esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Influence of Geometric Parameter  
### Diameter of Straight Section  
According to the definition in Figures 2 and 3, the influence of the straight-section diameter on support interference is shown in Figure 7 and Table 4. The results in the figure are the difference between the \"BWB aircraft with different V-shaped support\" and the \"BWB aircraft without any support\". In the figure, the difference of lift and drag with different straight-section diameters were almost negligible. As the diameter of the straight section increased from 44 mm to 55 mm, the magnitude of \(|\Delta C_{m}|\) decreased. When the attack angle was \(0^{\circ}\), the \(\Delta C_{m}\) achieved a maximum value of 0.0042, which was close to 60%, as shown in Table 4.  
Figure 6: Pressure contour of the BWB aircraft lower surface with and without the V-shaped support (\(Ma=0.75\), \(\alpha=0^{\circ}\)): (**a**) pressure contour of the real model without support or cavity; (**b**) pressure contour of the test model with the V-shaped support (D52_L300_L2_P15) and cavity.  
Figure 7: Support interference results from the BWB aircraft with different straight-section diameters (D = 44 mm/52 mm/55 mm, \(Ma=0.75\)): (**a**) \(C_{L}\)-\(\alpha\) curves; (**b**) \(C_{D}\)-\(\alpha\) curves; (**c**) \(C_{m}\)-\(\alpha\) curves.  
The pressure distribution near the cavity of the BWB aircraft with different straight-section diameters is shown in Figure 8. The typical point A/B/C and profile F-F was chosen in the sensitive area of the cavity (as shown in Figure 8a). The pressure characteristics of the sensitive area were reflected by the variation of the pressure coefficients of profile F-F and point A/B/C. The pressure coefficient of point A/B/C with different straight-section diameters is shown in Table 5. As the straight-section diameter increases from 44 mm to 55 mm, the pressure coefficients of point A/B/C and profile F-F (as shown in Figure 8b) decreased. The sensitive area is close to the trailing edge of the fuselage; therefore, the BWB aircraft was influenced by the positive pitching moment and the magnitude of negative interference in Figure 7c decreased.  
As the straight-section diameter increased, the pitching moment interference of the V-shaped support on the BWB aircraft was reduced. The interference influence of lift and drag were negligible with different straight-section diameters.
## 1 Introduction
The results showed that mortars made with dried and crushed cement slurry (d = 75 \(\upmu\)m), as a partial substitute for cement (10% replacement), showed a compressive strength comparable to that of the mortar totally produced with Portland cement. Instead, the mortar with a replacement rate of 20% showed a 7% strength reduction compared to the reference due to the high calcite content. As aforementioned, the cement sludge was also tested as an additive for soil stabilization, and two addition rates of sludge in soil were examined: 10% and 20%. The results showed a significant increase in the California bearing ratio (CBR) value of the soils treated with cement sludge but a greater sensitivity to water variations compared to soils without cement sludge. However, the authors stated that this component can be used as a potential soil stabilization material. In this context, the first goal of this paper is to propose a new innovative technology for the treatment of returned concrete that allows to obtain new quality products that can be reused in the production of concrete. In particular, by means of physical and mechanical processes, washed fine (d \(\leq\) 5 mm) and coarse (d \(\geq\) 5 mm) recycled aggregates, microfiltered water and cement sludge are produced. As stated above, the second goal is to investigate the technical and environmental feasibility of using these products as new raw materials for concrete production. To achieve this goal, several experimental tests are carried out on fine and coarse recycled aggregates (e.g., particle size distribution, particle density and water absorption, alkali-silica reactivity, water-soluble chloride content, acid-soluble sulphates, and leaching test), microfiltered water (e.g., suspended solids content, traces of oils, fats or detergents, chlorides, sulphates), cement sludge (e.g., moisture and dry matter content, isothermal conduction calorimetry test) and, finally, concrete mixtures (i.e., uniaxial compression strength and hygrometric shrinkage). Given the high quality level, all these components can be used as new raw materials for concrete production. This permits significant environmental benefits, including the conservation of natural aggregates and the avoidance of further waste disposal in landfills. This technology is also easy to use, as it is directly installed at the batching plant. The innovation of the proposed system is also related to its efficiency in terms of energy consumption. In fact, compared to a traditional recycling plant consisting of crushing, washing, and screening operations that consume over 6 kWh of electricity per ton of treated material [31], the proposed solution allows for savings of over 10% in terms of energy consumption (5.5 kWh/t). In this context, in order to verify the effective quality of the components obtained downstream of the treatment process proposed in this study, their technical and environmental suitability is experimentally assessed according to the performance requirements imposed by technical regulations.
