esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Micromotion Assessment  
Figure 6 shows the distribution of micromotion obtained from the FEM analysis. The calculated maximum micromotion was 4.89 \(\upmu\)m in the rotational direction and 11.74 \(\upmu\)m in the longitudinal direction. Compared with the reference value of 150 \(\upmu\)m [32], which has been reported as the threshold for the onset of implant loosening, the micromotion in both directions was clearly smaller. Furthermore, even in comparison with the more stringent reference value of 40 \(\upmu\)m [32], which is considered favorable for bone ingrowth, the present analysis results were well within acceptable limits in both rotational and longitudinal directions.  
Figure 7 shows the results of the micromotion distribution analysis at the distal region of the stem. Although the micromotion at the proximal porous press-fit region was minimal, both rotational and longitudinal micromotions tended to increase toward the distal end of the stem. The calculated maximum micromotion at the distal region was 65.38 \(\upmu\)m in the rotational direction and 74.65 \(\upmu\)m in the longitudinal direction.
# Property-Based Semantic Similarity Criteria to Evaluate the Overlaps of Schemas
## 4 Semantic Equivalence
\begin{array}{l}(1,0,0),\text{ }if\ Sim_{L}(U,\ V)>T_{L}\\ (1,1,0),\text{ }if\ Sim_{L}(U,\ V)\leq T_{L}\text{ and }Sim_{P}>T_{P}\\ (1,1,1),\text{ otherwise.}\end{array} \right.\]Here, \(T_{L},T_{P}\) are the predefined thresholds for the similarity on the label level and property level, respectively. Furthermore, an overall threshold \(T_{S}\) (\(T_{S}<T_{L}\)_and_\(T_{S}<T_{P}\)) is defined with the empirical value from an expert to simulate the semantic equivalence. For example, for the entity types _student_ in the schema of the education affairs office and _member_ in the schema of the student union as shown in Figure 1, if \(Sim_{s}\) (\(student,\ member\)) \(>T_{s}\), the two entity types are considered semantically equivalent. Theorem 1: _The method proposed in Definition 1 can measure the semantic equivalence of two entity types._  
Proof: First, if two entity types \(U,\ V\) reach a given threshold at the label level, there is  
\[Sim_{L}(U,\ V)>T_{L}\]  
then in Formula (3), \((\alpha,\ \beta,\ \gamma)=(1,0,0)\), there is  
\[Sim_{s}(U,\ V)=Sim_{L}(U,\ V)>T_{L}\]  
According to the limitation of \(T_{S},\ T_{S}<T_{L}\), we can deduce  
\[Sim_{s}(U,\ V)>T_{S}\]  
We can conclude that the two entity types \(U,\ V\) are regarded as semantically equivalent. When \(Sim_{L}(U,\ V)<T_{L}\) but \(Sim_{P}>T_{P}\), then \((\alpha,\ \beta,\ \gamma)=(1,1,0)\), and there is  
\[Sim_{s}(U,\ V)=Sim_{L}(U,\ V)+Sim_{P}(U,\ V)\]  
\[Sim_{s}(U,\ V)>Sim_{L}(U,\ V)+T_{P}>T_{P}\]  
and \(T_{S}\) meets \(T_{S}<T_{P}\), so  
\[Sim_{s}(U,\ V)>T_{S}\]  
which indicates that the two entity types \(U,\ V\) are regarded as semantically equivalent. Finally, in the third case when \(Sim_{L}(U,\ V)\leq T_{L}\)_and_\(Sim_{P}\leq\ T_{P}\), there is \(Sim_{s}(U,\ V)=Sim_{L}(U,\ V)+Sim_{P}(U,\ V)+Sim_{I}(U,\ V)\). If \(Sim_{L}(U,\ V)+Sim_{P}(U,\ V)+Sim_{I}(U,\ V)>T_{S}\), we can deduce \(Sim_{s}(U,\ V)>T_{S}\). \(\square\)  
In conclusion, when the calculated results of two entity types at the level of label and property reach the given threshold, the two entity types can be regarded as directly equivalent. The three levels label, property and individual can comprehensively evaluate the equivalence of the two entity types from multiple perspectives. The classical equivalence of two entity types lies only on the label level. Potentially equivalent entity types are usually neglected because of different labels. Such different labeling happens frequently for entity types from different schemas. This exaggerates the difference between the entity types, which in turn reduces the precision of the criteria to evaluate the overlaps between schemas. We consider the particularity of entity type in schemas and enhance the measurement of entity types. Our method not only considers the label level, which is compatible with the previous calculation methods, but also considers the property level and individual level. This method can find more equivalent entity types.
