using the Pearson Correlation Coefficient to measure the similarity between images. In Figure 7a, this coefficient is shown as a function of the percentage of shuffled pixels, pixels with modified phase, and pixels where both phase and position are modified, between the original reconstructed image (Figure 5a) and those obtained with different percentages. Only when all pixels are shuffled is the absence of correlation guaranteed. For the calculation of the Correlation Coefficient (CC) shown in Figure 7a, the reference image is Figure 5a. In the case of 0% shuffled pixels, it is compared with itself (\(CC=1\)). This value decreases, reaching (\(CC=0\)) when 100% of the pixels are modified (Figure 7a).  
Since all points in the scene contribute to all pixels in the CGH, a nearly complete shuffle is necessary to completely hide the recorded information. The same is true for an encryption process where only the phase mask is used to hide the information.
# Analytical Design of Fractional-Order PI Controller for Parallel Cascade Control Systems
## 2 Materials and Methods
### Design of Secondary Controller Based on IMC Approach for Disturbance Rejection  
Figure 3 shows the block diagram of the secondary control design based on the IMC approach where \(G_{p_{2}}(s)\) and \(\widetilde{G}_{p_{2}}(s)\) are the process and the process model respectively; \(G_{c_{2}}(s)\) is the IMC controller. Note that, in this work, the IMC controller is placed on the feedback loop, which is different from the well-known IMC approach. As a result, the role of the controller is to solve the regulator problem in the secondary loop.  
For the nominal case (\(G_{p_{2}}(s)=\widetilde{G}_{p_{2}}(s)\)), the set-point and disturbance responses in Figure 3 can be simplified as:  
\[y_{2}=G_{p_{2}}r_{2}+(1-G_{c_{2}}\widetilde{G}_{p_{2}})G_{d_{2}}d \tag{19}\]  
As mentioned above, the secondary transfer function \(G_{p_{2}}\) is stable in this study. Therefore, from Equation (19), it is obvious that the controller \(G_{c_{2}}\) is only used for disturbance rejection. The IMC approach [27] is adopted to design \(G_{c_{2}}\). The process model \(\widetilde{G}_{p_{2}}(s)\) is factored into two parts:  
\[\widetilde{G}_{p_{2}}(s)=p_{2m}p_{2a} \tag{20}\]  
where \(p_{2a}\) is the portion of the model which includes the dead time and/or right half plane zeros and \(p_{2a}(0)=1\); \(p_{2m}\) is the remaining portion of the secondary process. Therefore, we have:  
\[p_{2a}=e^{-\theta_{2}s};\;p_{2m}=\frac{K_{2}}{\tau_{2}s+1} \tag{21}\]  
To obtain a good disturbance rejection for the process \(G_{d_{2}}\) which has poles near zero at \(z_{d1},z_{d2},\ldots,z_{dm}\), then \((1-G_{c_{2}}\widetilde{G}_{p_{2}})\) should have zeros at \(z_{d1},z_{d2},\ldots,z_{dm}\). According to the IMC approach, the IMC controller is given as follows:  
\[G_{c_{2}}=p_{2m}{}^{-1}f=\frac{\tau_{2}s+1}{K_{2}}f \tag{22}\]  
where \(f\) is considered as the IMC filter whose transfer function is given in this case as follows:  
\[f=\frac{1}{\lambda_{2}s+1} \tag{23}\]  
Figure 3: Block diagram of the secondary process-based IMC structure.  
Then the controller is obtained:  
\[G_{c_{2}}=\frac{\tau_{2}s+1}{K_{2}(\lambda_{2}s+1)} \tag{24}\]
# Internet of Things (IoT) Cybersecurity: Literature Review and IoT Cyber Risk Management
## 3 The Four-Layer IoT Cyber Risk Management Framework
### IoT Cyber Risk Assessment Layer
#### 3.3.4 Multiple IoT Asset-Vulnerabilities-Threats with Constraints  
Figure 3 shows that the total cost decreases up to the defense probability of 0.99 for IoT asset-vulnerabilities-threat-1. Since the investment is subject to many financial and operational constraints and limited organizational resources, one challenging question is how an organization should allocate limited resources to multiple IoT cybersecurity projects. To make decisions further complicated, each organization is in a different stage of the NIST's four Implementation Tiers, and each IoT asset-vulnerabilities-threat is likely to be subject to different operational and financial constraints.  