# A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
## 2 Materials and Methods
### Feature Extraction
#### 2.4.3 The Number of Visible Satellites Vary in Weight  
IO transitions are a continuous process. It is worth noting that time sequence features contain more useful information. Figure 8 demonstrates the changes in the number of visible satellites under different devices when switching between indoor and outdoor scenarios. Due to the weak satellite signal in the indoor scenarios, the number of satellites detected by the terminals is low. When the smartphones switch from indoors to outdoors, the number of visible satellites increases rapidly. However, when switching from outdoors to indoors, it can be seen that the satellite is in the tracking status, and the receiver conducts targeted integration and other processing on the tracked satellites, the terminal can still detect these satellites within a certain period.  
Figure 7: The cumulative distribution probability of the number of satellites in the distribution of satellite azimuth using different smartphones in the range of different angles in a week.  
To mitigate the effects of signal diversity in a different environment and alleviate the influences of device heterogeneity on GNSS measurements, we utilized the number of satellites at the current time to calculate the weight of the change of the number of satellites. Inspired by the changes in the number of visible satellites, we defined GS_Num_w\({}_{t_{1}-t_{2}}\) as follows:  
\[\text{GS\_Num\_w}_{t_{1}-t_{2}}=\frac{cnt_{t_{1}}-cnt_{t_{2}}}{cnt_{t_{1}}} \tag{9}\]  
where \(cnt_{t_{1}}\) denotes the number of satellites at current time \(t\), \(cnt_{t_{2}}\) denotes the number of satellites at time \(t\)-\(k\). \(k\) denotes the window size (e.g., 1, 2, 3). In the experiment, we compared the influence of the feature under different window size on indoor and outdoor detection.
## 3 Results and Discussion
### Spectroscopic Analysis  
Figure 4 exhibits the PCA graph, which is centered on the mean and generated from the spectra obtained through FT-NIR analysis. The algorithm applied combines the Savitzky-Golay filter with singular value decomposition and utilizes the cross-validation method with 20 segments for analyzing all acacia leaf samples.  
This unsupervised multivariate statistical analysis shows that this promising technique can distinguish leaf raw materials by their distinct characteristics, including mineral elements, phenolic profiles, and flavonoid content. The results demonstrate a clear separation between species observed by the different leaves analyzed. Therefore, FT-NIR spectroscopy has shown potential as a valuable technique for monitoring the composition  
Figure 3: Heat maps plotting clusters of nutritional composition groupings of AMy—_A. melanogaster_, AD—_A. desluba_, AC—_A. cyclops_, AR—_A. retinodes_, AMs—_A. mernusii_, AP—_A. pycuantha_, AL—_A. longifolia_, AS—_A. saligan_. Legend: H—humidity; NT—nitrogen total; Prot—protein; Hem—hemicellulose; Ash—ash; Fib—fiber; ADF—acid detergent fiber; ADL—acid detergent lignin; NDF—neutral detergent fiber; Cel—cellulose; Fat—fat; NFC—non-fibrous carbohydrates; Ca—calcium; Zn—zinc; Cd—cadmium; P—phosphorus; K—potassium; Na—sodium; Mg—magnesium; Mn—magnanese; Fe—iron; Cr—chromium; Cu—copper; Ni—nickel.  
of acacia leaves. Nevertheless, further research is required to have a more accurate result. The application of the PCA method is a helpful tool to distinguish between the composition of acacia leaves. These results align with similar ones obtained with FT-NIR analysis in previous studies [25], as well as with those obtained using FT-RAMAN [6; 44] and FTIR-ATR [45]. However, previous studies used plant extracts, whereas in this study, we aimed to determine whether we can assess species differentiation and quality using unprocessed, directly harvested leaves. Measuring solid samples with a rotating sample cup to obtaining more representative spectral information is only feasible with FT-NIR.  
The first two components can account for 78% of the variation, thus allowing us to distinguish between species.
