esa-sceva/satcom-chunk-collection huggingface dataset:
# Conditional Measurements with Silicon Photomultipliers
## 5 Conclusions  
In this paper we addressed a thorough theoretical model for the conditional measurements with SiPMs. In particular, we included the contribution of the OCT and we took into account the possibility of an imbalance between the two detection chains. We provided a complete description of the detection of a multi-mode TWB state in the presence of the OCT, showing explicitly the effects of such correlated noise on the reconstructed distribution. We obtained a closed formula for the detected-event distribution of the conditional states and an analytic expression for the first moments. Hence, we retrieved the Fano factor, which represents a sufficient criterion for nonclassicality. In particular, we found that, in the presence of cross-talk effect, nonclassicality is more easily attained by:  
* reducing the imbalance between the two detection arms;
* decreasing the mean value of the unconditioned states;
* increasing the number of modes.  
Moreover, we found a useful bound between the quantum efficiency of the detectors and the OCT probability, which sets a link between their mutual values for still revealing the nonclassicality of conditional states. Actually, we demonstrated that this bound is valid in general for the twin-beam states, on which the conditioning operation is performed.  
The theoretical expectations have been validated by the experimental generation of conditional states by conditional measurements performed on multi-mode TWB states with SiPMs. The good agreement between the experimental data and the theoretical predictions suggests that the conditional measurements can be performed even on more populated states to produce well-populated conditional states as well by exploiting the good dynamic range of SiPMs.  
Finally, we hint that the model may be further improved by including the dark counts, which is another common drawback of SiPMs at room temperature. However, it is worth noting that in the case of a light signal integrated over short gate widths [17], which correspond to our experimental condition, the mean number of dark counts is remarkably low, which is the reason why we did not address this topic here. Moreover, it could be interesting to include cascade effects and generation of multiple secondary avalanches in the model for the OCT, so that a realistic case for large \(\varepsilon\) could be compared with the one explored here.  
[PERSON], [PERSON] and [PERSON] conceptualized the work, [PERSON] performed the theoretical calculations, [PERSON] and [PERSON] performed the measurements, [PERSON] and [PERSON] analysed and interpreted the data, [PERSON] and [PERSON] drafted the work, [PERSON] substantively revised it. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: The datasets used and analysed during the current study are available from the corresponding author on reasonable request.
## 3 Testing Results
### AE Characteristics  
#### 3.2.1 AE Ring Count  
Figure 5 shows the results of the AE ring count of the No. 4 coal specimen. AE ring count presents the following evolution laws: As the axial stress was linearly loaded from 0.0 to 3.0 MPa, the coal specimen is in the compaction and elastic deformation phases, and there occur basically no new cracks, so the AE ring count is very low. In the initial two loading-unloading cycles, a small number of new cracks began to appear in the coal specimen, but the AE ring count is still low. In the subsequent loading-unloading cycles, with the continuous improvement of cyclic loading and unloading maximum stress, the coal specimen falls into the crack propagation and penetration phases. The AE ring count keeps increasing and goes up to a maximum value at coal specimen failure. However, it should be noted that during the 1505-1506 s, the maximum value of the AE ring count is greater than that of the next cycle but less than that of coal specimen failure. This shows that a small-scale local failure emerged in the coal specimen at that time, and the internal structure of the coal specimen quickly reached a new stable state after readjustment. In addition, before the failure cycle of the coal specimen, the AE ring count is very high in the loading phase of each cycle but very small in the unloading phase of each cycle. This shows that when the loading and unloading do not cause the failure of the coal specimen, the AE activities mainly occur in the loading phase, and there exist basically no AE activities in the unloading phase. More importantly, it is worth noting that except for the failure cycle, the maximum AE ring count of other cycles appears before the maximum stress of the loading phase.  
Figure 4: Relation curves between elastic modulus and cycle number in different loading stress intervals.
