esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Analysis
### Personal Protective Equipment Image Detection
Figure 7 presents a comprehensive evaluation of the SFA-YOLO network performance based on multiple metrics, including the Precision curve (P-curve), Recall curve (R-curve), F1 score curve, and Precision-Recall (PR) curve. These performance metrics reflect the network's behavior from multiple perspectives. A combined analysis of the four metrics clearly shows SFA-YOLO's effectiveness across several key aspects. The results show that SFA-YOLO performs well across various evaluation metrics and proves its practical value in complex task environments. As shown in Figure 8, the results indicate that the classification performance of SFA-YOLO is superior, providing a comprehensive evaluation of the true performance of classi  
Figure 7: P curve, R curve, F1 curve and PR curve performance indicators. fication in the safety attire detection task. This lays a foundation for targeted improvements in future work.
# Analysis of a Landscape Intensely Modified by Agriculture in the Tiete-Jacare Watershed, Brazil
## 2 Materials and Methods
### Methodology
#### 2.2.2 Change Detection--Land Use Monitoring of the Tiete-Jacare Watershed  
The land use and land cover monitoring, including the _Cerrado_ and Atlantic Forest areas in the Tiete-Jacare Watershed, were carried out using the random forest technique for the Sentinel-2 (S2) processing chains (Figure 4), which worked in parallel with the ArcGIS(r) and SNAP software and the Python programming language, version 3.6.  
The Sentinel-2 MSI sensor, bands 8/4/3/2 and points/orbits T22 KGB, T22 KFA, T22 KGA, T22 KHA and T22 KGV (Table 2) were used to analyse the temporal dynamics of land use and land cover for 2016, 2017 and 2018 in the Tiete-Jacare Watershed. The Sentinel-2 images were acquired from September 2016, July 2017, and August 2018 [37].  
Figure 4: Sentinel-2 detection processing chains.  
Figure 3: Architecture of the random forest model.  
The use of different satellite scenes (S2A and S2B) occurred due to the limited availability of cloud-free images from a single satellite during the referred period. The three Sentinel-2 images datasets for this study had a spatial resolution of 10 m. The dates were selected according to the work schedule. The periods of July to September were chosen because of the seasonality of the region's agricultural practices. The difference of one year between the images allowed the study of the landscape's temporal patterns, essential for regional planning, which considered the present growth and development aspects.  
For the random forest method already mentioned, we randomly created 1600 training areas (average size of 1.36 ha) in ArcGIS related to the exposed soil, citriculture, pasture, silviculture, sugar cane, urban area, vegetation, and water classes (200 samples per class).
## 3 Proposed Power Losses Model
### Proposed Switching Losses Model
\begin{array}{l}P_{igbt\_cur\_on}\Big{(}I_{p},V_{L},T\Big{)}=\frac{E_{on} \big{(}I_{ref},V_{ref},150\big{)}}{\pi T_{sw}}\times\Big{(}\frac{T+273}{150+273 }\big{)}^{\frac{ln[\frac{E_{off}(I_{ref},V_{ref},25)}{E_{off}(I_{ref},V_{ref}, 150)}]}{ln[\frac{250}{25}]}}\times\Big{(}\frac{V_{L}}{V_{ref}}\Big{)}\times \Big{(}\frac{I_{p}}{I_{ref}}\Big{)}\\ P_{igbt\_cur\_off}\Big{(}I_{p},V_{L},T\Big{)}=\frac{E_{off}\big{(}I_{ref},V_{ ref},150\big{)}}{\pi T_{sw}}\times\Big{(}\frac{T+273}{150+273}\Big{)}^{ \frac{ln[\frac{E_{off}(I_{ref},V_{ref},25)}{E_{off}(I_{ref},V_{ref},150)}]}{ln[ \frac{250}{25}]}}\times\Big{(}\frac{V_{L}}{V_{ref}}\Big{)}\times\Big{(}\frac{I_{p }}{I_{ref}}\Big{)}\\ P_{igbt\_cur\_soft}\Big{(}I_{p},V_{L},T\Big{)}=P_{igbt\_cur\_on}\Big{(}I_{p},V_{L},T \Big{)}+P_{igbt\_cur\_off}\Big{(}I_{p},V_{L},T\Big{)}\end{array}  \tag{49}\]Similarly, the proposed switching losses model of FWDs can be expressed as:  
