esa-sceva/satcom-chunk-collection huggingface dataset:
# A Secure Key Aggregate Searchable Encryption with Multi Delegation in Cloud Data Sharing Service
## 4 Our Proposed Scheme
### Setup Phase  
For data sharing and upload data, a data owner \(DO\) have to generate bilinear map and public system parameters. \(DO\) also generates hash functions for encrypted information and bloom filter. The detailed steps of the setup phase are summarized in Figure 2 and discussed below.  
**Step 1:**\(DO\) generates a bilinear map \(B=(q,G_{1},G_{2},e)\), where \(q\) is the order of \(G_{1}\) and \(e:G_{1}\times G_{1}\to G_{2}\). \(G_{1}\) and \(G_{2}\) are multiplicative elliptic curve groups. Then \(DO\) picks random generator \(g\in G_{1}\) and random nonce \(\alpha\in Z_{q}\), and computes \(g_{i}=G^{(\alpha_{i})}\), where \(1\leq i\leq 2n\).  
**Step 2:** After that, \(DO\) chooses his/hear master secret key \(r_{do}\in Z_{q}^{*}\), secret key \(\rho_{do}\in Z_{q}^{*}\). \(DO\) generates hash functions \(h_{1}:\{0,1\}^{*}\to Z_{q}\) and \(h_{2}:\{0,1\}^{*}\to G_{1}\) for hashing information. Furthermore, \(DO\) also generates \(k\) independent universal hash functions \(\{H_{1}^{\prime},\ \ldots,\ H_{k}^{\prime}\}\) which are used to set up a \(m\)-bit bloom filter.  
**Step 3:** Then, \(DO\) computes public key \(PK_{do}=g^{\prime_{do}}\) for encrypting data and public key \(DPK_{do}=g^{\prime_{do}}\) for authentication. At last, \(DO\) publishes \(B,(g,g_{1},\ \ldots,\ g_{n}),DPK_{do}\), \(PK_{do},h_{1},h_{2}\) and \(\{H_{1}^{\prime},\ \ldots,\ H_{k}^{\prime}\}\).
## 3 Comparison of the Experiments with Analytical Models and Discussion of the Results
### Calculation for Pure Torsion and Combination of Torsion and Bending
was again made between the field section and the restraint, resulting in corner bars with an equivalent area of 7 \(\mathcal{O}\)8 mm + 2 \(\mathcal{O}\)12 mm in the field section and 2.5 \(\mathcal{O}\)8 mm + 2 \(\mathcal{O}\)12 mm (=7 \(\mathcal{O}\)8 mm) for the restraint. This area had to be reduced by the area required to take on the bending moment. For this purpose, the lever arm was selected as described in [16; 17; 20] and defined as the distance between the chord lines (i.e., at 1.20 m). The weakest side of a cross-section is decisive for failure [16; 17; 20]. In the case shown here, this concerns the deck slab, which is under tensile stress due to torsion and bending. In order to continue using Equations (1)-(3), the corner bars in the bottom slab--which are actually under compressive stress as a result of bending--had to be reduced by the same amount. Alternatively, Equations (1)-(3) could also be adapted to determine the torsional resistance of each individual slab, which would ultimately lead to the same result. The first approach was chosen, and the reduced area \(A_{sl,red}\) was used to replace \(A_{sl}\) in Equations (1)-(3). The occurring bending moment to be considered--resulting from the cantilever system of the test setup (the dead weight of the box girder, the diaphragm at the load introduction point, the steel girders, and the presses)--differed for the two specimens. While the moment at the crack leading to failure was chosen for TS\({}_{\text{Ref}}\), the maximum bending moment at the restraint was used for TS\({}_{\text{joint}}\). The parameters for the calculation of the combined loading of torsion and bending resulting from the previously discussed assumptions are listed in Table 3. The values for \(A_{sw}\), \(s_{sw}\), \(u_{k}\), \(A_{k}\), \(f_{ys,s}\), and \(f_{ys,l}\) can be taken directly from Table 2 as no alteration were made. For TS\({}_{\text{joint}}\), the shear joint can be regarded as an upper boundary for the maximum torsional moment. The stress is determined by means of the first Bredt's formula added to the average shear stress resulting from the shear force (4):  
\[\tau_{wcb}=\frac{V_{experiment}}{2\cdot h_{wcb}\cdot b_{wcb}}+\frac{T_{experiment}}{ 2\cdot A_{k}\cdot b_{wcb}}. \tag{4}\]  
The calculated results for all variants under the load case torsion and bending are listed in Table 4 and are directly compared to the results obtained from the experimental tests. The shear stress \(\tau_{wcb}\) was evaluated at the restraint for all variants.
