esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Realization of \"Touch-and-Away\" Behavior
### Selection of the Robot's Behavior  
We then need to choose which tangent point to approach. As shown in Figure 2, the basic idea is that the robot moves towards the opposite side of the player's motion direction. Let the direction of the player at time \(t\) be \(\theta_{P}(t)\). Then, we calculate \(\delta(t)=\theta_{P}(t)-\theta_{P}(t-1)\). When \(\delta(t)\geq 0\), the player is moving towards left--thus, the robot moves towards \(\textbf{{S}}_{R}\). Otherwise, it moves towards \(\textbf{{S}}_{L}\).  
Figure 6: Calculation of the tangent points.  
However, this simple behavior is not enough to ensure robust and safe movement because it is likely that the robot's movement becomes unstable if the selection of the tangent point rapidly changes. In addition, if the robot moves nearer than \(r_{\text{touch}}\), the robot cannot calculate the tangent points. Thus, we divided the field around the robot into six areas, as shown in Figure 7. The definitions of the areas are in Table 1.  
When the robot chases the player, the player is supposed to be in area \(A\). In this case, the robot chooses the tangent point according to \(\delta(t)\). When the player is in area \(B_{L}\) or \(B_{R}\), it is difficult for the robot to change the tangent point since it needs to rotate rapidly. Thus, the robot moves towards the tangent point that can be targeted with less movement. When the player is in area \(C\), the robot fixes the target tangent point (\(\mathbf{S}_{L}\) or \(\mathbf{S}_{R}\)) and does not change the target even when the player moves sideways because the robot's movement becomes unstable if it switches the target tangent point at this distance. Area \(D\) is within the length of the robot's arms. Thus, if the player is in this area, the robot regards the players as touched. Then it moves away from the player. Area \(E\) is the emergency zone. If the player enters this area, the robot stops immediately.
# Effects of Achilles Tendon Moment Arm Length on Insertional Achilles Tendonopathy
## 1 Introduction  
The Achilles tendon, attached to the posterior process of the calcaneus, is the largest and strongest tendon in the body. It is composed of the gastrocnemius muscle, originating from the condyle of the femur, and the soleus muscle, originating from the upper tibia. The gastrocnemius and soleus muscles, collectively known as the triceps surae, flex the ankle joint during gait. In the gait cycle, balance is maintained against gravity during the stance phase, and flexing the ankle joint during the toe-off phase provides a forward propulsive force [1].  
The contraction of the triceps surae is exerted as a rotational force (torque) around the ankle axis, which can be calculated theoretically as the product of the muscle cross-section and the moment arm (MA) (Figure 1) [2; 3; 4].  
In general, the length of the MA is known to change depending on the angles of plantarflexion and dorsiflexion of the ankle joint, which is different in each individual [2; 3; 4]. However, the length of the MA of the triceps surae has been reported to change depending on the angle of plantar- and dorsiflexion of the ankle joint [5; 6; 7]. Yet, it has also been reported that motion does not cause any change in the length of the MA [8; 9], and that the plantarflexion moment length, from the plantarflexion region to the dorsiflexion region, is different in each individual, with little change caused by motion [2; 3; 4].
## 1 Introduction
By using a no-moving-component output-fed bistable fluidic oscillator to control fluid which alternatively flows into a parallel path where a piston moves forth and back, generating an intense pressure pulse by creating a water hammer at the stoke end of the piston, [PERSON] [18] proposed a novel AOT, namely the fluidic axial oscillation tool(FAOT). Its feasibility in terms of its application to horizontal directional well drilling was verified by [PERSON] et al. [19], by using CFD (Computational Fluid Dynamics) simulations and experimental tests. Because of the absence of any movable or deformable parts and the manufacture with an all-metal construction, the working life of an FAOT can be remarkably extended, exhibiting a resistance to high temperatures, corrosive environments, and other extreme ambient conditions. A number of field tests have shown that the drilling efficiency of FAOTs in directional well drilling can be improved by more than 50% (on average) compared with the conventional drilling method [20,21]. Moreover, this tool can work for more than 155 h without failure in a down-hole complex environment [21]. Therefore, FAOTs seem to be a promising solution to develop the new generation of friction reduction tools for the drilling process of a complex well. The FAOT is not a totally new design, as it seems to be an analogue of the fluidic hammer patented in the 1970s--based on an output-fed bistable fluidic oscillator--as discussed by [PERSON] et al. [22]. As presented in Figure 1, in the original design of an FAOT, a throttle plate was introduced into the pressure pulsation assembly to enhance the intensity of pressure pulsation. When the fluidic oscillator actuates the piston to move forth and back, cyclical restrictions are generated in the flow path as the piston rod passes over the taper hole of the throttle plate. The total flow area changes from the maximum to minimum value, generating the desired pressure pulses which can be superimposed on the pressure pulses generated in the working chamber caused by the sudden stop of the piston. Following this, the superimposed pressure pulses propagate to the axial oscillation assembly, where they are converted into an axial oscillating motion. However, this specially designed structure may be questionable or even counterproductive, as it can also serve as a cushion which may buffer the forward movement of the piston, resulting in a lower stroke end velocity of the piston. According to [PERSON]'s law [23], the produced water hammer pressure is proportional to the stroke end velocity of the piston (ignoring the velocity rebounded), which implies that a lower stroke end velocity leads to weaker pressure pulses being generated in the working chamber. This impairment may be stronger than the enhancement of the synergistic effect, or at least counteract it to some extent. Besides, an additional pressure drop caused by this throttling structure is not desired. Therefore, the performance analysis of an FAOT with the absence of a throttling plate is meaningful and poses some possibilities for making improvements.
