esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Adsorption Process
#### 2.3.2 Adsorption Isotherm  
To obtain the adsorption isotherm, a series of beakers was used. Each beaker was filled with 50 mL of X-PR or MGC solution of varying concentrations: 0, 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 mg L\({}^{-1}\). The study of the adsorption equilibrium was carried out under the same optimal conditions indicated above. After equilibrium, the particles of the adsorbent were separated by centrifugation, and the clarified solution was analyzed by determination of the equilibrium concentration (\(C_{e}\)) of X-PR and MGC using the same calibration curve as previously used. The amount of reagent adsorbed at equilibrium (\(q_{e}\), in mg g\({}^{-1}\)) was calculated by Equation (1). To determine the adsorption isotherm, four adsorption modules were tested: the Langmuir model, Freundlich model, Temkin model and Dubinin-Radushkevich model. The most applicable model in the adsorption of X-PR by BS-HVL are the models of Langmuir and Freundlich. On the other hand, the Freundlich model is the most applicable in the elimination of MGC by BC-HVL. The adsorption of the Langmuir isotherm represented was attained from the following equation:  
\[q_{e}=\frac{q_{\rm m}\,K_{\rm L}C_{e}}{1+K_{\rm L}C_{e}} \tag{7}\]  
This equation can be reshaped and rearranged into the following linear equation [26]:  
\[\frac{C_{e}}{q_{e}}=\frac{C_{e}}{q_{m}}+\frac{1}{q_{m}K_{L}} \tag{8}\]  
where \(q_{e}\) is the amount (mg g\({}^{-1}\)) of X-PR and MGC adsorbed at equilibrium, \(C_{e}\) is the equilibrium concentration (mg L\({}^{-1}\)), \(q_{m}\) is the monolayer adsorption capacity (mg g\({}^{-1}\)) and \(K_{L}\) is the Langmuir constant (L mg\({}^{-1}\)) related to the adsorption free energy.  
An essential characteristic of the Langmuir isotherm can be expressed in terms of a dimension less constant called the separation factor and defined by the equation below [27; 28]:  
\[R_{L}=\frac{1}{1+K_{L}C_{0}} \tag{9}\]  
where:  
* \(C_{0}\) is the initial concentration of the adsorbate (mg L\({}^{-1}\)),
* \(K_{L}\) is the Langmuir constant (L mg\({}^{-1}\)),
* \(R_{L}>1\) indicates that the adsorption is unfavorable,
* \(R_{L}=1\) indicates that the adsorption is linear,
* \(0<R_{L}<1\) indicates that the adsorption is favorable,
* \(R_{L}=0\) indicates that the adsorption is irreversible.  
In our case, the found values of R\({}_{L}\) are all between 0 and 1, which reveals favorable adsorption.  
The linear form of Freundlich can be explained by the following equation:  
\[\ln(q_{e})=\ln\left(Kf+\frac{1}{n}\right) \tag{10}\]  
The Slope of this line is \(1/\mathrm{n}\) which indicates adsorption intensity.
# Research on Energy Harvesting Mechanism and Low Power Technology in Wireless Sensor Networks
## 4 Simulations and Results
### Neighbor Discovery  
In this experiment, the sending and receiving separation beacon mechanism based on reply to ACK (SRSB-A) is tested on the operating system Tiny OS 2.0 and the sensor node Telos B. To evaluate the performance of SRSB-A, we compare it with protocols such as Disco, U-Connect, etc. Figure 9A shows the contrast experiment with a 5% duty cycle in asynchronous symmetric scenes. The results show that the node in the SRSB-A can be successfully found by its neighbor node within 320 time slots, and in the worst discovery delay, the SRSB-A was reduced by 66.67% and 75% over Disco and U-connect, respectively. As shown in Figure 9B, in the contrast experiment with a 5% duty cycle in asynchronous asymmetric scenes, the node in the SRSB-A can successfully discover neighbor nodes  
Figure 7: The power generation performance of a bistable piezoelectric cantilever oscillator: (**A**) amplitude–frequency curves at different magnetization strengths; (**B**) output power at different impedance levels.  
within 3600 time slots. In the worst discovery delay, the SRSB-A was reduced by 53.37% and 71.27% over Disco and U-connect, respectively. Based on the above analysis, it can be considered that the SRSB-A has a good performance.