## 4 Conclusions  
The actual state of the art in the field of electrochemical sensors based on non-destructive methods for corrosion monitoring of RCS was reviewed. The most common causes of reinforcement corrosion are Cl\({}^{-}\) and carbonation by atmospheric CO\({}_{2}\). The corrosion monitoring of RCS can be performed using different sensors and methods and is a crucial part of planned maintenance and life prediction. The data obtained from corrosion monitoring provides quantitative information about the corrosion development of RCS exposed to aggressive environments. It can also be used to assess the effectiveness of repair and rehabilitation interventions by assessing the behavior of repairing systems such as coatings or corrosion inhibitors and to determine the future repair cycle.  
The use of a corrosion monitoring system is particularly economic in locations with difficult access, such as at the tidal zones of bridges and pier shafts. Furthermore, the success of a corrosion monitoring system relies on the fact that it must be considered as part of effective maintenance management for a RCS. A corrosion monitoring system cannot be the only measure for maintenance, but as an integrated measure of the whole maintenance program for a RCS.  
The studies revealed that the number of engineers and scientists, with proper education and experience in the field of corrosion of RCS, has grown considerably in the last two decades.  
Furthermore, over the past few years, the corrosion monitoring systems for new and existing RCS have achieved a high level of development. Improvements have been more evident in the development of durable, low-cost embeddable sensors and microprocessor control and communications. Furthermore, the studies published indicate that the measurements of corrosion potential or concrete resistivity are very useful techniques to characterize the corrosion state of a RCS. However, the corrosion process can only be quantified by \(i_{corr}\) measurements. The studies generally allow a conclusion that the RCSs exposed to aggressive environments have a considerable \(i_{corr}\) variation. Therefore, it is necessary, in the short term, to establish a methodology in order to define, with a high accuracy level, representative values of \(i_{corr}\) in certain environments.  
The author declares no conflict of interest.
## 5 Discussion  
The analysis of SAR data over the San Carlo-Mirabello area during the 2012 Emilia seismic sequence identified local ground movements. We argued that such displacements originated from at least two different sources: (i) a regional tectonic source, associated with postseismic fault afterslip and poroelastic stress changes [26; 30], and (ii) a shallow ground consolidation source, associated with the post-liquefaction progressive dissipation of excess pore water pressure developed in the saturated sandy layers during earthquake shaking.  
The assumed simple exponential decay for both phenomena (Figure 6) allowed us to discriminate between the two contributions to the observed ground displacements, thus providing a simple estimation of a median consolidation subsidence curve, with a maximum subsidence of approximately \(-2.1\) cm and a decay of roughly \(4\) to \(5\) months. The ground sinking followed a non-linear and exponential decay trend, which is typical of consolidation processes. It mainly occurred near the area that manifested the effect of liquefaction at the surface and had minimal impact on areas farther away.  
Calculating the consolidation subsidence from the available geotechnical database with simple analytical formulations provided a statistical distribution of post-liquefaction  
Figure 7: (**a**) Spatial distribution of the computed post-liquefaction vertical consolidation subsidence (colored circles). (**b**) Statistical distribution and boxplot of the computed post-liquefaction consolidation subsidence at available cone penetration tests over the study area. The dashed blue line identifies the asymptotic consolidation subsidence (approximately \(-2.1\) cm) determined from the analysis of InSAR data in Figure 6.  
ground subsidence, as shown in Figure 7b. The median subsidence estimated from InSAR data (\(\approx\)\(-\)2.1 cm) fell within the first and third percentile of the computed subsidence distribution, slightly larger than the computed median and mean consolidation values. This result supports the hypothesis that the observed subsidence could be attributed to post-liquefaction consolidation subsidence.