# End to End Delay and Energy Consumption in a Two Tier Cluster Hierarchical Wireless Sensor Networks
## 6 The Inter-Cluster Communication
### The Embedded Markov Chain  
Let \(F(z)=\sum_{i=0}^{\infty}f_{i}z^{i}\) be the PGF of the number of packets that arrive to a tagged CH during a Combi-Frame. \(F(z)\) is composed of two factors; the local traffic, \(L(z)=\sum_{k=0}^{V}\pi_{C,k}z^{k}\), see Equation (9), and the exogenous traffic that comes from some CHs located in the neighboring outer ring. We observe the Combi-Frame of our tagged CH at the beginning of a \(\mathcal{T}\)-slot, see Table 6, the right column. The instants at which the \(\mathcal{T}\)-slots begins define an embedded Markov chain where the steady state probabilities \(\boldsymbol{\pi}_{T}=[\pi_{T,0},\pi_{T,1},\ldots]\) are given by the following PGF,  
\[\pi_{T}(z)=\sum_{k=0}^{\infty}\pi_{T,k}z^{k}=\frac{N_{msT}-1}{z^{N_{msT}}-F(z)} F(z), \tag{12}\]  
Due to the lack of space, we omit the derivation of Equation (12), which can be found in [24]. Here we just mention a few details. First, we remark that \(\pi_{T,k}\) is the probability, in steady-state, that at the beginning of a \(\mathcal{T}\) slot, our tagged CH has \(k\) data packets in its queue. Second, although the dimension of \(\boldsymbol{\pi}_{T}\) is infinity (compare with \(\boldsymbol{\pi}_{C}\)) \(\pi_{T}(z)\) is determined with a finite number of values, that is, \(\pi_{T,0},\pi_{T,1},\pi_{T,2},...\pi_{T,N_{msT}-1}\). Third, from [PERSON]'s theorem (see [35]) the denominator of Equation (12), \(z^{N_{msT}}-F(z)\), has exactly \(N_{msT}\) zeros in \(|z|\leq 1\). Since \(\pi_{T}(z)\) must be analytical in \(|z|\leq 1\), the zeros in the denominator must also be zeros in the numerator. Let us denote by \(\hat{r}_{0}=1,\hat{r}_{1},\hat{r}_{2},...\hat{r}_{N_{msT}-1}\) the set of \(N_{msT}\) roots of \(z^{N_{msT}}-F(z)\) in \(|z|\leq 1\), e.g. \(\hat{r}_{i}^{N_{msT}}-F(\hat{r}_{i})=0\), for \(i=0,1,2,\ldots,N_{msT}-1\). Then, the set of probabilities \(\pi_{T,0},\pi_{T,1},\pi_{T,2},...\pi_{T,N_{msT}-1}\) can be obtained by solving the following system of \(N_{msT}\) linear equations,  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(r_{max}\)} & \multicolumn{8}{c}{**Ring \(k\) (\#\(CH_{k}\))**} \\ \cline{2-11}  & **0 (1)** & **1 (6)** & **2 (12)** & **3 (18)** & **4 (24)** & **5 (30)** & **6 (36)** & **7 (42)** &... \\ \hline
**0** & \(1\) (sink) & & & & & & & & & \\
**1** & \(1+6\) & \(1  
\[\sum_{i=0}^{N_{msT}-1}(N_{msT}-i)\pi_{T,i}=N_{msT}-F^{\prime}(1)>0 \tag{13}\] \[\sum_{i=0}^{N_{msT}-1}(\hat{r}_{k}^{N_{msT}}-\hat{r}_{k}^{i})\pi_{T, i}=0;\quad k=1,2,3,\ldots N_{msT}-1\]  
Observe that the first equation in Equation (13) comes from the fact that \(\lim_{z\to 1}\pi_{T}(z)=1\) (I'Hopital rule) and the inequality is mandatory due to ergodicity conditions.
# Utilizing Graphite Waste from the Acheson Furnace as Anode Material in Lithium-Ion Batteries
## 2 Materials and Methods
### Fabrication of Lithium-Ion Batteries Cells and Electrochemical Characterization  
Coin-type LIB half-cells were fabricated using SGW9, SGW9@RCT, and CAM as the anode materials. The electrodes were prepared by mixing the active material, conductive additive, and binder in a ratio of 96:1:3. The electrode slurry was cast onto copper foil with 18 \(\upmu\)m and dried at 100 \({}^{\circ}\)C for 0.5 h. The coin cells were assembled in dry room with lithium metal as the counter electrode. The electrolyte used was 1 M LiPF\({}_{6}\) in EC/EMC = 30:70, and a PE-type was used to separate the electrodes.  
