esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial-Temporal Variability of Future Rainfall Erosivity and Its Impact on Soil Loss Risk in Kenya
## 3 Results
### Baseline Soil Erosion by Kenya Regions and Basins  
The Coastal and Rift Valley regions had mean annual erosion rates below 10 t ha\({}^{-1}\) yr\({}^{-1}\) as demonstrated by the recent regional works by [PERSON] et al. (2019) and [PERSON] et al. (2017) (Table 8). The Lake Victoria region, which is characterized by a high mean annual precipitation (tropical rainforest climate zone) and mostly under croplands, recorded the highest soil loss rate of 25.25 t ha\({}^{-1}\) yr\({}^{-1}\) (Table 9). Out of the five basins that form the drainage landscape of Kenya, the Lake Victoria basin was shown to have the highest soil erosion rate, followed by the Great Rift Valley and Tana River basins, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**ACZ**} & **Area** & **MARE (Baseline)** & **MASLR (Baseline)** \\  & **(10\({}^{4}\) ha)** & **(MJ mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\))** & **(t ha\({}^{-1}\) yr\({}^{-1}\))** \\ \hline Humid & 266.5 & 9100.7 & 36.18 \\ Sub-Humid & 281.4 & 5265.1 & 19.25 \\ Semi-Humid & 271.5 & 3829.6 & 13.34 \\ Semi-Humid to semi-arid & 347.8 & 2818.7 & 7.25 \\ Semi-arid & 868.4 & 1856.9 & 3.60 \\ Arid & 1257.5 & 1327.4 & 1.50 \\ Very arid & 2533.3 & 643.5 & 0.60 \\ \hline \end{tabular}
\end{table}
Table 6: Baseline soil erosion rates in different Agro-Climatic Zones in Kenya.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**LULC**} & **Area** & **MARE (Baseline)** & **MASLR (Baseline)** \\  & **(10\({}^{4}\) ha)** & **(MJ mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\))** & **(t ha\({}^{-1}\) yr\({}^{-1}\))** \\ \hline Forest & 453.0 & 3155.5 & 2.41 \\ Shrubland & 1757.7 & 1246.0 & 1.84 \\ Grassland & 2169.7 & 1115.3 & 1.39 \\ Cropland & 1045.4 & 4069.7 & 17.86 \\ Aquatic vegetation & 5.0 & 1660.5 & 1.62 \\ Sparse vegetation & 105.1 & 512.5 & 2.62 \\ Bareland & 156.6 & 450.0 & 3.39 \\ Urban areas & 13.3 & 4256.2 & 2.24 \\ \hline \end{tabular}
\end{table}
Table 5: Baseline soil erosion rates per LULC in Kenya.
## 3 Methods
### Data Description  
We used a publicly available TDBRAIN dataset [39]. The dataset contains resting state raw EEG signals with relevant clinical and demographic data collected from psychiatric patients between 2001 and 2021. The dataset contains 1274 EEG signals (with a sampling frequency of 500 Hz) for psychiatric patients (aged 5 to 89 years). It includes both formal diagnoses and referral indications (unofficial diagnoses). The raw EEG signals have 26-channel (10-10 Ag/AgCl electrodes) recordings with eyes open (EO) and eyes closed (EC) for 2 min each. The data contain both healthy and several disorders such as major depressive disorder (MDD), attention deficit hyperactivity disorder (ADHD), subjective memory complaints, obsessive-compulsive disorder, burnout, dyslexia, chronic pain, Parkinson's, tinnitus, insomnia, and migraine as indications. In this study, we only analyzed the subjects that matched between each indication and formal diagnosis. Collected during eyes open (EO) and eyes closed (EC) tasks, the data in different task conditions are referred to as EO-data and EC-data throughout the paper for clarity. We analyzed 23 channels (C3, C4, CP3, CP4, CPz, F3, F4, F7, Cz, F8, P3, P4, FC3, FC4, FCz, Fp2, Fz, O1, O2, Oz, P8, T7, and T8). Given the prevalence of subjects categorized as 'Unknown' for formal diagnoses, we only considered the data associated with seven disabilities: 'burnout', 'dyslexia', 'chronic pain', 'MDD', 'OCD', 'ADHD', Parkinson's disease', and 'healthy' condition (see Table 1).  