# Evaluation of Hazelnut Cake Flour for Use as a Milk Powder Replacer in Ice Cream
## 3 Results and Discussion
### Texture Profile  
Ice cream texture is regarded as one of the most important quality attributes [72].  
The texture properties of analyzed ice cream samples are summarized in Figure 3. The complete replacement of SMP with the HPCF concentration significantly (\(p<0.05\)) affected the hardness, gumminess, and cohesiveness of the ice cream samples, while partial replacement did not affect these parameters. The sample prepared with a 100% replacement of SMP showed significantly higher hardness values (23,829.30 g) than the control sample (6881.71 g). Increased hardness in ice cream with hazelnut milk was also found in research conducted by [PERSON] et al. [42]. According to [PERSON] et al. [73], ice cream hardness is a reflection of mixture composition and processing parameters applied during the production, i.e., it is possible that lowering the milk component, and thus, increasing the plant component in ice cream, led to the increased hardness in samples from this research. Moreover, it is known that the overrun level affects the size of ice crystals, and subsequently hardness of ice cream. Namely, larger ice crystals are noticed at lower overrun values, which can lead to increased ice cream hardness [68; 74]. Although ice crystal size was not tested in this research, it can be suggested that overrun directly affected the hardness of analyzed samples, which is comparable with other studies [4; 75]. In addition, an increase in hardness can be the consequence of increased viscosity, due to fibre from hazelnut incorporation, because more viscous samples exhibit enhanced resistance to penetration during the texture analysis [56; 75]. [PERSON] et al. [76] found a strong positive correlation between hardness and viscosity of whey-based ice cream samples with reduced fat content. Additionally, HN4 showed a significantly higher level of cohesiveness (0.13) compared to the control sample (0.08). The presence of dietary fibre in hazelnut oilcake flour is likely to promote water absorption and swelling, which may increase viscosity, yielding a thicker mixture that enhances overall cohesiveness. Consequently, an increase in the level of SMP replacement led to an increase in gumminess, so HN4 showed a significantly higher value for gumminess (3192.60) in comparison to the control sample (518.31).  
Adhesiveness represents the power necessary for overcoming the attractive forces between the food and the contact material [53]. High levels of adhesiveness in ice cream can lead to undesirable textures, which can adversely impact consumer perception. Adhesiveness for the control sample was \(-521.79\) g\(\cdot\)s, and it was decreased to \(-1157.87\) g\(\cdot\)s and \(-1429.50\) g\(\cdot\)s for HN3 and HN4, respectively.
## 4 Conclusions  
This study investigated differences in the volatile and nonvolatile compounds of rice samples fermented by 12 different LABs. The level of ethanol differed significantly between obligatorily heterofermentative and facultatively heterofermentative/obligatorily homofermentative LAB samples. In homofermentative LAB, ethanol comes from pyruvate via acetyl-CoA, whereas it is derived from the pentose phosphate pathway of glucose in heterofermentative LAB. In addition, the amounts of 3-hydroxybutan-2-one and butane-2,3-dione were larger for the homofermentative/ facultatively heterofermentative LABs RTJL3, LPC, RTCL16, RTCL79, and RTCL31 than for the obligatorily heterofermentative LABs KR10, KR7, RTJL4, JFK2-2, JKA1-6, and RTCL9. This difference could at least partially be due to low-ethanol-tolerant microorganisms preferentially switching pyruvate utilization to the acetoin biosynthetic pathway in order to dispose of ethanol present at toxic levels.  