\[P_{fwd\_cur\_sw}\Big{(}I_{p},V_{L},T\Big{)}=\frac{E_{rec}\big{(}I_{ ref},V_{ref},150\big{)}}{\pi T_{sw}}\times\Big{(}\frac{T+273}{150+273}\Big{)}^{ \frac{ln\big{[}\frac{E_{rec}(I_{ref},V_{ref},25)}{E_{rec}(I_{ref},V_{ref},150)} \big{]}}{ln\big{[}\frac{223}{423}\big{]}}}\times\bigg{(}\frac{V_{L}}{V_{ref}} \bigg{)}\times\bigg{(}\frac{I_{p}}{I_{ref}}\bigg{)} \tag{52}\]  
where \(E_{rec}\big{(}I_{ref},V_{ref},150\big{)}\) and \(E_{rec}\big{(}I_{ref},V_{ref},25\big{)}\) denote the reverse recovery energy at the temperature 150 \({}^{\circ}\)C and 25 \({}^{\circ}\)C.  
Figure 7 shows the principle diagram of the proposed switching losses model. The power losses of IGBT modules based on the phase-current period are indicated as:  
\[ \begin{array}{l}P_{light\_cur\_loss}=P_{light\_cur\_cond}+P_{fwd\_cur\_sw}\\ P_{fwd\_cur\_loss}=P_{fwd\_cur\_cond}+P_{fwd\_cur\_sw}\\ P_{cur\_loss}=6\times\Big{(}P_{light\_cur\_loss}+P_{fwd\_cur\_loss}\Big{)} \end{array}  \tag{53}\]  
Figure 8 depicts the comparison between the traditional losses model and the proposed losses model. The inputs of the traditional model are based on the values from the current sensor and the measured loss energy, while the proposed model adopts the values from the command signals according to the FOC algorithm. Therefore, the traditional model and the proposed model can be regarded as the measured model and the given model, respectively. An experimental comparison will be introduced in the following section. Through the mutual validation of these two models, the proposed losses model can be validated. Figure 7: Proposed switching losses model.
# State of the Art of Telecommunication Systems in Isolated and Constrained Areas
## 3 Comparison of the Solutions  
### Criteria for Comparing Solutions  
We compared these different solutions and determined that they are adaptable to isolated and restricted conditions according to the following five parameters:  
* The power supply of the telecommunication system and the autonomy of the system,
* The acclimatization of the telecommunication system to weather conditions,
* The range of coverage of the telecommunication system,
* The cost of the telecommunication system, and  
Figure 10: The Thales Watchkeeper X drone [51].  
Figure 9: The Sensus drone [48].  
* Telecommunication system deployment time and permanence.
# DeepGOMIMO: Deep Learning-Aided Generalized Optical MIMO with CSI-Free Detection
## 3 Detection Schemes for GOMIMO Systems  
In this section, we first introduce two conventional detection schemes for GOMIMO systems by utilizing \(M\)-PAM modulation, including the optimal joint ML detection and the ZF-ML detection. After that, we further present two deep learning-aided detection schemes, including the CSI-based ZF-DNN detection and our newly proposed CSI-free DNN detection.  
Figure 1: Block diagram of a general \(N_{r}\times N_{t}\) GOMIMO system. Insets (**a**) and (**b**) show the mapping tables of GOSM and GOSMP, respectively.
# JSSTR: A Joint Server Selection and Traffic Routing Algorithm for the Software-Defined Data Center
## 2 Background  
### Sfla  
SFLA is a meta-heuristic algorithm, which performs a heuristic search to solve the combinatorial optimization problem [12]. In SFLA, the possible solutions are recognized as frogs. At first, some frogs will be randomly generated as a sampled population. For each frog, a predefined performance value is computed. Then, these frogs are divided into several different communities (named memeplexes in SFLA). Each memeplex independently evolves to search the solution space in different directions. After a certain amount of evolutions, the memeplexes are mixed to form a new population. Subsequently, all frogs in that mixed population are re-divided into new memeplexes. Using these periodical shuffling, dividing and evolving processes, SFLA can converge to a global optimal solution with high probability [12]. More specifically, the main steps of SFLA are summarized below.  