###### Abstract  
Background: Chronic ankle instability is a common condition that develops after lateral ankle sprains. Many surgical techniques have been described in case of failure of conservative treatment. The purpose of this paper is to present the results in terms of clinical outcomes, return to sport and recurrence rate after reconstruction of the lateral ligament complex of the ankle with a minimally invasive approach at a minimum follow up of five years. Methods: This retrospective study involved forty-seven patients treated for chronic ankle instability from January 2013 to November 2017. The clinical outcomes were evaluated with the American Orthopedic Foot and Ankle Society ankle and hindfoot score (AOFAS score), the University of California-Los Angeles Rating Scale (UCLA Rating Scale), the Karlsson-Peterson ankle instability score and the Halasi scale at a mean follow up time of 80 months. Results: The mean age of the patients was 42 years (23-63). The mean VAS pain score decreased from 5.6 (2-9) pre-operatively to 0.7 (0-7) post-operatively. The mean UCLA Rating Scale score improved from 7.7 (4-8) before surgical treatment to 9.2 (6-10) afterwards. The AOFAS score increased from a pre-operative mean value of 66.5 (40-95) to a post-operative mean value of 94 (88-100) and the Halasi scale from a pre-injury mean value of 4.3 (2-8) to 4.5 post-operatively (2-8). The mean Karlsson-Peterson ankle instability score increased from 71 (40-90) pre-operatively to 87.9 (70-90) post-operatively. During this period of follow up, the procedure survival rate was 95.7%, with two cases of recurrence of ankle sprain post-operatively after twelve months in one case and forty months in the other case. Conclusions: The described technique of reconstruction of the anterior talofibrular and calaneofibrular ligament with a minimally invasive approach with a semitendinos autograft is a viable treatment option for chronic ankle instability and enables the majority of patients to regain their activity and sport level with a low recurrence rate at a mid-term follow up.  
ankle sprains; ankle instability; ankle ligaments; surgical treatment; ankle lateral ligament reconstruction; semitendinos autograft; clinical outcomes; return to sport
## 3 Results
### Results
#### 3.2.2 Vistex Database  
Table 3 shows the classification results of our method applied to the Vistex database (54 images). Each image in the training set was partitioned randomly, and the number of windows per side was varied from two to four in each image chosen for training, with the number of random images from \(4\times\text{n}^{2}\) to \(10\times\text{n}^{2}\). The first column shows the best results reached by our method, and the second column shows the results after the post-processing stage. Table 4 compares the results published previously in the literature and our results for the Vistex database with 54 images. It can be observed in Table 4 that our post-processed method reached the highest results with 100%.
## 2 Materials and Methods
### Data Collection and Processing  
Data regarding livestock population and rice yield were collected from a report that was prepared by the livestock services and statistics departments of Bangladesh in 2024. Some data, like manure generation, along with biogas, electricity, biofertilizer, and compost potential, were generated by mathematical analysis, and the equations were modified from those suggested in previous articles and publications related to livestock manure management, which are discussed in specific sections of this paper. During mathematical estimation, this study considered the manure from six types of livestock (cattle, buffalo, goats, sheep, chickens, and ducks) for the evaluation of biogas and compost potential, as these animals are generally raised in Bangladesh [1]. For the calculation of manure valorization, this study considered cattle and buffalo as large animals, sheep and goats as small animals, and chickens and ducks as poultry.  
Before spatial analysis, the collected survey data (livestock population in each district/upazila) and mathematically estimated data (manure generation, biogas yield, electricity generation, compost production, synthetic fertilizer replacement) were processed via a spreadsheet to obtain shape file data using GIS (ArcMap 10.8) software. The shape file was further linked to the administrative dataset retrieved from the ArcGIS online [27]. This integrated spatial database was used to interpret the spatial analysis results.
## 2 Methods
### Kernel-Based Relevance Analysis
#### 3.1.2 Noise and Signal Mixing  
To asses the robustness of our proposal to realistic levels of noise and signal mixing, we do the following: we generate a noise time series \(\boldsymbol{\eta}\), with the same power spectrum of \(\mathbf{x}\), through the methodology proposed in [8]. Then, we add \(\mathbf{x}\) and \(\boldsymbol{\eta}\) to generate a noisy version of \(\mathbf{x}\), \(\mathbf{x}_{\boldsymbol{\eta}}=\mathbf{x}+10^{-\frac{\text{SNR}}{2}} \boldsymbol{\eta}\), where SNR is the signal to noise ratio. Likewise for \(\mathbf{y}\). Then, we mix \(\mathbf{x}_{\boldsymbol{\eta}}\) and \(\mathbf{y}_{\boldsymbol{\eta}}\) to simulate one of the effects of volume conduction, by doing \(\mathbf{x}_{\boldsymbol{\eta}}^{w}=\left(1-\frac{w}{2}\right)\mathbf{x}_{ \boldsymbol{\eta}}+\left(\frac{w}{2}\right)\mathbf{y}_{\boldsymbol{\eta}}\) and \(\mathbf{y}_{\boldsymbol{\eta}}^{w}=\left(1-\frac{w}{2}\right)\mathbf{y}_{ \boldsymbol{\eta}}+\left(\frac{w}{2}\right)\mathbf{x}_{\boldsymbol{\eta}}\), with \(w\) the mixing strength. We set the parameters SNR and \(w\) to 3 and 0.25 respectively, based on the results obtained in [8] for realistic values of noise and mixing for EEG signals. The coupling coefficient \(C_{12}\) is held constant at a value of 0.5 to simulate couplings of medium strength.