Due to the existence of numerous disorder types in the dataset, we analyzed the data by categorizing them into two groups: 'normal (healthy)' and 'abnormal (disabilities)'.
# A Low Sampling Rate Receiver Design for Multi-Antenna Multi-User OFDM Systems
## 3 Proposed Low Sampling Rate Design
### Precoding  
Since \(\mathbf{D}\) is rank deficient, we could not perform a transform \(\mathbf{D}^{\dagger}\) on \(\tilde{\mathbf{y}}_{k}\) to recover data, as we could in (9). Thus, we resort to precoding.  
We define:  
\[C_{k,m}=\mathbf{D}\text{diag}\{\mathbf{\tilde{H}}_{k,m}\}, \tag{20}\]  
for \(k=1,2,\cdots,K\) and \(m=1,2,\cdots,M\). Then, (18) could be rewritten as:  
\[\tilde{\mathbf{y}}_{k}=\sum_{m=1}^{M}\mathbf{C}_{k,m}\mathbf{x}_{m}+\tilde{\mathbf{z}}_{k}, \tag{21}\]  
for \(k=1,2,\cdots,K\). We further define:  
\[\mathbf{x}= \text{vect}(\mathbf{x}_{1},\mathbf{x}_{2},\cdots,\mathbf{x}_{M})\in\mathbb{C} ^{MN}, \tag{22}\] \[\tilde{\mathbf{y}}= \text{vect}(\tilde{\mathbf{y}}_{1},\tilde{\mathbf{y}}_{2},\cdots,\tilde{ \mathbf{y}}_{K})\in\mathbb{C}^{N},\] (23) \[\tilde{\mathbf{z}}= \text{vect}(\tilde{\mathbf{z}}_{1},\tilde{\mathbf{z}}_{2},\cdots,\tilde{ \mathbf{z}}_{K})\in\mathbb{C}^{N},\] (24) \[\mathbf{C}= \left(\begin{array}{cccc}\mathbf{C}_{1,1}&\mathbf{C}_{1,2}&\cdots&\mathbf{C }_{1,M}\\ \mathbf{C}_{2,1}&\mathbf{C}_{2,2}&\cdots&\mathbf{C}_{2,M}\\ \vdots&\vdots&\ddots&\vdots\\ \mathbf{C}_{K,1}&\mathbf{C}_{K,2}&\cdots&\mathbf{C}_{K,M}\end{array}\right)\in\mathbb{C} ^{N\times MN}. \tag{25}\]  
Then, the signals received by all users could be compactly written as:  
\[\tilde{\mathbf{y}}= \mathbf{C}\mathbf{x}+\tilde{\mathbf{z}} \tag{26}\] \[= \mathbf{C}\mathbf{W}\mathbf{d}+\tilde{\mathbf{z}}, \tag{27}\]  
where \(\mathbf{W}\in\mathbb{C}^{MN\times N}\) is the precoding matrix. \(\mathbf{d}=\text{vect}(\mathbf{d}_{1},\mathbf{d}_{2},\cdots,\mathbf{d}_{k})\), in which \(\mathbf{d}_{k}\in\Omega^{V}\) is the data symbol intended for user \(k\).  
Equation (27) indicates that the whole system in Figure 2 could be regarded as an virtual MIMO system with \(\mathbf{C}\) as its channel gain matrix. Through the appropriate design of \(\mathbf{W}\), the interference among users could be canceled out at each receiver.  
