esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Quantifying Hydrological Connectivity: Development of Spatial Indicators  
In the model, different indices have been set up to assess the connectivity within the catchment. As soon as a drop agent reaches the outfall, its starting patch is considered to be connected to the outfall [52].  
Three types of connectivity indicators have been developed:  
**The simple connectivity indicator Ic**. This is an indicator that immediately concerns the **land cells**. It is a true or false variable.  
**The connectivity indicator according to the share of connected areas \(Ic_{s}\)** is an indicator at a parcel scale. The unit of study is therefore **the plot**. This indicator calculates, for each plot, the number of connected patches. By multiplying this number by the area of a patch, the connected area (\(S_{C}\)) can be calculated. By relating this area to the total area of the patch (\(S_{T}\)), we obtain the connectivity indicator according to the percentage of connected area \(Ic_{s}\).  
\[Ic_{s}=\frac{S_{C}}{S_{T}}\times 100\]  
where \(S_{c}\) is the connected area and \(S_{T}:\) is the total area of the plot.  
**The connectivity indicator by volume of water connected \(Ic_{p}\)** maps the proportion of the volume of water discharged from the plot that reaches the outfall in relation to the total volume of precipitation discharged. For example, if the stock_in_water of the drop  
Figure 6: Simplified activity diagram of the hydrological agent.  
is 15 mm when it arrives at the outlet and its _list_start_patch_ is composed of three patches, each of these three patches is assigned a value of _volume_water_finish_ equal to \(15/3=5\) mm.  
The indicator is then calculated as the quotient between these two values.  
\[Ic_{v}=\frac{V_{c}}{V_{T}}\times 100\]  
where \(V_{c}\) is the volume of runoff from the plot that reaches the outlet and \(V_{T}:\) is the volume of precipitation falling on the whole plot at the beginning of the simulation.  
Figure 7 shows an example of the application of each of these indicators to the Glape basin (modelling a 5 mm rainfall event applied to the basin with the 1994 landscape configuration). While the \(Ic\) indicator is distinguished by its cellular scale, the \(Ic_{s}\) and \(Ic_{v}\), which are both at the parcel scale, do not show much difference in spatial distribution and are therefore both applicable if one wants to focus on parcel connectivity.
# Impact of Heart Rate Fragmentation on the Assessment of Heart Rate Variability +
## 4 Discussion
The present study suggests that a substantial portion of HF power at ages 60-90 may be non-parasympathetic origin. Decreased HRV in 24-h ECG is an increased mortality risk after acute myocardial infarction [2; 23; 24]. These studies have reported a greater predictive power for HRV metrics that mainly reflects fluctuations at lower frequencies (SDNN and VLF) than those reflecting fluctuations at higher frequencies (rMSSD and HF). This seems resulted from increased HRF in the elderly and in patients with adverse prognosis [7]. These suggest that proper subtraction of the influence of HRF on HRV may improve the usefulness of HRV as an autonomic index and risk predictor. This is an important future research topic. The possible limitation of the present study may be that the ALLSTAR database is composed of clinical Holter ECG data. All ECG data were recorded for some clinical purpose(s), although only data without ECG abnormality were selected for this study. On the other hand, these data can be regarded as a sample that reflects the general patient population undergoing Holter ECG in Japan. Another potential limitation is that the present findings were obtained from 24-h ECG data. Thus, they may not apply to other equipment or systems. HRF itself, however, could occur in short ECG recordings such as those used for autonomic function assessment by short-term (typically 5 min) HRV [3]. The LF and HF powers obtained from such analyses also need to consider the influence of HRF, especially in elderly subjects. On the other hand, the present observations may not apply to heartbeat interval data estimated from pulse waves. The evaluation of HRF depends on the accuracy of interval measurement, which may not be enough with pulse wave signals.
# Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks +
## 5 TERI-MAC Design
### Arrangement of \(\mathsf{ATS}\) Transmission
If we sort senders in the set \(\mathbf{S}\) by the propagation delay \(\tau\) in an ascending order, i.e., sender \(S_{L}\) has the largest propagation delay \(\tau_{L}\), and Equation (3) can be rewritten as  
\[T_{m}\geq(L-i)T_{\mathsf{ATS}}+(t_{0}+T_{\mathsf{R}\mathsf{T}\mathsf{R}})+2 \tau_{i},\hskip 28.452756 pti\in\{1,\ldots,L\}. \tag{4}\]  
Therefore, the minimum \(T_{m}\) is  
\[T_{m}=\max_{i}\{(L-i)T_{\mathsf{ATS}}+(t_{0}+T_{\mathsf{R}\mathsf{T}\mathsf{R} })+2\tau_{i}\},\hskip 28.452756 pti\in\{1,\ldots,L\}. \tag{5}\]Finally, the receiver calculates \(t_{i}\) by \(t_{i}=T_{m}-(L-i)T_{ATS}\) and the sender delay \(D_{i}\) by \(D_{i}=t_{i}-t_{0}-2\tau_{i}\) for each sender and then attaches the time difference \([D_{1},D_{2},\ldots,D_{L}]\) and the MAC addresses of the corresponding senders to an \(RTR\) packet. After the \(RTR\) reception, sender \(S_{i}\) is delayed for \(D_{i}\) before replying to its \(ATS\). From the above descriptions, we know that the knowledge of the receiver's local time is not necessary on the sender side, rather, the time difference \(D_{i}\) is sufficient for the scheduling of \(ATS\) transmission, which allows it to operate in a non-synchronized UWSN.
## 2 Related Work
### Information Diffusion and Macroscopic Prediction
#### 2.1.2 Feature-Based Approaches  
A variety of features are extracted from the raw data, which can be mainly divided into four types: temporal data [15; 16; 17; 22], structural data [14; 15; 19; 20; 21], user information [12; 13], and content features [12; 14; 18]. These features are used in a machine learning model to predict popularity. Temporal features are usually extracted using the peeking strategy, i.e., observing a small number of early participants and their active time. Temporal consideration in cascades has been identified as one of the most important factors in popularity prediction [14]. However, some studies claim that their advantages diminish over time [16]. Structural features extracted from graphs can be classified as [32]: (i) participants only, i.e., only cascade graphs are involved [20]; (ii) global graphs, i.e., both participants and non-participants are considered [21]; and (iii) r-reachable graphs, i.e., a compromise that extends the cascade graph within the scope of the global graph [14; 15]. Furthermore, different platforms have unique diffusion mechanisms, which might result in dynamics that differ from well-studied social network scenarios [19]. Opinions of recent studies differ on the validity of content features. The study [14] found that content features became less important when more participants were observed, and ref. [12] found that their model did not improve its effectiveness by the addition of content features.  
These hand-crafted features are difficult to build and rely on subject knowledge, and the conclusion of previous works may differ depending on the community platform.
# Time Series Clustering of Online Gambling Activities for Addicted Users' Detection
## Appendix A Time Series Analysis
### Constrained DTW  
The time and space complexity of DTW is \(O(N^{2})\), because each cell in the cost matrix is filled once in constant time, and the cost of the optimal alignment can be recursively computed by:  
\[D(t,t^{\prime})=d(t,t^{\prime})+\min\begin{cases}D(t-1,t^{\prime})\\ D(t-1,t^{\prime}-1)\\ D(t,t^{\prime}-1)\end{cases}\]  
The complexity of DTW is, usually, not an issue. Nevertheless, the \"conventional\" algorithm is too slow for searching an alignment path for a large data set. To improve the time and space complexity, as well as accuracy of DTW, different techniques for aligning time series have been introduced [20; 21]. In this paper, we rely on the constrained DTW [22].  
In constrained DTW, some constraints are used to speed up the original algorithm. The Sakoe-Chiba band [23] and Itakura parallelogram [16] are well-known constraints. The cost matrix is filled only in the shaded areas around the diagonal by DTW. These shaded areas depend on the constraint, as shown in Figure A2. Thus, the algorithm finds the optimal alignment according to the selected constrained window. It is fundamental to notice that the globally optimal alignment is found only if it is fully inside the window.  
Sakoe-Chiba DTW (DTW\({}_{sc}\)) is defined as follows:  
\[\begin{split}&\text{DTW}_{sc}(x_{i},x_{j})\equiv\min_{\pi\in \mathcal{A}}C(\pi),\\ & C(\pi)\equiv\frac{1}{|\pi|}\sum_{k=1}^{|\pi|}w_{\pi_{1}(k),\pi_{ 2}(k)}\phi(x_{\pi_{1}(k)}^{i},x_{\pi_{2}(k)}^{j})=\frac{1}{|\pi|}\sum_{(t,t^{ \prime})\in\pi}w_{t,t^{\prime}}\phi(x_{it},x_{it^{\prime}})\end{split} \tag{10}\]  
where if \(|t-t^{\prime}|<c\), then \(w_{t,t^{\prime}}=1\), and else \(\infty\), function \(\phi\) is the Euclidean norm and \(c\) is the Sakoe-Chiba bandwidth.  