# The RHO Family GTPases: Mechanisms of Regulation and Signaling
## 3 Regulation of RHO Family GTPases
### GTPase-Activating Proteins (GAPs)
#### 3.4.2. Structural and Functional Characteristics of the RHOGAP Domain  
The GAP domain of the RHOGAP family consists of approximately 190 amino acids and shares high sequence homology within the family. Although the RHOGAP domain shares no similarities to RASGAP family members at the amino acid level, RHOGAPs and RASGAPs resemble each other in their tertiary structure [128; 129]. Comparative structural analysis of the RHOGAP domain with other GAPs of RAS subfamilies has suggested that GAP domains in RAS and RHO family proteins are evolutionarily related [128; 130] and that the catalytic domains of RHOGAPs share a core structural fold. The RHOGAP domain is made up of seven \(\alpha\)-helices. The functional characteristic of the RHOGAP domain is a pair of conserved basic residues: catalytic arginine (the arginine finger) and lysine (Arg282 and Lys319 in p50 RHOGAP numbering) [114; 131].
## 3 Tracking Algorithm
### Multiple Model Algorithm  
Based on the above considerations, the two control variables cannot be measured directly and their initial states are hard to choose as the filter initial states in HGV tracking. However, their variation ranges usually are within certain ranges [6]: AOA: 6 to 12 degrees; bank angle: \(-20\) to 20 degrees.  
So we can use a nine-model multiple model algorithm based on the aerodynamic model to track HGVs. The interacting multiple model (IMM) [15] estimator is a widely used cost-effective algorithm for maneuvering target tracking. We can get the model set by combining the three fixed AOAs and three fixed bank angles as  
\[\left\{\begin{array}{c}(\alpha_{1}=6^{o},\
u_{1}=-20^{o}),(\alpha_{2}=6^{o },\
u_{2}=0^{o}),(\alpha_{3}=6^{o},\
u_{3}=20^{o}),(\alpha_{4}=9^{o},\
u_{4}=- 20^{o}),(\alpha_{5}=9^{o},\
u_{5}=0^{o}),\\ (\alpha_{6}=9^{o},\
u_{16}=20^{o}),(\alpha_{7}=12^{o},\
u_{7}=-20^{o}),(\alpha _{8}=12^{o},\
u_{8}=0^{o}),(\alpha_{9}=12^{o},\
u_{9}=20^{o})\end{array}\right\} \tag{8}\]  
The details of the nine-model IMM algorithm can be found in our paper [7].
## 3 System Integration
### Synthesis Results  
The whole design is synthesized using Xilinix Vivado 28 nm. The Vivado utilization of the whole chain implemented on Virtex 7 is depicted in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Resource** & **Utilization** & **Available** & **Utilization (\%)** \\ \hline
**LUT** & 50,728 & 433,200 & 11.71 \\ \hline
**LUTRAM** & 3016 & 174,200 & 1.73 \\ \hline
**FF** & 31,896 & 866,400 & 3.68 \\ \hline
**BRAM** & 69 & 1470 & 4.69 \\ \hline
**DSP** & 80 & 3600 & 2.22 \\ \hline
**IO** & 4 & 850 & 0.47 \\ \hline \end{tabular}
\end{table}
Table 1: VIVADO utilization for Virtex 7 for whole chain.  
Figure 15: RTL BER vs. SNR.
## Results  
### Relationships of SIF and APAR with Tower GPP  
The seasonal dynamics of daily OCO-2 SIF, averaged APAR (16-d), GOME-2 SIF (16-d), RSIF (16-d), and daily tower GPP for the Daman and A'rou sites from 2014-2017 are shown in Figure 2. APAR was calculated by Equation (4). The variations in SIF\({}_{757}\), SIF\({}_{771}\), SIF\({}_{740}\), RSIF, and APAR exhibited seasonal patterns that were similar to those of the tower GPP at both sites. SIF\({}_{757}\), SIF\({}_{771}\), SIF\({}_{740}\), RSIF, and APAR showed large discrepancies from the tower GPP measurements during the spring onset, the peak of the growing season and senescence stages, and the tower GPP reflected slightly better consistency with SIF\({}_{757}\), RSIF and APAR than with SIF\({}_{771}\) and SIF\({}_{740}\).  