For simplicity, the transmit zero-forcing [36] precoding is considered here. The ZF precoder is obtained by:  
\[\mathbf{W}_{\text{ZF}}=c\mathbf{C}^{\dagger}\Big{(}\mathbf{C}\mathbf{C}^{\dagger}\Big{)}^{-1}, \tag{28}\]where \(c\) is the normalization factor:  
\[c=\sqrt{\frac{N}{\text{tr}\bigg{(}\mathbf{C}^{\dagger}\big{(}\mathbf{C}\mathbf{C}^{\dagger} \big{)}^{-1}\bigg{(}\mathbf{C}^{\dagger}\big{(}\mathbf{C}\mathbf{C}^{\dagger}\big{)}^{-1} \bigg{)}^{\dagger}\bigg{)}}}, \tag{29}\]  
so that \(\mathbb{E}[\|\mathbf{W}_{\text{ZF}}\,\mathbf{x}\|^{2}]=\mathbb{E}[\|\mathbf{x}\|^{2}]\).
## 2 Laboratory Model Test
### Laboratory Model Test Procedure  
In this study, a laboratory model test was conducted for 12 cases based on vertical pile-tunnel offsets of 1.0 D, 2.0 D, and 3.0 D and horizontal pile-tunnel offsets of 0 D, 1.0 D, 2.0 D, and 3.0 D (Figure 11). In the names of the cases, the first number represents the vertical offset, and the second number represents the horizontal offset.  
The calibration results above, as well as the allowable and ultimate loads obtained from the P-S curve, were applied to the 12 cases. The laboratory model test was divided into a preparation step, a test step, and a calculation step. In the preparation step, sandy soil was prepared in the chamber, and the parameters of the model tunnel and model pile were set. In addition, a linear variable differential transformer (LVDT) was installed to measure surface settlement, and the strain gauges of the model tunnel were connected to the data logger. In the test step, surcharge loads (allowable and ultimate loads) were applied to the model pile in sequence, and the surface settlement and strain gauge values of the model tunnel were measured. Finally, in the calculation step, the data obtained in the test step were converted and analyzed to complete the laboratory model test. Figure 12 shows each step of the test procedure.  
Figure 11: Cases for model test in this study.  
Figure 10: P-S curve for model test. \(q_{u}\): ultimate load.
## 3 Numerical Analysis
### Effects of Permittivity Tensor on Propagation Characteristics of Surface Waves  
The effects of the relative permittivity tensor on propagation characteristics of surface waves are investigated. Based on Equations (19)-(21) and (36)-(38), the corresponding values of \(\beta_{x}/k_{0}\) and \(\mid\beta_{x}/R_{y}\mid\) as a function of slab thickness are calculated. As an instance, the calculated frequency is set as \(f\) = 16 GHz, and the slab width is \(2a\) = 56.25 mm.  
Figure 5a,d show the effects of \(\varepsilon_{x}\) on \(\beta_{x}/k_{0}\) and \(\mid\beta_{x}/R_{y}\mid\) as a function of slab thickness. It is seen that when both \(\varepsilon_{y}\) and \(\varepsilon_{z}\) remain to be 2.45 and the value of \(\varepsilon_{x}\) varies from 2.45 to 3.15, \(\beta_{x}/k_{0}\), and \(\mid\beta_{x}/R_{y}\mid\) are almost stable for quasi-TE modes. However, the corresponding values of quasi-TM modes slightly change with the increase of \(\varepsilon_{x}\). Similarly, the effects of \(\varepsilon_{y}\) and \(\varepsilon_{z}\) on the propagation characteristics of surface waves are also investigated. Figure 5b,e illustrate that slightly increasing \(\varepsilon_{y}\) results in a significant increase of \(\beta_{x}/k_{0}\) and \(\mid\beta_{x}/R_{y}\mid\) for quasi-TE modes, while the quasi-TM modes are nearly unaffected. In contrast, as shown in Figure 5c,f, the value of \(\varepsilon_{z}\) can significantly affect the propagation characteristics of quasi-TM modes, but hardly contributes to the quasi-TE modes. Since most of the substrates exhibit a nonnegligible amount of anisotropy during the process of fabrication, these effects should be seriously taken into account for engineering applications. It should be mentioned that although the relative permittivity tensor can affect the propagation constants along both \(x\) and \(y\) directions, the slab width plays a major role in affecting the value of \(\mid\beta_{x}/R_{y}\mid\), as displayed in Figure 4b. In addition, we also employ the other two methods discussed above to analyze these effects as well as to validate our derivation further. The phenomena are in good agreement by using different methods, and these discussions are omitted here for the sake of brevity.