# Unravelling the Environmental Application of Biochar as Low-Cost Biosorbent: A Review
## 2 Adsorption Process  
The elimination of organic and inorganic pollutants from the aquatic ecosystem is essential due to the increase in the global population, estimated to reach around 9.3 billion by 2050 [13]. Among the different techniques that are being applied today for the rapid removal of organic and inorganic pollutants from wastewater, the adsorption process has increasingly gained attention as a valuable promissory technique. Adsorption is a well-known process, especially in the removal of pollutants from effluents containing moderate or low concentrations pollutants.  
Several materials can be used as adsorbents, and their proper selection is largely related to their feasible environmental applications. To consider a material as an ideal adsorbent, it is required to have many characteristics, such as cost-effectiveness, abundance, physical and chemical resistance, morphology and surface chemistry, high specific surface area, user-friendliness or being easily renewable [14]. Other important parameters are its hydrophobic-hydrophilic properties and the functional groups present in the adsorbent [15]. In recent studies, different strategies, such as the loading of metals, oxides or ions, physical and chemical modification and so forth, have been assessed in order to achieve higher adsorption capacity of the existing adsorbents by improving the above-mentioned properties [16].  
Figure 2: Biochar production methods.  
Numerous studies have demonstrated the applicability of biochar as a low-cost adsorbent, [17; 18], a fact confirmed by [PERSON] et al. [19] who estimated the cost for the production of biochar from feedstock as lower than 0.08 USD/kg, which is lower than 10% of the price of the commercial adsorbents. Generally, biochar is characterized by low apparent density (0.30 to 0.45 g/cm\({}^{3}\)), high surface area (200-400 m\({}^{2}\)/g) and high porosity (micro-, meso- and macropores) [20]. Several variables can be selected to ameliorate the biochar properties [21]. In general, as the temperature of the process increases, a greater percentage of gases is produced and the percentage of the solid fraction is reduced, with a similar effect on the surface functional groups of the biochar (C=O and C-H), which is also reduced [22]. Thus, it is necessary to understand better how to act on the different process variables in order to provide the adequate properties to the engineered biochar based on the characteristics of the effluent to treat and the operational scale request.
## 4 Discussion
With its visualization potential, AR also has a lot to offer as a training tool, having been proven to reduce the learning curve for trainees. Unanimity does not appear to have been reached regarding the drawbacks of these technologies due to most of them being improvable in the foreseeable future. Limitationsof extended reality technologies include visual fatigue, inaccuracy of these devices, and high costs. AI, a newly rising technology, achieved remarkable improvements in the medical field. Despite its almost flawless prediction accuracy in femur fracture classification cases [53] and general fracture detection [54], AI has been used the most for other purposes. Specifically, it has been used to grade the severity of lumbar spinal stenosis in epidemiological studies (31). These studies involved a considerable volume of highly consistent MRI spine data computed by the system thanks to its algorithm elaboration capabilities. Additionally, this technology has been used to predict the diagnosis and severity of cervical spondylotic myelopathy [27], further demonstrating its great accuracy. In the aforementioned studies, machine-learning, a subset of AI, was employed. This branch of computer science has the potential to revolutionize epidemiologic sciences [55]. The algorithms utilized by this technology can be used in the prediction, classification and clustering of several conditions. [PERSON] et al. [27] utilized deep learning, which is defined as a subfield of machine-learning, which employs neural networks (algorithms inspired by the function and structure of the human brain). Neural networks have gained attention in the radiology field, as they can analyze various areas of the human body with great accuracy [56]. Even though AI has the incredible potential to revolutionize orthopaedics, not enough literature discusses its applications in orthopaedic surgery. When applied to surgery, VR, AR and AI allow the surgeon to operate more consciously and safely. Physicians will work side-by-side with their machine counterparts and completely shift their focus towards more patient-centred health care, yielding unprecedented benefits. It appears clear that extended reality technologies are subject to continuous development, which will likely push them to become the object of a revolution in the medical field. The limitations of this paper were the high heterogeneity between studies and the lack of data such as sample size in four studies or mean follow up. Moreover, due to the high heterogeneity of the data, it was impossible to perform a meta-analysis. Only English and Italian articles were included, constituting a limitation in the search string. Lastly, the quality of evidence of the studies included was low; therefore, it was impossible to obtain significant conclusions.