## 4 Results
### Performance Evaluation Metrics
#### 4.1.5 Average End to End Latency (\(\mathcal{T}\))  
Equation (41) is used to compute the average end-to-end latency for each node during a data collection cycle (\(\mathcal{T}\)). A packet's latency is the amount of time it takes to travel from its source to its destination. The ideal latency value is minimal.  
\[\mathcal{T}\ =\ \frac{1}{\mathcal{K}}\sum_{m=1}^{\mathcal{K}}\frac{1}{N_{d}} \sum_{i\in\mathcal{S}_{d}}\left(T_{ri}-T_{si}\right) \tag{41}\]  
The receiving timestamp of the packet sent by the \(i\)th node at the destination is \(T_{ri}\) in Equation (41), whereas the sending timestamp of the packet sent by the \(i\)th source node is \(T_{si}\). In addition, \(N_{d}\) is the total number of delivered packets in a routing cycle, \(\mathcal{S}_{d}\) is the source node ID set of delivered packets, and \(\mathcal{K}\) is the total number of data collection cycles. The data packets are routed between the nodes (OBUs or RSUs). Therefore, the end points (source and destination) for latency calculation are two different nodes (two data plane units).
# Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks
## 6 Cooperative Content Caching Framework  
We proposed a novel framework that works in a cooperative behavior with CS to find the optimality, under multiple tiers. Although CS is used in various research, the uniqueness of our technique is to provide pre-caching while predicting the upcoming server and neighboring nodes. Pre-caching, mobility prediction, and cooperative behavior with CS to find the optimal path increases its overall performance in terms of delivery delay, hit ratio, and caching cost. First, end-user rating data are acquired, including their interests, such as the types of movies and content they prefer. Then, the server caches the most popular material. Content caching helps users and reduces network traffic. Second, send the requested content using the best route. We employ K-means for caching on cross-tier caching servers and cuckoo search for path optimality due to its improved speed. In this work, only popular content is cached in the MBS and surrounding RSUs based on user preferences and popularity estimates. As a user requests content, it is already cached on the server and provided quickly. But if a user requests content that has not been cached, the request will be sent to a central cache such as MBS. MBS's popularity is conveyed to RSUs in its area. The MEC server first downloads content, then distributes cached data to associated RSUs. We employ Geo-life Trajectory to anticipate the requesting node's position. User-ID, spot-ID, location name, longitude, latitude, and time are the key attributes of the data set to determine content popularity. After calculating popularity based on user-ranked movies, data is cooperatively pre-cached on caching servers.
# Predicting Runoff from the Weigan River under Climate Change
## 5 Conclusions  
In this study, data from seven GCMs from different countries and organisations were selected and subjected to downscaling bias correction using the delta method, and the correlations between the temperature and precipitation data of each model and the observed data of the historical period were assessed. The weighted ensemble averaging method based on Pearson's r correlation coefficient was used to process the selected GCM data, and the ensemble averaged processed \(r\)-MME data were coupled with the SWAT model to predict the future runoff out of the mountain in the study area. The following conclusions are drawn based on the results:  
(1) The SWAT model constructed in this study has good applicability in the upper watershed of the Weigan River outflow, which can recreate the hydrological process of the study area in historical years and reveal the trend of its future runoff evolution.  
(2) Under four different radiative forcing scenarios, namely, SSP1-2.6, 2-4.5, 3-7.0 and 5-8.5, the maximum and minimum temperatures, precipitation and runoff in the study area in the years 2020-2050 show an increasing trend, and the magnitude of the increase and the trend of the increase in these elements are not the same, with the increase under the radiative forcing scenario of SSP5-8.5 being the most pronounced in comparison to the other three scenarios.  
(3) Under the four radiative forcing scenarios, the runoff from the Weigan River out of the mountain is predicted to be maintained at 25.68-30.89 \(\times\) 10\({}^{8}\) m\({}^{3}\) in 2030 and 2050, which is an increase of 1.35-21.91% compared with the current runoff, among which the contribution of changes in temperature and precipitation to the increase in future runoff is 68.71% and 27.24%, respectively.  