## 3 Experiments and Configuration Detail
### Evaluation Metrics  
The accuracy and the F1 score were used as the primary metrics to evaluate the classification performance of the networks. These metrics are calculated based on the confusion matrix, as shown in Table 3.  
In addition, the complexity of the models was measured in terms of the \"Parameters\", \"Complexity\" and \"Weights\". \"Parameters\" represents the total number of participants in the model. \"Complexity\" represents the model's computational complexity, commonly expressed in GFLOPs. \"Weight\" represents the size of the file where the model's weights are stored.  
In this equation, true positive (TP) denotes the number of correctly classified crack images; false positive (FP) denotes the number of background images, which are misclassified as crack images; false negative (FN) denotes the number of crack images, which are misclassified as background images; and true negative (TN) denotes the number of correctly classified background images.  
\[Accuracy=\frac{TP+TN}{TP+TN+FN+FP} \tag{2}\]  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]  
\[Recall=\frac{TP}{TP+FN} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Image**} & \multicolumn{3}{c}{**Prediction**} \\ \cline{2-4}  & & **Crack** & **Background** \\ \hline \multirow{2}{*}{Label} & Crack & TP & FN \\  & Background & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix.  
\[F1\text{-}Score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{5}\]
# A Reflection-Type Dual-Band Phase Shifter with an Independently Tunable Phase
## 2 Design Method of Proposed Dual-Band Phase Shifter
### Parasitic Susceptance Compensation
#### 2.1.1 Susceptance Compensation at Low-Band Operation  
In the \(f_{\rm L}\) operation, the co-operating band can be represented by \(f_{\rm H}\). A \(\lambda/4\) shunt open stub for \(f_{\rm H}\) will provide a short-circuited condition at node A. However, a \(\lambda/4\) shunt open stub will act as a capacitive component at \(f_{\rm L}\) because \(f_{\rm L}<f_{\rm H}\). To compensate for this additional susceptance, the shunt inductor (\(L_{\rm c}\)) is connected at node A, as shown in Figure 2a. The input admittances of the \(\lambda/4\) shunt open stub and \(L_{\rm c}\) when observed from node A can be expressed as (1) and (2):  
\[Y_{\rm inA}^{\rm stub}=jY_{\rm Hs}\tan\biggl{(}\frac{\pi f_{\rm L}}{2f_{\rm H} }\biggr{)} \tag{1}\]  
\[Y_{\rm inA}^{\rm inductor}=\frac{1}{j2\pi f_{\rm L}L_{\rm c}} \tag{2}\]  
To compensate for the parasitic effect of a \(\lambda/4\) shunt open stub, Equations (1) and (2) must be conjugated. The required \(L_{\rm c}\) is calculated using Equation (3):  
\[L_{\rm c}=\frac{1}{2\pi f_{\rm L}Y_{\rm Hs}\tan\biggl{(}\frac{\pi f_{\rm L}}{ 2f_{\rm H}}\biggr{)}} \tag{3}\]
# Low-Cost RSAC and Adsorption Characteristics in the Removal of Copper Ions from Wastewater
## 3 Results and Discussion  
### Adsorption Experiments  
#### 3.1.1 Effect of Adsorption Time  
The concentration of the residual copper ion in the supernatant of each sample as a function of adsorption time is shown in Figure 4. The residual copper ion concentration showed a similar trend for different dosage amounts. At the beginning of the adsorption reaction (0-12 h), the residual copper ion concentration decreased significantly with the increasing time; at the middle of the adsorption reaction (12-48 h), the residual copper ion concentration