1. Initialization: \(\alpha\) and \(\beta\) are firstly initialized. \(\alpha\) represents the number of memeplexes. \(\beta\) is the number of frogs in each memeplex.
2. Generate a population: \(F\) frogs will be generated to form a population, where \(F=\alpha*\beta\).
3. Compute the performance value: For each frog, a performance value is firstly computed.
4. Divide all frogs into \(\alpha\) memeplexes. In this step, all frogs are divided into \(\alpha\) memeplexes one by one in order of decreasing performance value.
5. Evolution within each memeplex: Each memeplex is evolved according to the predefined local searching strategy.
6. Shuffle memeplexes: After executing a certain number of evolutions, all frogs will be mixed. The performance values of all frogs will be computed again.
7. Check convergence: If the convergence criteria are satisfied, SFLA will stop. Otherwise, it will return to Step 4.  
An example of SFLA is illustrated in Figure 2. In Figure 2a, 16 frogs are generated at the beginning of Time Loop 1. Then, these frogs are divided into four memeplexes, which are denoted by a triangle, square, pentagon and circle. After that, each memeplex independently executes a certain number of evolutions. Figure 2b shows the evolution result at the end of Time Loop 1. Subsequently, as shown in Figure 2c, in the next time loop, these frogs are mixed and re-divided into four new memeplexes. Again, each memeplex is independently evolved. Figure 2d shows the evolution result at the end of Time Loop 2. In Figure 2d, all frogs are clustering near the optimal location (denoted by a cross).  
The shuffling, dividing and evolving process will be periodically executed until the convergence criteria are satisfied.
## 3 Results and Discussion
### Optimization of Fermentation Processes
#### 3.2.3 RSM Optimization of Fermentation  
Ethanol concentration (g/L) was the response values entered into Minitab(r) 17 following fermentation of RSM generated samples. Polynomial regression equations were generated for each categorical factor, or yeast species to predict maximum ethanol production (Table S1c). The significance of interaction effects between treatment factors on ethanol yield are shown in Table S4c and surface plots in Figure S3f.  
Investigated fermentation parameters included five continuous factors (pH, time, temperature, inoculum percentage, and nutrient addition percentage) and 1 categorial factor (yeast species). Optimal levels of treatment were determined using Minitab(r)17 for RSM design of experiment, using maximum ethanol concentration (g/L) as the targeted response variable. Fermentation conditions for maximum ethanol yield include an initial pH of 8.1, nutrient addition of 8.1% (_w/v_) for peptone and 4.05% (_w/v_) for yeast extract, 2 2% (_v/v_) inoculation with _Scheffersomyces stipitis_, at a temperature of 33.4 \({}^{\circ}\)C for 104.8 h (4.3 days). Under these parameters (n = 3), 4.86 \(\pm\) 0.31 g/L ethanol were produced. The predicted concentration of ethanol was to be 6.8 g/L, providing a 71.47% yield.  
As fermentation parameters increased their respective level of treatment, increased ethanol yield was observed. The categorical factor of yeast species had significant interaction effects with pH, time, and inoculum percentage. _Scheffersomyces (Pichia) stipitis_ showed significantly greater ethanol yield than _Kluyveromyces marxianus_, and _Pachysolen tannophililus_. _Scheffersomyces stipitis_ is a native xylose fermenter, and the combined viewer hydrolysate was rich in xylose from the hemicellulose fraction [13; 68; 69]. A pH of 8.1 was optimal for maximum ethanol yield. As pH increased, ethanol yield increased, as well. This trend was also observed for the fermentation of sunflower hulls [70]. By increasing pH, the amount of undissociated acetic acid molecules decreases, which minimizes their intracellular ATP depleting capacity [58; 69]. A fermentation temperature of 33.4 \({}^{\circ}\)C was predicted to produce maximum ethanol. This temperature is suitable for the yeast species selected; typically, a fermentation temperature of 30 \({}^{\circ}\)C is used [68; 71]. Fermentation time of 5.5 days was determined to be optimal, however, peak ethanol as observed at 35 h. Similar results have been noted for peak ethanol achievement [69]. An inoculation percentage of 22% (_v/v_) was predicted to yield maximum ethanol. Higher inoculation rates increase ethanol productivity [72; 73]. Peptone and yeast extract additions were determined to be optimal at 8.1% (_m/v_) and 4.05% (_m/v_), respectively. Peptone and yeast extract provide nitrogen and minerals to support yeast health, components that the hydrolysate lacks [72].