**Author Contributions:** Conceptualisation, J.S., C.R. and A.L.; methodology, J.S. and F.C.; software, J.S.; validation, J.S., P.Z. and A.L.; formal analysis, J.S., C.R. and J.Z.; investigation, J.S.; resources, A.L. and X.D.; data curation, J.S. and A.L.; writing--original draft preparation, J.S.; writing--review and editing, C.R., A.L. and P.Z.; visualisation, J.S., C.R. and J.Z.; supervision, A.L., P.Z. and F.C.; project administration, A.L.; funding acquisition, X.D. and P.Z. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (Grant No. 52309041; Grant No. 52179028) and the Third Xinjiang Scientific Expedition (Grant No. 2022 xjkk0103).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Acknowledgments:** We express our sincere thanks to [PERSON] from Xinjiang Corps Survey and Design Institute (Group) Co., Ltd., for her assistance in investigation and resources.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 1 Introduction  
With the rapid development of China, the massive projects in civil engineering require more and more river sand as normal fine aggregates based on the main trend in concrete design. The depletion of river sand could happen in a number of places, especially places that are further away from any rivers. In this condition, the use of machine-made sand [1; 2; 3] could be an easy and feasible option. Compared to river sand, machine-made sand has several advantages, such as the existence of aggregate micro fines (AMF). There have been conflicting reports on the role of AMF [4; 5; 6; 7] on the properties of machine-made sand concrete (MSC). Some researchers [8; 9; 10] reported a decreased slump and an increase in water demand in the MSC with an increase in AMF content. Others [11; 12; 13; 14] reported that there is an optimum content of AMF for the properties of MSC, and the existence of AMF could contribute to a higher compressive strength. For example, [PERSON] et al. [11] reported that the highest compressive strength of MSC was achieved when there was 7.5% AMF in the machine-made sand. However, the compressive strength was reported to be influenced by many different factors [15],and this could result in the different reported findings. It can be concluded that the effect of AMF on the concrete properties requires further investigations, especially in MSC. It was found that the effect of AMF on the theology of cement paste was influenced by the surface property, particle size, and also the mineral types [16]. [PERSON] et al. [14] provided a theory analysis based on the physical and mathematical packing model.  
In order to modify the properties of concrete, the polypropylene fiber (PPF) could be added [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28], and the PPF was found to be very effective in improving the crack resistance compared to other methods. The addition of PPF was found to decrease the workability [17] and increase the compressive strength [29; 30; 31; 32], crack resistance [33; 34; 35], and durability [36; 37; 38; 39; 40; 41] given that the PPF was within an optimum content. The addition of fibers in concrete can also decrease the shrinkage and reduce the porosity [42; 43]. However, the addition of PPF in MSC has rarely been reported, and the optimum content of PPF in MSC is still unknown.  
In addition, the use of aluminum sulfate in the concrete could be beneficial because the aluminum sulfate could react with CaO or Ca(OH)\({}_{2}\) to form the ettrinigite and thereby decrease the permeability and increase the compaction of concrete [44; 45; 46], but the use of aluminum sulfate in MSC has not been investigated before.  
Therefore, in this study, the optimum content of AMF and its effect on the properties of MSC (including slump, compressive strength, and permeability) were investigated. In addition, based on the optimum content of AMF, the effects of aluminum sulfate and polypropylene fiber on the properties of MSC were studied, and the related mechanisms were analyzed.
## 2 Data and Methods
### Development of \(n_{p}\) Estimation Model  
#### 2.5.1 Estimation of TSM from \(R_{\rm rs}(\lambda)\)  
Estimation of TSM is a key step in retrieving the final target, \(n_{\rm p}\), by \(R_{\rm rs}(\lambda)\). This study firstly analyzed the correlation between single \(R_{\rm rs}(\lambda)\) and TSM in the spectral range of 400-700 nm, the results of which showed that the red bands were the most sensitive to TSM vis-a-vis the other bands (Figure 2). For instance, the correlation coefficients (\(R\)) showed high values (0.838 and 0.839) in the 660-nm and 680-nm bands of the GOCI image data, respectively. These findings also agree with those of previous studies [30; 31]. These bands can be subsequently considered for use in establishing a TSM model. Empirical methods are simple and easy