decreased slowly with time; at the end of the adsorption reaction (48-72 h), the adsorption equilibrium state was reached. The reason for this trend could be that the initial adsorption occurs mainly on the surface and in the pores of RSAC particles. In the initial adsorption stage, there are many active sites on the surface and in the pores of RSAC particles so copper ions could occupy the active sites rapidly and show the characteristics of a fast adsorption rate [22]. With the extension of time and the increase of adsorption capacity, the active sites become fewer [23]. It was also found that there were large functional groups on the surface of FA and MFA, such as O-H, C=C, and Si-O-Si, which played a crucial role in the process of adsorption of heavy metal ions [24]. It could be inferred that the adsorption equilibrium time of copper ions on RSAC particles is 48 h. This is consistent with the experimental results to explore the optimal ratio of RSAC particles. Therefore, the adsorption time could be set as 48 h in the subsequent static adsorption experiments.
## 4 Discussion
Since the cycle lengths were more uniform than the dynamic ranges, \(PPQ3\) did not change statistically significantly but \(APQ3\) did. Figure 8: (**a**) Dynamic ranges of ten consecutive cycles (bright blue line). _Amplitude Perturbation Quotient_-3% (APQ3) and _Mean Shimmer_ (MShim) are calculated for different intervals of the total range (red, green, dark blue and black line). (**b**) Cycle lengths of ten consecutive cycles (bright blue line). _Period Perturbation Quotient_-3% (PPQ3) and _Mean Jitter_ (MJit) are calculated for different intervals of the total range (red, green, dark blue and black line). The mean and maximum values and standard deviations for most parameters displayed consistent tendencies and changed most clearly between the shorter sequence lengths (for details see Table 2 and Figures 5-7). Minimum values usually increased with an increasing sequence length without reaching a stable region. The instability of the minimum values for all parameters could be due to the rising probability of changes in phonation with increasing sequence length. Furthermore, it is noteworthy that all Perturbation Quotients (PPQ, APQ and EPQ) behaved clearly distinctively from the other parameters of their groups but rather similar in comparison to each other. This is because they are calculated using the same formula only for different input data [47]. However, except for _AVI_, none of these changes were found to be statistically significant for comparisons between sequence lengths of 20 cycles and longer sequences. Furthermore, even though systematic increases and decreases were often visually observed up to a sequence length of 50 or 100 cycles (see Table 2), the largest changes were observed for almost all parameters between shorter sequence lengths. Hence, we suggest avoiding smaller sequence lengths than 20 cycles for calculation of all GAW perturbation measures. Additionally, we suggest avoiding the use of the parameter AVI in general. We make this general suggestion because taking into account the observed often systematic behavior of the descriptive values, it is possible that other more subtle effects exist that were not significant in our analysis. To be able to make a more precise statement, it is necessary to confirm these findings for larger datasets and especially for subjects with vocal disorders.