The application of PCA to data sets of the profiles of volatile and nonvolatile compounds revealed that fermented rice samples can be distinguished according to different LAB strains. The volatile compounds 2-ethylfuran, 2-propylfuran, 2-butylfuran, 2-pentylfuran, hexanal, 2-methylbutanal, pentan-1-ol, and butan-1-ol were strongly correlated with the rice sample fermented by _Lac. sakei_ (RTCL16). On the other hand, ketones and aldehydes such as butane-2,3-dione, 3-hydroxybutan-2-one, 6-methylhept-5-en-2-one, 2-methylbutanal, and nonanal were related to fermented rice samples inoculated with obligatorily heterofermentative LAB. These findings indicate that the profiles of volatile and nonvolatile compounds of fermented rice inoculated with different LABs can change significantly depending on the microbial strains present during the fermentation process. These results can be used to improve the quality of rice-based fermented products and develop rice-based functional foods, such as probiotic beverages.  
Conceptualization, Y.-S.K.; Methodology, M.S.K. and Y.R.H.; Analysis, S.M.L. and Y.R.H.; Investigation, Y.-S.K. and M.S.C.; Data Curation, M.S.K. and S.M.L.; Writing-Original Draft Preparation, S.M.L. and M.S.C.; Writing-Review & Editing, Y.-S.K. and S.M.L.; Visualization, S.M.L.; Supervision, Y.-S.K.  
This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) through Advanced Production Technology Development Program (grant number 316081-04) and the High Value-added Food Technology Development Program (grant number 317035-03), funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA).  
The authors declare no conflict of interest.
# Path Loss Measurements of Indoor LTE System for the Internet of Things
## 5 Conclusions  
In this paper, the measurement results of SDR-based small cell experimental platform were used to estimate the channel parameters, to improve the accuracy of ITU indoor path loss model, and to predict the coverage range of small cell base stations in indoor environments. The modified path loss model and the calculated fade margin (FM) for 90% SR were exploited to predict the coverage range of the deployed small cell base station of indoor LTE systems in the laboratory. In subsequent research, the proposed experimental method will be applied to factorial environments to perform wireless  
Figure 8: Coverage radius versus transmit power for the LOS and NLOS scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline Distance (m) & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 \\ Standard deviation (dBm) & 0.008378 & 0.03065 & 0.052378 & 1.150136 & 0.401302 & 0.822842 & 0.557252 & 1.339847 \\ Distance (m) & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\ Standard Deviation (dBm) & 1.398415 & 1.476168 & 0.278014 & 1.956235 & 1.97834 & 1.777501 & 0.853046 & 0.877525 \\ \hline \end{tabular}
\end{table}
Table 2: Measured standard deviation of shadow fading (0–15 m) for LOS.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline Distance (m) & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 \\ Standard Deviation (dBm) & 0.008378 & 0.03065 & 0.000157 & 2.67308 & 2.596399 & 1.908254 & 2.153402 & 3.272921 \\ Distance (m) & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\ Standard Deviation (dBm) & 4.230201 & 1.90781 & 3.208245 & 3.013187 & 3.063887 & 2.005314 & 2.930522 & 2.507987 \\ \hline \end{tabular}
\end{table}
Table 3: Measured standard deviation of shadow fading (0–15 m) for NLOS.  
channel parameter measurements. The results could improve the configuration and optimization of LTE small cell base stations in factory environments in order to meet high SR communication requirements for future IoT applications.  
The research work was supported by Ministry of Science and Technology, Taiwan, R.O.C. (MOST 105-2218-E-155-001 and MOST 106-2622-8-155-003-TE2). The authors would like to thank the National Instruments Taiwan Corporation for the widespread support.  
[PERSON] and [PERSON] conceived, designed and wrote the experiments; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] contributed test equipments/analysis tools; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Expansive Soil Stabilization with Time, Cement, and Silica Fume
## 4 Presentation and Discussion of Test Results  
### Consolidation Test  
To measure the free swell and swelling pressure for the expansive soil and the expansive soil treated with lime, cement and silica fume, some samples were prepared and utilized as oedometer cells. In Figures 6 and 7, the free swell and swelling pressure for the untreated and treated soil are depicted. It is noticeable that when adding a stabilizing substance, such as lime, cement or silica fume, both the free swell and swelling pressure decrease. The rate of decrease seems to be uniform for the three types of additives.  