### Orbital Precession Rate Change
Here, the quality and stability of the daytime imagery acquired by two EO-1 sensors are examined to determine the effect of orbital precession on data products with changing SZAs and footprints in areas with different surface characteristics. Multiple approaches were used in several locations with varying surface properties to highlight different aspects of the effects of EO-1's changing precession over time. Three vegetation sites and two Committee on Earth Observing Satellites (CEOS) Pseudo Invariant Calibration Sites (PICS) were selected to evaluate data quality and stability changes. Figure 3: Solar Zenith Angles (SZAs) at EO-1 overpass time showing the effect of changing seasons and latitudes on SZAs as overpass time drifts to earlier times (colored lines, the colors indicating the latitude of the observation as shown in the legend). The blue shaded region shows SZAs at various latitudes during nominal operations, compared to SZAs experienced in 2016 (orange shaded region).
## 3 Information Rate
### Multisine PSK Modulation  
Secondly, we introduce the mutisine PSK modulation scheme, which is the default modulation scheme considered in this work. In this scheme, the information is embedded in the phase \(\phi_{n}\) only. Specifically, to ensure the optimized waveform for wireless power transfer (WPT) [27], all tones have the same amplitude \(a=\sqrt{\frac{2P_{i}}{N_{t}}}\) and phase as \(\phi=\phi_{n}=\phi(t)\) in one symbol. Specifically, \(\phi(t)\) is fixed during one symbol period, which equals the multisine envelope period \(T_{s}=\frac{1}{\Delta f}=\frac{N_{t}}{B}\), so that the information is modulated without changing the designed signal envelope.  
Figure 2 shows two examples applying the multisine PSK modulation: First we show a binary PSK where \(\phi(t)\) alternates between 0 and \(\pi\); in the second example, \(\phi(t)\) follows a uniform distribution between 0 and \(2\pi\). It is clear that both result in the same PAPR, only the location of the peak amplitude in the symbol period varies.  
By applying the same phase on each tone, a smaller bit error rate can be achieved at the cost of a lower bit rate. For example, considering M-PSK is applied, the bit rate is \(N_{t}\log_{2}M\) for independent coding (as in OFDM) and \(\log_{2}M\) is the bit error rate when considering joint coding. The bit error rate is \(Q\left(\frac{E_{s}}{N_{t}N_{0}\log_{2}M}\right)\) for independent coding and \(Q\left(\frac{E_{s}}{N_{0}\log_{2}M}\right)\) for joint coding, where \(N_{0}\) is the noise power spectral density. If \(M\) goes to infinity, the bit rate and bit error rate of independent coding and dependent coding converge. The bit rate of reliable transmissions is bounded by the total channel capacity experiencing the frequency-selective channel [25] and each sub-channel is bounded by the PSK channel capacity [28]. As the per tone coding strategy does not matter for very large M, and as capacity is the same for both, we use the capacity equation to evaluate the information transfer performance. Thus the channel capacity with mutisine PSK modulation experiencing frequency-selective channel is  
\[C_{PSK}=\sum_{n=1}^{N_{t}}\int\frac{1}{T_{s}}\log_{2}\sqrt{\frac{4\pi}{e}\gamma _{n}}Pr(\gamma_{n})d\gamma_{n}. \tag{10}\]  
It should be noted that the maximum capacity is achieved when \(\phi(t)\) follows the uniform distribution \([0,2\pi]\)[28].
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 3 Evaluation Framework
### Network Architecture  
As described in Figure 1 the mmWave FWA network architecture is composed of three different wireless nodes:  
* Point of presence (PoP) is the node that connects the wireless backhaul network to the fibre core. We use an PoP location from one of the operator in Belgium. It consists of a tower, 14 m in height, with four antenna boxes covering a 360-degree area.