To minimize the total cyber cost of the entire IoT asset-vulnerabilities-threats under these constraints, this paper proposes a linear programming (LP) model with financial and operational constraints. The LP model has three major components: decision variables, which represent the defense probabilities of IT asset-vulnerabilities-threats; an objective function, which is a mathematical function of the decision variables for the minimization of the total cyber cost; and constraints, which are a set of functional equalities or inequalities that represent financial, technological, and operational restrictions on what numerical values can be assigned to the decision variables. All formulas for the objective function and constraints are in a linear form. The algorithmic discussion of the LP model is outside the scope of this paper, and readers interested in the details can refer to [PERSON], [PERSON], and [PERSON] [80].  
Constraints ensure the defense probability of each IoT asset-vulnerabilities-threat to have an upper limit. For example, they may believe the organization does not have the staffing or experience necessary to develop a security solution with a high defense probability. A lack of experience or personnel may also force the organization not to adopt a highly complicated technology. All of these constraints will affect the optimality of the defense probabilities and investment costs.
## 3 System Model of HAP Drones and LEO Satellite Collaborative Networks  
In this section, we consider the HAP drones and LEO satellite collaborative network, as shown in Figure 1. In this network, there are \(M\) LEO satellites flying at an altitude of 200 km [10]. They carry edge servers to provide computing services to terrestrial IoT devices. In the stratosphere, 20 km above the ground, \(N\) HAP drones are hovering. They also carry edge servers, so they can also provide computing services to terrestrial IoT devices.  
We denote the collection of LEO satellites as \(\mathcal{M}=\{1,2,\ldots,M\}\). For LEO \(m\), its computing capability can be denoted as \(F_{m}\). The set of HAP drones can be labeled as \(\mathcal{N}=\{1,2,\ldots,N\}\), and the maximum computing capacity of HAP drone \(n\) can be denoted as \(F_{n}\). There are \(J\) terrestrial IoT devices directly connected to different HAP drones that can offload their tasks to HAP drones. In each time slot, the input task size of IoT \(j\) can be modeled as \(D_{j}\), the computing density can be labeled as \(c_{j}\), and the maximum tolerable delay of this task is \(T_{j}\).
## 5 Performance Analysis
### Average Idle and Collision Probabilities  
Figure 13a-c show probabilities of successful transmission, idle channel, and collided transmission, respectively. The probability of successful transmission increased for a while until the number of stations reached 50, then decreased. This is because the probability that just one transmission occurred, \(P_{s}\), was sufficiently high; moreover, the probability that one or more transmissions were tried, \(P_{tr}\), was very low in the case of MLA-S. Further, we can confirm that the aggressive manner of accessing the channel led to higher successful transmission probability in MLA-S when the number of stations in the network was small.  
We can see the probability that the channel was idle in Figure 13b. With similar logic, the probability of MLA-S tended to be lower than those of the other schemes since the channel of MLA-S was over-densified. In conclusion, that behavior also means that the transmissions in MLA-S were very likely to collide, especially when the number of stations was large, as shown in Figure 13c.  
Moreover, we can see that misalignment of transmissions over multi-link led to a severe lack of transmission opportunity because asynchronous transmission occurred regardless of whether the other link occupied the wireless medium asynchronous transmission on non-STR, and thus, most of the air time was idle, as shown in Figure 13b.  
To sum up, considering Figures 10, 11 and 13 comprehensively, if the asynchronous channel access was used in the non-STR case without RTS/CTS exchange, we can see that its throughput underperformed the other schemes considering synchronous transmission because the links suffered almost doubled channel busyness for the non-STR case and the links hardly had the opportunity to access the channel. These results show the importance of synchronous transmission, considering non-STR situations, especially when the RTS/CTS exchange was disabled. Meanwhile, when it comes to RTS/CTS enabled, MLA-S had the lowest throughput among the multi-link schemes. With RTS/CTS exchange, collision events had less effect on airtime occupancy compared to when RTS/CTS exchange was not used, as shown in Equation (9). Since the asynchronous transmission on non-STR had relatively less collided channel probability, it could take advantage of RTS/CTS exchange; it still performed relatively low throughput compared to the others. In MLA-S with RTS/CTS exchange, since the MLDs' throughput sharply decreased according to the number of stations, the sum of throughput also steeply went down.  
Figure 13: (**a**) Successful transmission probability, (**b**) idle channel probability, and (**c**) collided transmission probability.