With an increase in the percentage of additive, as seen in Figures 6 and 7, there is a decrease in both free swelling and swelling pressure. This results from an improvement in the chemical properties of the soil, which subsequently leads to an improvement in the overall soil and less swelling. It is caused by the chemical composition of the additive materials and the chemical characteristics of the expansive soil.  
Due to the presence of Van der Waals forces joining the montmorillonite sheets, the soil expands as a result. When water is absorbed, this repulsion between the forces causes the soil volume to grow [25].  
According to [PERSON] et al., the swelling pressure rises with a rise in the dry unit weight and falls with a rise in the initial water content of the molding (2008). The initial molding water content, particularly on the dry side of the optimum, has a greater impact on lowering or raising the swelling pressure than the dry unit weight does.  
Figure 5: Grouting probe.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Material** & **Density (kg/m\({}^{3}\))** \\ \hline Soil & 1750 \\ \hline Lime & 3000 \\ \hline Cement & 1400 \\ \hline Silica fume & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mixing proportions for 1 cubic meter of the grout material.  
The swelling pressure for materials compacted at high density (more than 1.4 Mg/m\({}^{3}\)) can mostly be attributed to the pressures of hydration, as described by [PERSON] et al. [26].  
The swelling percentage and swelling pressure have an inverse linear relationship with the initial water content when the initial dry unit weight is constant, as shown by [PERSON] [27] and [PERSON] et al. [28]. The swelling percentage and swelling pressure, on the other hand, may have a linear connection with the initial dry unit weight if the initial water content is constant.
## 3 Results
### Estimating ANHP from iAPAR
\begin{table}
\begin{tabular}{c c} \hline  & **Dependent Variable:** \\ \cline{2-3}  & **ANHP (SE) (kg/ha –1)** \\ \hline iAPAR: Grazing management: Ungrazed & 0.39 (1.05) \\ iAPAR: Grazing management: Pulse-grazed & \(-\)0.65 (1.09) \\ iAPAR: Ecosite Sandy Plains & 3.92 *** (0.96) \\ iAPAR: Ecosite Salt Flats & 5.71 *** (1.22) \\ iAPAR: Grazing management: Ungrazed: Ecosite Sandy Plains & \(-\)0.65 (0.90) \\ iAPAR: Grazing management: Pulse-grazed: Ecosite Sandy Plains & 0.69 (1.00) \\ iAPAR: Grazing management: Ungrazed: Ecosite Salt Flats & \(-\)2.46 * (1.21) \\ iAPAR: Grazing management: Pulse-grazed: Ecosite Salt Flats & \(-\)0.34 (1.32) \\ Constant & 149.95 (119.99) \\ Observations & 852 \\ Log Likelihood & \(-\)6037.74 \\ Akaike Inf. Crit. & 12,109.50 \\ Bayesian Inf. Crit. & 12,190.20 \\ \hline \end{tabular} Note: *_p_ < 0.05; ***_p_ < 0.001.  
\end{table} 
Table 1: _Cont._  
Figure 7: Ground-collected Aboveground Net Herbaceous Production (ANHP) values compared to satellite-based iAPAR values. The season-long heavy treatment was only conducted from 2019 to 2022 on the Loamy Plains ecosite, so we subset data for other grazing management treatments to only include those years and ecosite. The analysis involved assessing the slopes of the linear relationship between iAPAR and ground-based ANHP. Points represent ground-collected data, with colors and shapes denoting different grazing management treatments. Lines display linear relationships between ANHP and iAPAR values. No significant differences were found among grazing treatments (Tukey HSD; \(p>0.05\)).
# Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine
## 3 The Proposed Approach MIMLELM  
MIMLSVM is a representative two-phase MIML algorithm successfully applied in many real-world tasks [2]. It was first proposed by [PERSON] _et al._ in [1] and recently improved by [PERSON] _et al._,  
Figure 1: The relationship among these four learning frameworks.  
in [5]. MIMLSVM solves the MIML problem by first degenerating it into single-instance multi-label problems through a specific clustering process and then decomposing the learning of multiple labels into a series of binary classification tasks using SVM. However, as mentioned, MIMLSVM may suffer from some drawbacks in either of the two phases. For example, in the first phase, the user-specific parameter for the number of clusters may incur the effective problem; in the second phase, utilizing SVM as the classifiers builder may bring high computational cost and require a great number of parameters to be optimized.  