As shown in Figure 3, OCO-2 SIF\({}_{757}\) and SIF\({}_{771}\) showed much stronger linear correlations with the tower GPP at a daily timescale than shown by GOME-2 SIF\({}_{740}\) and tower GPP at a 16-d averaged timescale. The goodness-of-fit between SIF\({}_{757}\) and tower GPP was better than that between SIF\({}_{771}\) and tower GPP at both sites (Daman and A'rou), and the goodness-of-fit between SIF\({}_{740}\) and tower GPP was relatively weak at a 16-d averaged timescale at both sites. The correlation between SIF\({}_{771}\) and tower GPP is weaker than the correlation between SIF\({}_{757}\) and tower GPP, but stronger than the  
Figure 2: Seasonal cycles of SIF\({}_{757}\), SIF\({}_{771}\), SIF\({}_{740}\) (16-d), RSIF (16-d), APAR (16-d), and tower GPP for the period 2014–2017 at the (**a**) Daman and (**b**) A’rou sites.  
correlation between SIF\({}_{740}\) and tower GPP. The 16-d averaged APAR and RSIF had the strongest linear correlations with 16-d averaged tower GPP. The correlations between SIF\({}_{757}\) and tower GPP, SIF\({}_{771}\) and tower GPP at a daily timescale were greater at the A'rou site than at the Daman site. So were correlations between SIF\({}_{740}\) and tower GPP, RSIF and tower GPP, APAR and tower GPP at a 16-d averaged timescale. In general, OCO2 SIF (SIF\({}_{757}\) and SIF\({}_{771}\)), GOME-2 SIF, RSIF, and APAR showed strong relationships with tower GPP.  
Figure 3: Scatterplot of the flux tower GPP and (**a**) SIF\({}_{757}\) and (**b**) SIF\({}_{771}\) at a daily timescale, as well as (**c**) SIF\({}_{740}\), (**d**) APAR, and (**e**) RSIF at a 16-d averaged timescale for the period 2014–2017.
# Review of the Research Progress in Combat Simulation Software
## 2 Top-Level Specification of Simulation Software
### Lvc  
The Simulation Interoperability Standards Organization (SISO) [19] was established in 1996 and is committed to promoting the interoperability of modeling and simulation. The LVC was originally proposed by SISO as a classification method for model and simulation, where L, V, and C refer to live simulation, virtual simulation, and constructive simulation, respectively.  
In order to realize the joint simulation test that is multirange, multi-agency, and multiplatform in a complex environment, the U.S. military proposed the LVC simulation architecture development roadmap [11] in 2007 to improve the interoperability of a multi-architecture simulation environment. In this roadmap, the ultimate goal of the modeling and simulation field is to create a unified LVC integration architecture, which can quickly integrate models and form an effective LVC environment for tactical training, coordination, and operational evaluation.  
In 2012, SISO proposed the Layered Simulation Architecture (LSA) [20], which could unify the distributed simulation architecture such as DIS and HLA through the DDS (Data Distribution Service) [21]. At present, a Spanish company, named Nextel Aerospace Defense & Security, has developed Simware [22] that can fully support the LSA architecture.  
Figure 4 shows the architecture of the LSA simulation system, which is composed of layers of _application_, _coupler_, _middleware_, and _protocol_.  
* The _application_ layer includes two types of simulation applications: the legacy applications based on existing simulation standards (e.g., DIS, TENA [23], and HLA), and new simulation applications based on DDS and other new architectures.
* The _coupler_ layer provides various couplers between the _application_ layer and the _middleware_ layer, including interfaces, gateways, and services. The legacy simulation systems can be seamlessly connected to the middleware through the specific interface (e.g., DIS-DDS, TENA-DDS, and HLA-DDS), gateway, and various simulation services. New simulation systems, such as the ones based on DDS, can connect to the _middleware_ layer with public interfaces and gateways.
* The _middleware_ layer is the key infrastructure layer for data exchange, through which each simulation system can realize mutual operation. The LSA currently selects the data-centric DDS as the middleware to build a low-latency, high-reliability, and scalable communication architecture for the distributed simulation system.
* The _protocol_ layer currently utilizes the interactive real-time publish-subscribe protocol DDSI [24] as the underlying communication protocol for the DDS middleware.  