# Engineering a Combinatorial Laplacian Solver: Lessons Learned +
## 4 Evaluation
### Results
#### 4.2.4 Preconditioning  
The convergence of most iterative linear solvers on a linear system \(Ax=b\) depends on the condition number \(\kappa(A)\) of \(A\). The smaller the condition number is, the better the solvers converge. A common way to improve the condition number is to find a matrix \(P\), such that \(\kappa(P^{-1}A)<\kappa(A)\) and then solve the system \(P^{-1}Ax=P^{-1}b\) instead of \(Ax=b\) (preconditioning). Some linear solvers, such as Gauss-Seidel, are good preconditioners even though they are slow when used on their own. Thus, we check whether this is also true for KOSZ.  
In iterative methods, we usually do not explicitly compute \(P^{-1}A\), but apply \(P^{-1}\) and \(A\) separately to the current vector in each iteration. In our case, we use a few KOSZ iterations as a preconditioner in each iteration instead of taking a fixed matrix \(P\). Since the solver only works for SDD matrices, we need to use an iterative solver that only passes SDD matrices to the preconditioner. We choose the CG method and the FGMRES method (see Section 9.4 in [42]) on an unweighted \(100\times 100\) grid. The convergence of the residual with these solvers is plotted in Figure 6.  
For the CG method, we see that, unfortunately, the more iterations we use, the more slowly the methods converge. Since the cycle repairs depend crucially on the right-hand side and the solver is probabilistic, using the Laplacian solver as the preconditioner means that the preconditioner matrix is not fixed, but changes from iteration to iteration. [PERSON] and [PERSON] [43] show why this behavior leads to convergence problems and propose a CG method with variable-step preconditioning to cope with it. In practice, the flexible GMRES method is often more resistant to these convergence problems. Since the initial vector on the special ST is very good, we get good convergence in Figure 6 when using zero iterations of the solver in FGMRES, a behavior that is obviously not generalizable. For more iterations of the Laplacian solver, FGMRES still has convergence problems, but it is somewhat better than CG.  
We conclude that KOSZ is not suitable as a preconditioner for common iterative methods. It would be an interesting extension to check if the solver works in a specialized variable-step method.
## 2 The State-Dependent Constitutive Model for Saturated Sand
### Constitutive Equation for Saturated Sand  
The constitutive equation for saturated sand can be obtained as below:  
\[\left\{\begin{array}{c}dq\\ dp^{\prime}\end{array}\right\}=\left\{\left(\begin{array}{cc}3G&0\\ 0&K\end{array}\right)-\left(\begin{array}{cc}9G^{2}X&-3 KG\eta X\\ 3 KGDX&-K^{2}\eta DX\end{array}\right)\right\}\left\{\begin{array}{c}de_{q} \\ de_{\psi}\end{array}\right\} \tag{8}\]  
with:  
\[X=\frac{h(L)}{K_{p}+3G+K\eta D} \tag{9}\]  
\[L=\frac{dq-\eta dp^{\prime}}{K_{p}}=\frac{p^{\prime}d\eta}{K_{p}} \tag{10}\]  
where \(L\) is a loading index, \(h(L)\) is a Heaviside function with \(h(L)=1\) for \(L>0\) and \(h(L)=0\). It is noticeable that the extension of the foregoing relationship to account for reverse loading requires some additional mechanism such as a back-stress, or memory of the reversal stress ration point. These aspects are not addressed for the restricted scope of this paper.  