```
Input: DB: dataset; HN: number of hidden layer nodes; AF: activation function Output: Results
1\(DB=\{(X_{1},Y_{1}),(X_{2},Y_{2}),\ldots,(X_{m},Y_{m})\}\), \(\Gamma=X_{1},X_{2},\ldots,X_{m}\);
2 determine the number of clusters, \(k\), using AIC;
3 randomly select \(k\) elements from \(\Gamma\) to initialize the \(k\) medoids \(\{M_{1},M_{2},\ldots,M_{k}\}\);
4 repeat
5\(\Gamma_{t}=\{M_{t}\}(t=1,2,\ldots,k)\);
6 foreach\(X_{u}\)\(\in\)\((\Gamma-\{M_{t}\})\)do
7\(index=\arg\min_{t\in\{1,2,\ldots,k\}}d_{H}(X_{u},M_{t})\);
8\(\Gamma_{index}=\Gamma_{index}\cup\{X_{u}\}\)
9\(M_{t}=\arg\min_{A\in\Gamma_{t}}\sum\limits_{B\in\Gamma_{t}}d_{H}(A,B)(t=1,2,\ldots,k)\);
10 Transform \((X_{u},Y_{u})\) into into an SIML example \((z_{u},Y_{u})\), where \(z_{u}=(d_{H}(X_{u},M_{1}),d_{H}(X_{u},M_{2}),\ldots,d_{H}(X_{u},M_{k}))\);
11 until\(M_{t}\)\((t=1,2,\ldots,k)\) don't change;
12 foreach\(z_{u}\)\((u\in\{1,2,\ldots,m\})\)do
13 foreach\(y\)\(\in\)\(Y_{u}\)do
14 decompose \((z_{u},Y_{u})\) into \(|Y_{u}|\) SISL examples
15 Train \(\text{ELM}_{y}\) for every class \(y\);
16 Integrate all \(\text{ELM}_{y}\)'s based on GA
```  
**Algorithm 2**The MIMLELM algorithm.  
In this paper, we present another algorithm, namely MIMLELM, to make MIMLSVM more efficient and effective. In this proposed method: (1) We utilize ELM instead of SVM to improve the efficiency of the two-phase framework. To our best knowledge, we are the first to utilize ELM in the MIML problem and to conduct the comparison of ELM and SVM on MIML. (2) We develop a method of theoretical guarantee to determine the number of clusters automatically, so that the transformation from MIML to SIML is more effective. (3) We exploit a genetic algorithm-based ELM ensemble to further improve the prediction performance.  
The MIMLELM algorithm is outlined in Algorithm 2. It consists of four major elements: (1) determination the number of clusters (Line 2); (2) transformation from MIML to SIML (Lines 3-12); (3) transformation from SIML to SISL (Lines 13-17); (4) multi-label learning based on ELM (Lines 18-19). In what follows, we will detail the four elements in Section 3.1-3.4, respectively.
## 3 Results
### Response Time of Slave and Master Modules
#### 3.4.2 Mathematical Model with Multiprocessing  
* For the slave module: The improved response time of the slave module (_T_improved_slave_) as a function of the workload (C) can be modeled with an exponential function: \[T\_improved\_slave(C)=ae^{-bC}\] (4) where 'a' and '\(b\)' are constants that determine the magnitude and rate of improvement provided by the multiprocessing algorithm.
* For the master module: The enhanced response time of the master module (_T_enhanced_master_) as a function of the workload (C) can also be modeled with a similar exponential function: \[T\_enhanced\_master(C)=ce^{-dc}\] (5)where '\(c\)' and '\(d\)' are constants representing the magnitude and rate of improvement for the master module.
## 3 Results
### Security Analysis of vePOD  
In vePOD, secret shares are sent to edge servers instead of raw images. These encrypted secret shares contain random data, which significantly enhances vePOD's resistance to cryptography attacks, such as eavesdropping attacks and chosen ciphertext attacks. Different ciphertext is generated even for the same plaintext each time. Furthermore, vePOD does not suffer from key management risks, since it divides and hides information in secret shares instead of through encryption.  
In a vehicular edge-computing environment, edge servers may not be honest and they can collaborate to detect private information. To protect the privacy of a vehicle's sensor data, the vehicle can run one or more convolutional layers on a raw image locally to generate feature maps. Then, the secret shares of those feature maps are sent to edge servers where the remainder of the object-detection network will be executed. Even if edge servers collaborate with each other, they can only rebuild the feature maps. Thus, the original image data is protected.