Figure 4: LSA simulation system architecture.
## 3 Results
### Quality of the Dried Maize
#### 3.2.3 Colour of Dried Maize  
The mean values for the L*, a*, b*, chroma, hue, and total colour difference for maize before drying (fresh); maize dried in an inflatable solar dryer (ISD); and direct sun drying (DSD) are presented in Table 2.  
The results show that there was no significant difference in colour due to the drying method. The L* a* b* values for the grains show that the maize was white in colour, as L* was high, with a slight tendency towards pale clean. The b* value, which indicates the variation from yellow to blue, was positive, an indication of the yellow tendency of maize grains [34]. The relatively higher \(\Delta\)E* value of grains treated by DSD means that there was a slight effect of drying on the colour. However, this change was not statistically significant. Experiments in another study showed that temperatures of 120 \({}^{\circ}\)C and higher had a significant influence on maize lightness, redness, and yellowish [19]. Our results are consistent with those of [PERSON], [PERSON], [PERSON] and [PERSON] [16], who analysed the impact of different drying methods on the colour of maize and found that the impact was not significant. Several other scientific publications have also indicated the minor impact of solar drying on a product's colour [35].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & L* & a* & b* & C* & h* & \(\Delta\)E* \\ \hline Fresh & 63.4 \(\pm\) 0.5 a & 1.9 \(\pm\) 0.3 a & 15.3 \(\pm\) 1.3 a & 15.4 \(\pm\) 1.3 a & 1.5 \(\pm\) 0.0 a & - \\ ISD & 61.6 \(\pm\) 2.4 a & 2.2 \(\pm\) 0.1 a & 15.5 \(\pm\) 0.6 a & 15.7 \(\pm\) 0.6 a & 1.4 \(\pm\) 0.0 a & 2.8 \(\pm\) 1.73 a \\ DSD & 66.0 \(\pm\) 5.3 a & 2.0 \(\pm\) 0.2 a & 16.1 \(\pm\) 2.3 a & 16.1 \(\pm\) 2.3 a & 1.4 \(\pm\) 0.0 a & 5.1 \(\pm\) 3.0 a \\ LSD & 6.59 & 0.48 & 2.54 & 2.55 & 0.023 & 11.26 \\ \hline \end{tabular} Notes: Means followed by the same letter are not significantly different (\(p\leq\) 0.05).  
\end{table}
Table 2: Colour parameters of maize dried by ISD compared to DSD (means comparison \(t\)-test).  
Figure 5: Total aflatoxin content in fresh maize, maize dried in the inflatable solar dryer (ISD), and maize dried under direct sun (DSD) for the three batches (Treatments with same letters are not significantly different, \(p\leq\) 0.05).
## Materials and Methods
### Statistical Analysis  
All of the data are presented as mean \(\pm\) SD. The Q-Q plot and the Shapiro-Wilk test with skewness were used to test for normal distribution of data. Differences between left and right muscles during the symmetrical exercises SH, RF, CE, and CF, or between ipsilateral and contralateral muscles during CL and CR performed towards left and right side were examined while using paired-t test. In the case of no difference, the mean value of the two sides was used. If significant differences were found,the side specific values are reported and the highest used to represent the muscle activation level for that exercise. Regarding the main hypotheses of the muscle activity (%MVE) for each muscle group, one way repeated measures ANOVA was used to compare the different exercises (SH, RF, CE, CF, CL, and CR). If the assumption of Sphericity was violated, the Greenhouse-Geisser correction was used. Planned contrasts were used to localize the differences between the exercise, resulting in the highest activation of specific muscle group and the other exercises. Paired t-test was used for testing possible differences between the two exercise intensities. Furthermore, paired t-test was used to compare muscle activity in the concentric phase and eccentric phase of each muscle group. The fatigue development was tested by paired t-test to compare the third repetition and the last one repetition in normalized EMG amplitude (%MVE) and EMG frequency (MPF). A substantial addition in muscle activation level of at least 20% was requested in order to recommend an advanced, instead of a simple, exercise. This level is well above the SD of approx. 10% in mean level of trapezius activation both found within groups and variability of repeated EMG recordings during high intensity contractions, see, for instance, mean activation levels for trapezius in [17]. A power analysis showed that eight subjects would be sufficient to reveal a difference in muscle activation level of 20%. Within the recruiting period, we managed to recruit 11, who all conducted the full protocol. All of the results were analyzed using SPSS 16.0 statistical software (SPSS Inc, Chicago, IL, USA). A probability level of \(p<0.05\) (two-tailed) was considered to be statistically significant.