to model and, more importantly, straight and efficient, particularly for local regions [32]. Thus, this study made use of single bands, band ratios, and band combinations to develop the TSM model. Specifically, eight band forms, as shown in Table 2, were tested. For each band form, the correlation with TSM was examined for all possible combinations from six GOCI \(R_{\rm rc}(\lambda)\) bands, and the optimal band combinations with the highest \(R\) are given in Table 2. After comparing the correlations among these band forms, we determined \(X\)7, i.e., (\(R_{\rm rs}(555)\) + \(R_{\rm rs}(660)\))/(\(R_{\rm rs}(555)\)/\(R_{\rm rs}(660)\)), with the highest \(R\) of 0.847 among those band forms for establishing the TSM model. This method to find the optimal band form and optimal band combination was similar to that used in previous studies [33; 34]. Several mathematical methods, including linear, power, exponential, and logarithmic functions, were used to model TSM, and their accuracies were then  
Figure 2: Correlation coefficient spectra between total suspended matter (TSM) and \(R_{\rm rs}(\lambda)\) in the range of 400–700 nm. intercompared to obtain a good retrieval model. The obtained results showed that the simple linear model continued to perform best (Figure 3), and it was subsequently used to estimate TSM in our whole model framework. 2.5.2 Estimation of \(b_{\text{bp}}\) and \(B_{\text{p}}\) from \(R_{\text{rs}}(\lambda)\)  
Estimation of the particulate backscattering coefficient, \(b_{\text{bp}}(\lambda)\), is another important step. Similar to TSM estimation, this study firstly carried out a correlation analysis and then demonstrated that the X7 band combination form was the best indicator for the \(b_{\text{bp}}(\lambda)\) estimation (Figure 4B). Note that the 488-nm channel was used to denote the parameter due to its better performance when compared to those of the other channels. After testing several different mathematical functions, we selected and
## 2 Materials and Methods
### Experimental Methods and Sample Analysis  
#### 2.2.1 Cd Uptake, Accumulation and Translocation in _S. nigrum_  
The methods of experimental procedure and sample analysis were, in general, similar to those applied in another study [5]. The harvested _S. nigrum_ plants were washed three times with tap water, next with deionized water, and separated into root (underground part) and shoot (aboveground part consisting of stem, leaf and flowers). The plant material was then oven dried at 105 \({}^{\circ}\)C for 30 min, followed by drying at 75 \({}^{\circ}\)C to constant weight (with 0.0001 g accuracy). For drying, a laboratory oven (SML, ZALMED, Warsaw, Poland) was used. Biomass (Bm\({}_{\text{n}}\)) was measured as dry weight (DW) of underground and aboveground plant parts, where n-index means root (R) and shoot (S).  
The Cd concentrations in the plant material (root and shoot) were determined after grinding into fine powder in GRINDOMIX GM 200 high-speed grinder (Retsch, Dusseldorf, Germany) and open digestion of about 0.5 g of the powdered material with a mixture of concentrated nitric and perchloric acids (Merck, Darmstadt, Germany) in proportion 15:5, until the mineralization the next day, followed by quantitative filtration and Cd determination with ICP-OES (OPTIMA 2000 DV, Perkin-Elmer, Waltham, MA, USA). For quality assurance/quality control (QA/QC), the standard reference material (NIST SRM 1547, peach leaves) was used.  
Soil samples were analyzed for Cd content following a similar procedure with the use. of microwave-assisted digestion (Anton Paar Multiwave 3000 SOL, Gratz, Austria). The digestion was carried out at 1400W, IR = 240 \({}^{\circ}\)C and pressure values p = 60 bar. After mineralization and quantitative filtration, Cd contents in soil were determined with the use of the same ICP-OES (OPTIMA 2000 DV, Perkin-Elmer, Waltham, MA, USA).  
On the basis of Cd concentrations (C\({}_{\text{n}}\)) and biomass (Bm\({}_{\text{n}}\)) determined in the specific parts \(n\) of the plants, where _n_-index means root (R) and shoot (S), Cd enrichment factors of the related parts of the plants EF\({}_{\text{n}}\) = C\({}_{\text{n}}\)/C\({}_{\text{soil}}\), translocation factors TF = C\({}_{\text{S}}\)/C\({}_{\text{R}}\), accumulated Cd loads L\({}_{\text{n}}\) = C\({}_{\text{n}}\) Bm\({}_{\text{n}}\), and load translocation factors LTF = L\({}_{\text{S}}\)/L\({}_{\text{R}}\)--from root to shoot were calculated according to [PERSON] et al. [5; 21]. In these equations, C\({}_{\text{n}}\) is Cd concentrations in root and shoot, respectively (mg kg\({}^{-1}\)), L\({}_{\text{n}}\) is accumulated loads of Cd in root and shoot, respectively (ug pot\({}^{-1}\)), Bm\({}_{\text{n}}\) is biomass of root and shoot, respectively (g pot\({}^{-1}\)).