Based on the proposed model, the dilatancy can be regarded as a state-dependent quantity within critical soil mechanics. In this research, the model can capture the characteristics of soil state variation before and after scouring when the horizontal displacement of pile occurs. The finite element simulation below depicts the bearing capacity development characteristics of the pile-soil interface in each horizon. There are eleven material constants in the proposed state-dependency sand model, as shown with the value after model validation in Section 3.2 below, which is separated according to their functions. The function of elastic parameters (\(G_{0}\),\(v\)), critical state parameters (\(M,e_{\Gamma},\lambda_{c},\xi\)), dilatancy parameters (\(d_{0},m\)) and harden parameters (\(h_{1},h_{2},n\)) of the cohesionless soil is explained in Equations (1)-(7). As illustrated in the interface soil of this research, the development of the void ratio \(e\) and the effective axial stress \(p^{\prime}\) can be extracted, followed by the state variation.
## 2 Materials and Methods
### Materials  
The study material was 100 consecutively selected cone beam computed tomography scans (CBCT) of the maxilla of patients of the University Dental Clinic in Krakow (50 men and women each), taken for any reason between 2018 and 2021. Sample size determination showed that 91 patients would be suitable based on a margin of error of 0.05, confidence level 0.8, and population size of 200. Subjects included in the study met the following criteria: age of the patient >12 years, presence of upper first and second molar, absence of pathologies of the maxilla, and absence of clefts and maxillofacial syndromes. Patients with a history of orthodontic treatment, facial surgery, or facial trauma as well as images with artifacts were excluded from the research.  
The CBCT scans were acquired with the OP 3D Pro (KaVo, Berlin, Germany). The protocol was: field of view 130 \(\times\) 150 mm, average exposure time 8.5 s, average scanning time 39 s, average voxel size 380 \(\upmu\)m\({}^{-}\)5 mA. For the analysis of the CBCT images, a medical diagnostic monitor (RadiForce MX215, EIZO, Viena, Austria) and InViVo Dental Viewer (Anatomage, Santa Clara, CA, USA) were used. All the scans were analyzed by a single trained and calibrated senior postgraduate trainee in orthodontics.
## 3 Results and Discussions
### Optimization of Fermentation Condition
#### 3.4.2 Effect of L-Cysteine Supplementation Amount  
Sulfur amino acids play a crucial role in the formation of volatile sulfur compounds, such as 4-mercapto-4-methylpentan-2-one (4 MMP), 3-mercaptoethanol-1-ol (3 MH), and 3-(methylthio)-1-propanol [36; 37; 38; 39]. Previous research has demonstrated that L-cysteine is a core substrate for 2-furfurylthiol, and its yield is positively correlated with L-cysteine concentration [40; 41; 42]. Therefore, we hypothesized that the concentration of L-cysteine in the fermentation medium would affect the yield of benzenemethanethiol and investigated the effect of different concentrations of L-cysteine on the yield of fermentation products (0.605, 1.21, 1.815, 2.42, 3.025, and 3.63 g/L). As depicted in Figure 5B, the corresponding benzenemethanethiol content level increases with the increase in L-cysteine concentration, reaching the maximum 0.87 mg/L at 3.025 g/L, which is significantly higher than the initial 0.605 g/L addition. However, a decrease in benzenemethanethiol content occurred when the L-cysteine substrate was added above 3.025 g/L. This phenomenon may be attributed to the inhibitory effect of high L-cysteine concentrations on yeast cell viability [43]. Therefore,  
Figure 4: Analysis of the evolutionary relationship of strain J14 with other strains using MEGA software v11 with the neighbor-joining method and a bootstrap value of 1000.  
we selected 3.025 g/L as the optimal amount of L-cysteine for subsequent fermentation experiments.