## 7 Annotations on Topological Evaluation of HVdc Circuits  
From all of the above discussions, the following remarks are drawn on multi-terminal VSC-HVdc transmission system topologies:  
1. In the context of dc breakers, ST, GRT, and SGRT require a dc breaker for each WF and onshore SS (NWF + NSS). SSRT and WFRT demand that DCCB equals the number of WFs (NWFs) while PPT does not need dc breakers (not an MT-HVdc topology). 2 WF-1 SST and proposed 2 WF-SSRT acquire dc breakers equal to the number of substations (NSS). The fewer the requirements of dc breakers, the more economical is the topology. Thus, 2 WF-1 SST and 2 WF-SSRT are more economical configurations than all of them. 2. No extra offshore SS is required for all discussed topologies except ST and SGRT for connections and dc breakers' installations. 3. \(P_{max\_fail}\) criterion imposed by GBSQSS is satisfied during all cases but for a permanent dc fault within WF/SS ring of WFRT/SSRT, on a central star node of star topology and during onshore converter disconnection and dc-link fault within remote/far-end/the last unit (unit-1 and unit-3 of Figure 5) of 2 WF-1 SST is not satisfied. Stability and flexibility of the MT-HVdc grid are on the stake in the event of permanent failure of GS-VSC station in WFRT, SSRT, ST, SGRT, and GRT, unlike the proposed 2 WF-SSRT. Simulations show that 2 WF-SSRT is the most stable configuration as it satisfies all operating conditions of GBSQSS. 4. Proposed 2 WF-SSRT cuts the complexities such as operation and control approach and the protection scheme because of the standalone operation of each unit of 2 WF-SSRT without mediation and burden of the power converters, UFMA, and DCCBs from the adjoining units during the steady-state operation. However, the impact of all VSC stations, UFMAs, dc cables, and DCCBs of a configuration needs to be considered while designing/selecting control and operation, and a protection approach for all other topologies, which proves that 2 WF-SSRT is a practical configuration. Comparative evaluation and detailed remarks on MT-HVdc grid topologies are summarized in Table 4[27; 28; 29; 30]. Figure 15: Permanent dc line–line fault F2 on cable L13 within substation ring.
## 4 Conclusions  
In this study, correlations of environmental and material workability conditions including temperature, viscosity, cohesive force, adhesion strength, toughness, and water pressure resistance were analyzed to present quality standards for proper filling amount and thickness management for on-site quality control when using SPRG leak repair materials. Based on presented analysis, a theory was presented that can determine the water pressure resistance according to the change in the underground depth according to the viscosity and thickness, and the conclusion of this study is as follows:  
1. In this study, an experimental method to reproduce the behavior of cracks injected with SPRG type repair materials was presented. The adhesion and elongation of the leak repair material obtained through this experiment were interpreted as 'toughness' and 'water pressure resistance', and a theory that can be viewed as an equivalent physical force was presented;
2. Based on this method, a correlation between viscosity, cohesive strength, toughness, and hydraulic resistance of SPRG used as a leak crack repair material for concrete structures was presented. Based on this analysis, study results suggested the appropriate thickness to resist the water pressure in the underground depth for SPRG of high viscosity, medium viscosity, and low viscosity. In this regard, it was found that a thickness of about 28 mm or more was required for high-viscosity SPRG at 100 m underground, and it was found that even with the same thickness, a high-viscosity material can resist higher water pressure. Limited within the scope of this testing and the SPRG types investigated, it was found that the SPRG types from the highest hydraulic resistance to lowest hydraulic resistance is Type C > Type B > and Type A;
3. This study demonstrates that an evaluation method (test method) can be made possible to evaluate leakage repair materials and their hydraulic resistance performance in various underground water pressure conditions. Therefore, in the future, the work to use the correlation of viscosity, toughness, and water pressure resistance of the repair materials presented in this study as a practical evaluation standard will be promoted.  
Conceptualization, H.S., K.O., J.L. and B.K.; experimental plan, H.S., K.O. and J.L.; data curation, H.S., K.O. and B.K.; formal analysis, H.S., K.O. and B.K.; investigation, H.S., K.O., J.B. and B.K.; methodology, H.S., K.O., J.B., B.K. and S.O.; project administration, J.L., B.K. and S.O.; resources, H.S., K.O. and B.K.; supervision, S.O.; writing--original draft preparation, H.S., K.O. and S.O.; writing--review and editing, H.S., B.K. and S.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Major Technical Innovation Project in Hubei Province of China (2022 BCA082).  
Not applicable.  
The authors declare no conflict of interest.