Figure A1: Optimal alignment path between two sample time series with time warping (left), without time warping (right).  
Itakura DTW slightly differs from the Sakoe-Chiba version, since, instead of using the boundary condition introduced by [PERSON] and [PERSON], it imposes that two consecutive horizontal and vertical moves are not allowed. This is done by introducing an indicator function \(g(\cdot)\) in the boundary constraints, for horizontal moves, and introducing a set of three actions for vertical ones. As a consequence, the algorithm is controlled to search through only a parallelogram space. In the DTW literature, the resulting search space is often called the \"global\" Itakura parallelogram, and the role of the indicator function \(g(\cdot)\) is called the Itakura \"local\" constraint.
# Digestate as Sustainable Nutrient Source for Microalgae--Challenges and Prospects
## 4 Digestate Processing
### Solid-Liquid Separation  
For solid-liquid separation, various technologies are available, e.g., decanter centrifuges, screw press separators, bow sieves, and sieve belt presses. Decanter centrifuges and screw press separators are most commonly used. Choosing the appropriate technology is based on the characteristics of the digestate. Decanter centrifuges are most suitable for small particle sizes and low TS concentrations [52] and are, for example, used in industrial and municipal wastewater treatment plants [17; 47; 48].  
In the solid fraction, the main shares of the elements phosphorous and carbon remain, while soluble compounds such as nitrogen (TN, NH\({}_{4}\)-N) and potassium (K) stay mainly in the liquid fraction (see Table 3) [17; 53]. By coupling various efficient technologies such as centrifuge or screw press with filtration, the separation efficiency could be increased. It can further be enhanced by using flocculating and precipitating agents, e.g., aluminum sulfate (Al\({}_{2}\)(SO\({}_{3}\))\({}_{3}\)), ferric chloride (FeCl\({}_{3}\)), and ferric sulfate (Fe\({}_{2}\)(SO\({}_{4}\))\({}_{3}\))[46; 47; 53; 54]. There are various additives on a natural basis such as lime (Ca(OH)\({}_{2}\)), functionalized chitosan, cellulose, gums, mucilage, and modified starch, as well as synthetic additives such as polyacrylamide [46; 55; 56]. Through the addition of inorganic metal salts, the salt concentration in the liquid phase might also increase.  
By adding precipitating agents (lime and FeCl\({}_{3}\)), the separation efficiencies of suspended solids (from 46% to 75%) and P (from 54% to 95%) were increased and that of N (from 18% to 47%) and K (from 10% to 25%) as well. The increase of N can be explained by higher separation of organic nitrogen rather than by NH\({}_{4}\)-N, which is hardly affected by precipitating agents [54].
## 1 Introduction
Moreover, these interferences are likely to adopt the orthogonal frequency division modulation (OFDM) signaling for high data rate and strong multi-path fading resilience. When an equally spaced multi-tone OFDM signal comes into the MedRadio device as an interferer, numerous intermodulation distortion components will be created by the multiple subcarrier tones, even though they are out of the band. Thus, in modern MedRadio receiver design, it is essential to analyze the effects of the out-of-band (OOB) interference on the receiver's signal-to-noise ratio (SNR) and to design circuits for mitigating its effects. Note that this issue usually cannot be neglected in typical MedRadio receivers even though the interference exists out of the band. We cannot expect sufficient filtering and attenuation for the OOB interference at the RF front end because typical MedRadio antennas show wideband characteristics [11], a complex of high-order matching and filtering at the RF front end may not be favored for low cost and small form factor realization [10], and the LNA in a receiver integrated circuit is not typically band-specific because resistive loads are frequently adopted rather than bulky inductors for the small silicon area. In this work, we analyze the intermodulation distortion effects induced by the OOB OFDM interferer on the MedRadio receiver and, then, present a design of a CMOS RFreceiver by focusing on the second-order intermodulation (IM2) distortion tolerance improvement. This work is carried out based on our prior work [17], which is expanded by adding a complex bandpass filter to realize a low-IF receiver and an IM2 calibration circuit to minimize the OOB OFDM-induced IM2 distortions.