## 4 Detection Method-Based Classification of Anomaly Detection Models in WSNs
### Clustering-Based Anomaly Detection Models for WSNs
Furthermore, the proposed approach is different from the standard approach of [56] in that it does not rely on a threshold that depends on a certain percentage of data measurments to be included in the ellipsoid. Instead, a suitable covariance matrix is derived based on the distance between data points. The problem of this approach is the high computational complexity that results from the data transformation and distance calculations to find the radius. This complexity affects the suitability of the approach for real time applications. _[PERSON] et al._[54, 69, 74]: the authors in [69] used ellipsoidal clustering for anomaly detection in heterogeneous WSNs in which more than one type of data were collected. The difference of this approach from the ellipsoidal clustering approach of [71] was the ability to deal with heterogeneous applications that have different data distributions at the same time. The procedure of detecting elliptical anomalies in this approach is depicted in Figure 6 and can be summarized as follows:  
* At each sensor node, the local ellipse is calculated from the data measurements collected by that node for specific period of time. The ellipse parameters are then sent to base station. * At the base station, the similarity between each pair of ellipses received from all nodes is calculated and the number of clusters for each ellipse is estimated. * The ellipses are then clustered into a set of merged clusters and the parameters of merged ellipses are transmitted to each sensor node. * At each sensor node, the merged ellipses are utilized to detect global anomalies by flagging any measurement that fall outside the merged ellipses as anomalous. In [54, 74], incremental versions of the ellipsoidal clustering models of [69] were proposed to cope with the dynamic changes of the monitored environments. It was claimed that these incremental models can successfully detect anomalies in an effective manner with high detection accuracy. The use of clustering models helps in reducing the communication cost by clustering the data locally. Besides, there is no need to have prior knowledge of data since clustering is fully unsupervised. Three main drawbacks are identified and need more consideration when using clustering techniques for anomaly detection in WSNs, which are:Dependency on the choice of cluster width in some clustering techniques makes them not suitable for WSN applications. * Clustering is very computationally expensive with multivariate data because the calculation of the distance measures among all data patterns has high computational cost that make them unsuitable for limited resource devices such as sensors. * Clustering techniques cannot cope with continuous changes of data streams over time so the normal reference model will be out of date by the time they are used. Although, some recent clustering-based models [54,74] have tackled this issue via incremental learning methods, the computational cost for such methods is too high to be affordable by constrained resource devices.
## 6 Challenges and Future Research Directions
### Challenges
#### 6.1.2 Complexity in Algorithm Design  
The joint optimization of spectrum efficiency (SE) and energy efficiency (EE) in SWIPT systems requires sophisticated algorithms that can adapt to varying channel conditions and user demands. The complexity of designing such algorithms is further compounded in mMIMO-NOMA systems due to the large number of antennas and users involved. This increases the computational burden and can lead to latency issues, which are critical in real-time applications.
# Mg,Sr-Cosubstituted Hydroxapatite with Improved Structural Properties
## 4 Conclusions  
MgSr-HAs have the potential to exert beneficial biological effects by releasing Mg ions in situ. Although MgSr-HA can be thermally consolidated, the maximum firing temperature of Mg-containing HAs is lower than what is used with apatite scaffolds to avoid the formation of secondary phases, and this limits their mechanical properties, and thus, their use.  
Mg ion exchange on high temperature-sintered Sr-HA was exploited to prepare MgSr-HA porous scaffolds that mimicked the architecture of cancellous bone and with improved mechanical properties. This process can be applied on sintered Sr-HA materials irrespective of their 3D architecture and, based on our data, without significantly altering their compressive strength.  
These improved structural properties are coupled with a chemical composition that, due to Mg and Sr incorporations, has the potential to stimulate bone metabolism and formation.  
The present study, therefore, constitutes a proof of concept for viable MgSr-HA bone substitutes with characteristics that better meet clinical requirements in orthopedics and dentistry. Future studies will investigate the cellular effects of this material for further characterization of its biological properties.  
Conceptualization, E.L. and E.P.; methodology, E.L.; validation, E.L., S.G., and C.G.; formal analysis, E.P.; investigation, E.L. and E.P.; resources, S.G.; data curation, E.L.; writing--original draft preparation, E.L.; writing--review and editing, C.G.; visualization, C.G.; supervision, S.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
This paper is published in memory of [PERSON], senior researcher at ISTEC, crystallographer of great experience, who greatly contributed to this work.  
The authors declare no conflict of interest.  
Figure 6: Compressive stress–strain curves of Sr-HA foams (45%vol of porosity) before and after Mg ion exchange, showing the proposed process is not substantially affecting the mechanical property.
# A Review of Requirements for Gamma Radiation Detection in Space Using CubeSats
## 4 Operating Scenarios
A viable solution concerns the ability to reconfigure the payload from ground, by uploading a suitable scenario that copes with the orbital conditions. This action requires a solid review process and debugging procedure: to mitigate the failure risk, each new scenario should be beforehand validated on the engineering model (equivalent to the flight one) and, once commissioned, uploaded from the ground station to the satellite.