* Edge nodes (EN) are the ones in charge of serving final users or create a backhaul mesh network with other edge nodes.
* Customer premises equipment (CPE) is the final node in users' homes  
We define a hierarchical topology, as shown in Figure 2, where CPE nodes (blue circles) are connected to only one EN (purple triangles). The backhaul network connecting ENs to the PoP has a tree shape where all traffic is aggregated to the PoP in a hierarchical way. In this architecture, each EN has only one route to connect to the PoP, which minimises the number of hops, simplifying the complexity of the ENs, thus reducing its cost [65].
## 5 Research Trends in Ge Photodetectors
### Improvement of Speed Performance of Waveguide Ge Photodetectors  
Another trend of the continuous evolvement of Ge photodetectors is the increase of detector bandwidth. At the starting point of Ge detectors' integration with Si waveguides, Ge growth on SOI wafers and optical coupling between Ge detectors and Si optical waveguides was first explored. The reported detectors are \(\sim\)100 \(\upmu\)m long to ensure full absorption of light around 1.55 \(\upmu\)m wavelength, inevitably leading to large device capacitance so that the bandwidth are limited \(<\)10 GHz by RC delay.  
Nowadays, special care in design is given to detector's bandwidth performance. With adoption of shorter devices, sophisticated radio-frequency coplanar waveguides (CPW), metal interconnections and frequency measurement technologies, 40 Gbit/s operation was reported by several groups [47,49,52,73], with waveguide detector bandwidths as high as 47 GHz [73].
## 4 Discussion
### Spatiotemporal Variation of NPP  
In general, all three NPP estimates showed similar spatial patterns, with mean annual NPP higher in the southeast and gradually decreasing towards the northwest (Figure 3 and Figure S2). This finding was consistent with previous studies [10; 11; 58; 12]. This may have been due to the climatic gradient (e.g., temperature and precipitation) from the southeast to the northwest in China, which is more favorable for vegetation growth in southern China [11; 59]. The lowest NPP value in northwest China and the Tibetan Plateau, which was consistent with References [10; 11], may have resulted from low temperature and the absence of precipitation. In addition, vegetation type was also a key factor that affected NPP spatial distribution. In Southern China, the dominant vegetation type are evergreen forests (Figure S1) which have a higher productivity than that of the grassland and desert vegetation (Figure 4) distributed mostly in Northern China and the Tibetan Plateau. Productivity in the southeast Tibetan Plateau was higher than that of other areas in the Tibetan Plateau as demonstrated by our conclusion above.  
The total amount of NPP increased at a rate of 0.009 Pg Ca\({}^{-1}\) in China from 2001-2014, which was similar with Reference [10], who found a rate of 0.008 Pg Ca\({}^{-1}\) in China from 1999-2010. The decreases in NPP over Southern China were more than offset by increases in NPP over Northern China and the Tibetan Plateau. The overall increased NPP could help to mitigate climate warming at regional scale and/or global scale to some extent. In terms of trend variation, a continually increasing trend of NPP in China from 2001-2014 was found, despite the occurrence of a turning point year which was similar with the previous finding in Reference [10]. This may be due to the fact that over the past three decades, the central government of China has decided to combat severe environmental degradation, including declining vegetation cover and expanding desertification [29]. To realize this goal, the Three North Shelter Forest System project, the Beijing-Tianjin Sand Source Control Program, and the Grain for Green Project [29] were implemented. Alternatively, as the turning point is related to ENSO events/cycles, it is necessary to validate this conclusion in the future.  
At a regional scale, NPP trends in China showed a prominent geographical heterogeneity. The trends before and after the turning point year in Northern China and the Tibetan Plateau were all positive. In contrast, the trend in Southern China was the opposite, which may have been due to ecorestoration, forest/grassland protection, and reforestation in Northern China and the Tibetan Plateau after 2000 [29]. However, in Southern China, freezing low temperatures in early 2008, the severe drought in 2009 [31], and decreased solar radiation [12] may have all contributed to the decreasing NPP. Due to the physiological and/or local environmental conditions, the trend and turning point year among vegetation types were diverse.