# An Active-Clamp Forward Inverter Featuring Soft Switching and Electrical Isolation
## 3 Operation Principles
### State 3 (\(t_{2}<t<t_{3}\), Figure 6c)  
At the time \(t=t_{2}\), the primary voltage \(v_{p1}\) drops to be zero, so that both the forward diode \(D_{1}\) and the freewheel diode \(D_{2}\) are turned on. The forward diode current \(i_{D1}\) is gradually reduced, and the freewheel diode current \(i_{D2}\) is gradually increased. The equivalent circuit is shown in Figure 6c. When the voltage \(v_{ds1}\) increases, the leakage inductor current \(i_{Lr1}\) decreases and can be expressed as:  
\[i_{Lr1}(t)\ =\ \frac{i_{D1}(t)}{n}+i_{Lm1}(t). \tag{13}\]  
Figure 6: The equivalent circuits of each operating state during the positive half line-cycle. (**a**) State 1 [\(t_{0}<t<t_{1}\)], (**b**) state 2 [\(t_{1}<t<t_{2}\)], (**c**) state 3 [\(t_{2}<t<t_{3}\)], (**d**) state 4 [\(t_{3}<t<t_{4}\)], (**e**) state 5 [\(t_{4}<t<t_{5}\)], (**f**) state 6 [\(t_{5}<t<t_{6}\)], (**g**) state 7 [\(t_{6}<t<t_{7}\)], (**h**) state 8 [\(t_{7}<t<t_{8}\)], (i) state 9 [\(t_{8}<t<t_{9}\)].  
The magnetizing current \(i_{Lm1}\) remains unchanged. At this time, the voltage \(v_{ds1}\) and the leakage inductor current \(i_{Lr1}\) can be respectively expressed as:  
\[v_{ds1}(t)\ =\ V_{in}+i_{Lr1}(t_{2})Z_{2}\sin\ \omega_{2}(t-t_{2}), \tag{14}\]  
\[i_{Lr1}(t)\ =\ i_{Lr1}(t_{2})\cos\omega_{2}(t-t_{2}), \tag{15}\]  
where \(\omega_{2}\ =\ \frac{1}{\sqrt{L_{r1}C_{ds1}}}\), and \(Z_{2}\ =\ \sqrt{\frac{L_{r1}}{C_{ds1}}}\). When the voltage \(v_{ds1}\) arrives \(V_{in}/(1-d_{m1})\), the leakage inductor current \(i_{Lr1}\) stops charging the capacitor \(C_{ds1}\), and the circuit operation enters the next state.
## 4 Results and Discussion
### PV Integration into Net-Metering Schemes: Aquifer 23 Discussion  
By considering that PV solar installations for pumping groundwater purposes can be used more efficiently under net-metering schemes, significant economic benefits and environmental profits can be provided by these facilities. The proposed PV configurations allow reduction in the energy costs and subsequently the production costs, becoming more competitive without changing the profit margin. In addition, these solutions give rural areas an opportunity to maintain their population and, at the same time, reduce their economic dependency mainly based on subsidies. A remarkable reduction of emissions in the agricultural sector can also be achieved. According to the annual benefit estimation for the self-consumption and net-metering schemes previously described are summarized in Figure 12, it is possible to extrapolate the data to the rest of the aquifer (Aquifer 23). In this way, we consider the point where the concentration of wells is larger: within zones Z1 and Z2, accounting for 58% of the wells. If communities of irrigators of 800 ha, such as an existing one of this size, were connected to the grid, considering the average aquifer depth in those zones, emissions would be reduced in a more than relevant way. As previously discussed, after implementing a PV power plant connected to the grid in a community of irrigators, the economic benefits are highly dependent on the specific crop water need and the aquifer depth, which corresponds to 50,000 to 90,000 Euro in Z1 and 100,000 to 140,000 Euro in Z2, based on a net-metering scheme excluding taxes and fees; and from 28,000 to 50,000 euros in Z1 and 40,000 to 90,000 euros in Z2 according to current legislation in Spain (RD900/2015). Extrapolating the economic benefits for the entire aquifer, direct benefits to farmers of between 8 and 13 million Euro could be achieved in Z1, and between 3 and 4 million Euro in Z2, in accordance with a preliminary net-metering scheme without taxes and fees. However, with the current legislation in Spain regarding self-consumption and net-metering, between 4 and 8 million Euro in Z1 and between 1 and 2.5 million  
Figure 11: PV installation connected to the grid: annual benefit estimation examples excluding taxes and fees (1500 m\({}^{3}\)/ha and 3000 m\({}^{3}\)/ha).  