The cells were tested in half-cell configuration using a VMP-3 potentiostat (BioLogic Science Instruments, Claix, France). Galvanostatic charge-discharge cycling was performed at a C-rate of 0.1 C, and the voltage range was set to 0.005 V to 3.0 V. Initial Coulombic efficiency (ICE) was determined from the capacity ratio between 1 st charge and discharge (Equation (4)). Specific capacity was calculated based on the 3 rd discharge data.  
We have illustrated the entire experimental process in Figure 2.  
Figure 2: The experimental processes for sample preparation, cell fabrication, and evaluation.
## 4 Discussion
### Signal Selection and Emotion Model  
The literature [25] systematically reviewed the physiological signals employed in current research and concluded that internal physiological signals, such as EEG and ECG, are involuntarily activated and, therefore, more reliable for emotion recognition than body signals such as facial expressions and posture.  
Research has shown that when individuals experience emotions such as anger, fear, or happiness, the frequency and amplitude of their breathing increase involuntarily owing to the heightened energy demand of cells. Conversely, when individuals are in a state of sadness, relaxation, or calmness, their energy demand decreases, resulting in slower and shallower breathing. When individuals are startled, there may be a brief breathing interruption resulting in no RSP signal [47]. The RSP parameters also included vital capacity, respiration rhythm, and tidal volume.  
Our study indicates that high-valence emotions exhibit more stable and uniform RSP values than low-valence emotions. In terms of arousal differences, high-arousal states were characterized by a higher breathing frequency, whereas low-arousal states exhibited a lower breathing frequency. Low-arousal emotions are characterized by quick inhalations and slow exhalations with a prolonged ending [48].  
In this study, we used FaceReader as a comparative emotion recognition method. FaceReader captures facial images using a camera and detects emotions by analyzing the relative position changes of facial landmarks. This recognition method is different from the measurement approach of the millimeter-wave radar and is more direct, thereby providing a robust validation of the effectiveness of the proposed method. FaceReader has shown high accuracy in some literature [49]. However, in our study, we observed that the accuracy of the FaceReader had some discrepancies compared to the emotions expected to be elicited by the videos.  
We believe that this discrepancy is due to differences in cultural backgrounds and emotional expression styles, which can lead to recognition errors when FaceReader is used with East Asian individuals. This issue has also been discussed in the literature [50].  
To classify emotions, we referred primarily to [PERSON]'s two-dimensional emotion model using valence and arousal as classification parameters. There are three reasons for using this approach:  
1. Consistency with FaceReader: We aimed for our method to be as consistent as possible with FaceReader's classification method, making the comparison between the two more relevant and informative.
2. Simplified Modeling: Using valence and arousal simplifies the modeling process, as these dimensions are easily comparable.
3. Enhanced Research Depth: Our study builds on existing literature and further refines classification by providing a more detailed differentiation of emotions.
## 4 Numerical Examples  
To evaluate the proposed algorithm, we use the same numerical example shown in Section 2.2. A data set with 20 elements \(X=\{X_{1},X_{2},\ldots,X_{20}\}\), \(X_{i}\in R^{2}\), as seen in Table 1, is to be divided into two clusters. Implement the sSMC-FCM algorithms with \(M=2\) for the following 3 cases:  
* Case 1: unsupervised, \(M^{\prime}=M=2\) (standard FCM algorithm).
* Case 2: semi-supervised, attempting to place data points 9 and 10 into cluster 1, \(M^{\prime}=4\), \(M=2\).
* Case 3: semi-supervised, attempting to place data points 9 and 10 into cluster 1, \(M^{\prime}=8\), \(M=2\).  
The Euclidian distance was used in the calculations and results in this work. Table 3 shows membership matrices \(U\) for each case, as the results of clustering.  
From the results in Table 3, we have the following observations:  
* As \(M^{\prime}\) increases, the membership grades of the supervised elements increase. Initially, with no supervision, data points 9 and 10 were placed into cluster 2. With supervision and \(M^{\prime}=4\), their membership grades increased but not enough to move into cluster 1, while with \(M^{\prime}=8\), these two data points were successfully placed into cluster 1;
* In general, the sSMC-FCM algorithm converges after a similar number of iterations to the standard FCM algorithm. For instance, with \(\varepsilon=10^{-3}\), it required about 10 iterations;
* When \(M^{\prime}\) is changed, the cluster centers change to suit the supervised elements. For instance, case 1 has cluster centers \(V_{1}=(2.719,5.000)\), \(V_{2}=(9.018,5.000)\), case 2 has cluster centers \(V_{1}=(2.735,5.000)\), \(V_{2}=(9.200,5.000)\), and case 3 has cluster centers  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**i** & **Unsupervised** & **i** & \(M\)**=4** & **i** & \(M\)**=8** \\ \hline  
\hline \end{tabular}
\end{table}
Table 3: The resulting membership \(U\) for the 3 cases using the sSMC-FCM algorithm.  