# Stochastic Natural Vibration Analyses of Free-Form Shells
###### Abstract  
This work investigates the natural vibration characteristics of free-form shells when considering the influence of uncertainties, including initial geometric imperfection, shell thickness deviation, and elastic modulus deviation. Herein, free-form shell models are generated while using a self-coded optimization algorithm. The Latin hypercube sampling (LHS) method is used to draw the samplings of uncertainties with respect to their stochastic probability models. ANSYS finite element (FE) software is adopted to analyze the natural vibration characteristics and compute the natural frequencies. The mean values, standard deviations, and cumulative distributions functions (CDFs) of the first three natural frequencies are obtained. The partial correlation coefficient is adopted to rank the significances of uncertainty factors. The study reveals that, for the free-form shells that were investigated in this study, the natural frequencies is a random quantity with a normal distribution; elastic modulus deviation imposes the greatest effect on natural frequencies; shell thickness ranks the second; geometrical imperfection ranks the last, with a much lower weight than the other two factors, which illustrates that the shape of the studied free-form shells is robust in term of natural vibration characteristics; when the supported edges are fixed during the shape optimization, the stochastic characteristics do not significantly change during the shape optimization process.  
refe-form shells; natural vibration; stochastic analysis; sensitivity analysis Article
## 3 Results and Discussions
### Moisture Content as a Function of Water Activity via Sorption Isotherms  
In addition, the moisture content at various relative humidities (water activity) was examined via sorption isotherms (Table 2). The water sorption isotherms for GRE and \(\beta\)-GRE exhibited a Type II behaviour, which is synonymous with most food systems (Figure 1). The moisture content of the green rooibos extracts increased with increasing water activity, with both GRE and \(\beta\)-GRE exhibiting similar MCs (\(p>0.05\)) at a lower a\({}_{\text{w}}\) of 0.11 to 0.33. At an a\({}_{\text{w}}\) of 0.44, GRE absorbed a higher quantity of moisture compared with \(\beta\)-GRE (\(p<0.05\)), as can be seen in Figure 2. The GAB model exhibited an R\({}^{2}\) of 0.808 for GRE and 0.9611 for \(\beta\)-GRE, illustrating a good fit for both extract isotherms; thus, we can conclude by obtaining the characteristic parameters for a physical interpretation of the data (C, M\({}_{0}\), and K). The monolayer value (M\({}_{0}\)) of the native GRE was 7.93 and decreased to 6.4 g H\({}_{2}\)O. 100 g\({}^{-1}\) d.b. for encapsulated \(\beta\)-GRE. [PERSON] et al. [16] also reported a 31% reduction in M\({}_{\text{O}}\) of \(\beta\)-CD-olive pomace. The monolayer value is a critical parameter in the field of food science, particularly for food powders. A reduced monolayer reflects a reduction in available water-binding sites; in this instance, this could be attributed to complexation that resulted in fewer sites for water binding, thus improving the stability, protection, and shelf-life of the encapsulated bioactive compounds. The green rooibos extract water sorption behaviour determines the ideal storage conditions for dehydrated systems.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **a\({}_{\text{w}}\)** & **MC** & **L\({}^{\star}\)** & **a\({}^{\star}\)** & **b\({}^{\star}\)** \\ \hline GRE & 0.181 \(\pm\) 0.00 b & 3.29 \(\pm\) 0.18 a & 47.95 \(\pm\) 1.43 a & 16.12 \(\pm\) 0.51 b & 23.01 \(\pm\) 0.59 a \\ \(\beta\)-GRE & 0.111 \(\pm\) 0.01 a & 2.45 \(\pm\) 0.12 a & 53.70 \(\pm\) 0.80 b & 15.96 \(\pm\) 0.51 a & 24.46 \(\pm\) 1.01 a \\ \hline \hline \end{tabular} Data presented as mean \(\pm\) standard deviation (\(n=3\)) of a\({}_{\text{w}}\)—water activity, MC—moisture content, L\({}^{\star}\)—lightness (0–100), a\({}^{\star}\)—red\(/-\)green, b\({}^{\star}\)—yellow\(/-\)blue of GRE—green rooibos extracts and \(\beta\)-GRE—betacyclodextrin-encapsulated green rooibos extract. ANOVA and \(t\)-tests were performed. ab Means with different letter superscripts in the same column denote significant differences (\(p<0.05\)).  
\end{table}
Table 1: Physicochemical properties and betacyclodextrin-encapsulated green rooibos extracts.