Figure 12: PV installation connected to the grid: annual benefit estimation examples including taxes and fees (1500 m\({}^{3}\)/ha and 3000 m\({}^{3}\)/ha).  
Euro in Z2 would be estimated annually. Tables 4 and 5 summarizes the economic benefits from the corresponding net-metering schemes by Z1 and Z2 zones, respectively.  
Finally, Figure 13 summarizes the analyzed approaches considered in this work and the corresponding advantages from these different perspectives. Presently, to undertake projects and design aid for the promotion of new renewable technologies and energy efficiency in agriculture, the European Union promotes several programs along this line under the European Agricultural Fund for Rural Development (EAFRD), to which is added the Green Fund for Climate [60] for other countries.
## 3 Engineering Overview and Model Establishment
### Calculation Model and Parameters of Bearing Capacity of Dam Body
#### 3.3.2 Model and Parameters  
The RCC gravity dam [30], which has the same height as the CSGR dam of Xijiang, is selected for comparison; the downstream slope ratio of the dam section is 1:0.7, and the upstream surface is upright. The section model information of the two types of dams is shown in Figure 6. The dam body and its foundation are consistent with those in Section 3.2. Both types of dams consider loads from the maximum water level of the upstream surface, the upstream reservoir water pressure, the maximum water level of the downstream surface, the downstream reservoir water pressure, and the weight of the dam body, and both adopt the head overload calculation method. The calculation parameters of the dam body and the foundation are shown in Table 6, among which the shear parameters of the CSGR dam (weathered material) come from the field material test [31], and those of CSGR dam (unweathered material) and RCC gravity dam are referred to in the literature [15].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Type** & **Region** & **Density (kg/m\({}^{3}\))** & \(E\) (GPa)** & \(\
u\) & **Friction Coefficient** & **Cohesion (MPa)** \\ \hline CSGR (weathered material) & Dam body & 2400 & mat-5 & 0.2 & 0.75 & 0.8 \\  & Foundation & 2690 & 10 & 0.25 & 0.87 & 0.6 \\ CSGR (unweathered material) & Dam body & 2400 & mat-5 & 0.2 & 1.0 & 0.9 \\  & Foundation & 2690 & 10 & 0.25 & 0.87 & 0.6 \\ RCC gravity dam & Dam body & 2400 & 28 & 0.167 & 1.3 & 1.2 \\  & Foundation & 2400 & 20 & 0.2 & 1.0 & 0.9 \\ \hline \end{tabular}
\end{table}
Table 6: List of the overload calculation parameters.  
Figure 5: General view of yield criterion.  
Figure 6: Two types of dam sections (unit: m).
## 3 Data Sources and Research Methods
### Research Methodology
#### 3.2.5 Coupling Coordination Model  
The coupling degree is an important index used to measure the degree of interaction between two or more elements in a system. Due to the large difference of the selected indicators, the data was standardized and calculated with the coupling degree formula.  
\[C=2\sqrt{\frac{U_{1}U_{2}}{\left(U_{1}+U_{2}\right)^{2}}} \tag{6}\]  
Here, \(C\) is the coupling degree; \(U_{1}\) and \(U_{2}\) represent the values of passenger transport and freight transport, respectively (after the total amount of correlation strength is standardized). In special cases, coupling degree has difficulty reflecting the overall degree and synergistic effect of regional accessibility and economic connection strength. Indicators for different cities all have dynamic, staggered, and uncoordinated characteristics, and there may be errors in evaluating synergies only based on the coupling degree. Therefore, the coupling coordination degree model is further applied, and the formula is as follows:  
\[D=\sqrt{CT} \tag{7}\]  
\[T=xU_{1}+yU_{2} \tag{8}\]  
\(D\) is the coupling coordination degree, and \(D\) (0, 1). \(T\) is the harmonic coefficient of passenger and freight transportation correlation strength, reflecting their contribution level to the coupling coordination degree. The values \(x\) and \(y\) represent the weights of the passenger and freight transportation correlation strength, respectively; \(x+y=1\) and its value can be calculated with the entropy weight method.