## 4 Coupling of Solutions in Regions \(R_{i}\) and \(R_{ii}\)
As a result, the following equation is obtained:  
\[H_{n,\varphi}^{(II)}(r,z)=\frac{I_{n}}{2\pi r}\frac{\cosh(\beta_{n}z)}{\cosh \left(\frac{\beta_{n}z}{2}\right)}+\frac{2\beta_{n}^{2}I_{n}}{\pi a\hbar}\sum \limits_{m=0}^{\infty}\frac{(-1)^{m}}{k_{m}\lambda_{n,m}^{2}}\frac{K_{1}( \lambda_{n,m}r)}{K_{1}(\lambda_{n,m},a)}\cos(k_{m}z). \tag{20}\]  
At the same time, if the hole radius \(a\) is much greater than the skin-effect depth \(\delta_{0}\) for the fundamental harmonic, i.e., \(a\gg\delta_{0}\), then the following asymptotic formulas can be used for the functions \(K_{1}(\lambda_{n,m}a)\) and \(K_{1}(\lambda_{n,m}r)\), namely,  
\[K_{1}(\lambda_{n,m}a)\propto\sqrt{\frac{\pi}{2\lambda_{n,m}a}}e^{-\lambda_{n, m}a}\;\;\;\text{and}\;\;\;K_{1}(\lambda_{n,m}r)\propto\sqrt{\frac{\pi}{2 \lambda_{n,m}r}}e^{-\lambda_{n,m}r}. \tag{21}\]  
The substitution of (21) into Equation (20) results in the following approximate formula for the nth harmonic of the MF in the tank wall:  
\[H_{n,\varphi}^{(II)}(r,z)=\frac{I_{n}}{2\pi r}\frac{\cosh(\beta_{n}z)}{\cosh \left(\frac{\beta_{n}z}{2}\right)}+\frac{2I_{n}\beta_{n}^{2}}{\pi\hbar\sqrt{ \pi}}\sum\limits_{m=0}^{\infty}\frac{(-1)^{m}}{k_{m}\lambda_{n,m}^{2}}\;e^{- \lambda_{n,m}(r-a)}\cos(k_{m}z). \tag{22}\]Equation (22) is a simplified formula for computing MFs in tank walls of transformers. It does not require the use of special functions such as the modified Bessel functions, etc., which makes formula (22) advantageous for practical implementation.
## 1 Introduction
To mitigate the aforementioned constraint, a paradigm known as geo-opportunistic routing emerges, in which geographic routing is adopted for greedy forwarding by using geographic location of the set of forwarder nodes [9; 10]. However, in multi-hop data delivery, nodes positioned nearby base station are overburden with traffic which dissipates the node energy very quickly. Due to the quick dissipation of node battery near the sink, nodes placed away from destination are unable to transmit data due to the unavailability of forwarders. To reduce the data load at intermediate nodes and recover data from the void regions, mobile sinks are mounted over the ships, vehicle, etc. to gather the information of interest from the region of interest. The availability of mobile sinks enables new horizon of applications including but not limited to seabed survey, the detection of minerals from the oceans which are humanly not possible to monitor [11]. Hence, the mobility provided ease to directly retrieve the information from the communication void. With the incorporation of sink mobility, the network topology and delay in the network increases with the passage of time. To reduce the aforementioned constraints in geo-graphic, opportunistic, geo-opportunistic and mobility of sinks, we have made the following contributions:  
**Contributions:** We have proposed two routing algorithms; geo-spatial division based geo-opportunistic routing scheme for interference avoidance (GDGOR-IA) and geographic routing for maximum coverage with sink mobility (GRMC-SM). The distinctive features of our work are list as follows:  
* The distribution of the network field into small cubes is performed to make local routing decisions for efficient energy consumption. * The distributive geo-opportunistic routing in geo-spatial network field avoids the interference by restricting number of nodes. * In order to minimize traffic load on intermediate nodes, mobile sinks gather data directly from underwater nodes and also use to recover data from void hole. * An optimal holding time is formulated to ensure that successful transmission acknowledgement receives before the time expires of an individual node. This paper is organized as: a comprehensive overview of existing underwater routing schemes is stated in Section 2. While, Section 3 presents the pre-requisites of the network which are network model, energy model and control messages. Geo-opportunistic routing without sink mobility is discussed in Sections 4 and 5 illustrates geo-opportunistic routing with sink mobility. In Section 6, a detailed linear programming based mathematical problem formulation subjected to attain optimal network lifetime and packet delivery ratio (PDR). Section 7 presents a detailed discussion of simulation results regarding network lifetime, PDR and data traffic load. Finally, Section 8 concludes our proposed work based on the analysis made in Section 7 with compared existing literature. The symbols and notations used in the manuscript are listed in Table 1.