\(V_{1}=(2.714,5.000)\), \(V_{2}=(9.303,5.000)\). We can observe that, from case 1 to 2, the cluster center \(V_{2}\) moves further away from data points 9 and 10;
* Compared to the sSFCM algorithm, we can see that the matrices \(U\) in the case of \(M^{\prime}=4\), \(M=2\) using the sSMC-FCM algorithm shown in Table 3 are similar to the corresponding matrices \(U\) in the case of \(\overline{U}_{9,1}=\overline{U}_{10,1}=0.3\) using the sSFCM algorithm shown in Table 2. Both cases were able to increase the membership grade of the points to belong to cluster 1 but were not successfully in moving the points into cluster 1;
* In future works, it is possible to expand on other different representations for the fuzzification coefficients, combining with hedge algebra [17; 18; 19] in new representations and calculations.
# Game Algorithm for Resource Allocation Based on Intelligent Gradient in HetNet
## 2 System Model
The SINR can be calculated as follows:  
\[SINR_{F_{ij}}=\frac{P_{F_{j},F_{j},i}}{P_{m,F_{j},i}+\sum_{k\
eq j}P_{F_{k},F_ {j},i}+N_{0}} \tag{2}\]  
where \(P_{F_{j},F_{j},i}\) is the received signal power from \(F_{j}\) to user \(F_{j,i}\) which is calculated as \(P_{F_{j},F_{j},i}=P_{F_{j}}\cdot 10^{-\frac{127+30\log_{10}\left(\left(x_{f}-x_{j} \right)^{2}+\left(y_{f}-y_{j}\right)^{2}\right)^{1/2}}{10}}\). Similar to the calculation signal power from \(m\) and \(F_{j}\) to user \(F_{j,i}\). which are calculated as \(P_{m,F_{j,i}}=P_{m}\cdot 10^{-\frac{128+37\varepsilon\cdot\log_{10}{(({F_{j}}^{2}+y _{j}}^{2})^{1/2}}{10}}\) and \(P_{F_{k},F_{j,i}}=P_{F_{k}}\cdot 10^{-\frac{128+37\varepsilon\cdot\log_{10}{(({F_{j}} ^{2}+y_{j}}^{2})^{1/2}}{10}}\). When SINR of users (calculated as Equation (1) or (2)) is lower than the SINR threshold \(c_{th}\), where \(c_{th}\) is a predefined threshold which has been discussed in [28], it is held that femtocell has sufficient interference on macro base station users, with strong interference link existing. At the moment, to ensure the improvement of overall system performance, we adopt the thought similar to community expansion, i.e., commanding the macro base station to allocate its users under severe interference to femtocells nearby and transferring partial spectrum resources to femtocells to serve the users allocated. The diagram is as follows. The gray area in Figure 2 is the existing service area of femtocell, and MUE4 is a macro user under serious cross-layer interference. To eliminate cross-layer interference there, the macro base station divides frequency spectrum with \(\alpha\cdot W\) bands for use of the femtocell, so as to expand its existing service area with a factor of proportionality of \(\beta\), where \(\alpha\) is spectrum bias transferred by macro base station. Femtocell will include users under serious interference into family users and uninstall the partial load of macro base station to uplift the overall performance of the system. Expanded service area is indicated in blue as in the Figure 2. In this way, user \(F_{j,i}\) served by \(\alpha\cdot W\) spectrum will no longer be subject to cross-layer interference. The computation formula for SINR is calculated as follows:  
\[SINR^{*}_{F_{j,i}}=\frac{P_{F_{j},F_{j,i}}}{\sum_{k\
eq j}P_{F_{k},F_{j,i}}+N_{ 0}} \tag{3}\]  
This scheme encourages femtocells to unload the load of macro base station. Substantially, it means expansion of femtocell community service area, so as to improve the overall system performance.