## 1 Introduction
Then, based on the frequency characteristics of each subsequence, the subsequence prediction is performed by using the advantages of ARMA and DBN, respectively. Finally, the subsequences are reorganized, and the final PV output prediction value is obtained. The main contributions of this article are as follows:  
(1) To reduce the complexity and non-stationarity of the PV output data series, the VMD decomposition is used for the first time to preprocess the PV data sequence and decompose it into a series of IMF component sequences with good characteristics, achieving an effective extraction of advanced nonlinear features and hidden structures in PV output data sequences. (2) An innovative method for predicting PV output based on VMD-DBM-ARMA is proposed. According to the characteristics of the IMF component sequence decomposed by the VMD, DBN and ARMA models are used to improve predictions of the high- and low-frequency component sequences, respectively. Based on this, the DBN is used for feature extraction and structural learning of the prediction results of each component sequence. Finally, the PV output predictive value is obtained. (3) Taking the actual measured data of a PV power plant in China-Yunnan for application, the short-term PV output predictions of ARMA, DBN, EMD-ARMA-DBN, EEMD-ARMA-DBN, and VMD-ARMA-DBN were conducted and three prediction precisions were introduced, respectively. The evaluation indicators perform a statistical analysis on the prediction effect of each model. The results show that the proposed method can guarantee the stability of the prediction error and further improve the PV prediction accuracy. The remainder of this paper is organized as follows: Section 2 describes our proposed approach: A Hybrid Forecasting Method for Solar Output Power Based on VMD-DBN-ARMA; experimental results are presented in Section 3; and the experimental comparison and conclusion are given in Sections 4 and 5, respectively.
# Optimization of AZ91D Process and Corrosion Resistance Using Wire Arc Additive Manufacturing
## 2 Materials and Methods
### Sample Preparation  
A series of trials using different printing parameters were performed to determine ideal printing parameters. Parameters related with the intensity of the arc were first found by a trial-and-error method. It was found that one of the main factors that affect the quality of the line was the gas flow rate of the pilot arc. When the flow rate is low (<0.4 L/min) the arc was not able to melt the wire. On the other hand, high flow rates (>0.8 L/min) create a strong jet that carves the substrate and ejects the molten metal wire away from the molten pool. Thus, a medium pilot arc gas flow rate was used (0.6 L/min). A shielding gas flow rate of 12 L/min was used for all the trials, protecting the melting pool from the exposed air. For both the pilot gas and the shielding gas 99.99% Argon was used. The cross-section area of the selected wire's diameter was 1.5 mm and 12 V motor was used. The minimum feed rate that the motor is capable to produce is 117 cm/min (46 in/min). This feed rate was then used for all the trials.  
Figure 1: Experimental setup: (**a**) Overall system for WAM; (**b**) pro-Fusion Dual Arc 82 HFP welding machine used a power supply; (**c**,**d**) stainless steel produced by WAAM (scale bar represents 1 cm).  
Samples were prepared at various arc currents and printing speed, keeping all other parameters constant. Table 3 shows a visual representation of the combination of parameters that were used on the trials to determine the best printing conditions. Printing speed was changed from 90 to 180 mm/min, using intervals of 15 mm/min between each trial. 20-50 Ampere were tried. This time, the intervals were kept on 5 A between each trial. A window of parameters in which visually uniform lines could be printed was found. 15 parameters inside this printing window were selected (5 current intensities; 30 A, 32.5 A, 35 A, 37.5 A, and 40 A, and 3 printing speeds 120 mm/min, 135 mm/min, and 150 mm/min). Using each one of these combinations, five parallel lines were printed over the same substrate. After printing, the lines were detached from the substrate and their dimensions were measured. The lines were sectioned and mounted in epoxy resin for further analysis and characterization. All the samples were wet ground through successive grades of silicon carbide abrasive papers from P240 to P4000 (Buehler, Lake Bluff, IL, USA) and etched with glycol (1 mL HNO3, 24 mL of water, 75 mL Ethylene Glycol) [20].
## 2 Materials and Methods
### Drop Vertical Jump Task  
A total of six sets of double-legged drop vertical jumps (DVIs) were performed from 31-cm, 46-cm, and 61-cm platforms in both shoe conditions. Five valid trials were obtained for each set and hence, there were 30 trials in total (5 trails \(\times\) 3 drop heights \(\times\) 2 levels of shoe hardness) for each participant entering data analysis. The shoe order was randomized among the participants while the same order was kept consistent across all drop heights within a participant. The platform height order also was randomized among the participants. The selected platform heights fell within the typical range of drop heights observed in basketball, and similar heights were used in previous DVJ studies [19; 20; 21; 22; 23]. Participants stood on the platform with their hands placed on opposite shoulders and elbows pointing forward [21] and were instructed to step off the platform with the right leg, land simultaneously on both feet, and then jump vertically to maximum height (Figure 1a). Trials were considered successful when the right foot landed entirely on the right force plate with the left foot landing entirely on the adjacent force plate. The intertrial reliability of biomechanical variables obtained from this drop jumping test protocol was reported as good [24].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Condition**} & \multirow{2}{*}{**Features**} & **Midsole** & **Size** & **Mass** & **Midsole** & **Rearfoot** & **Forcefoot** \\  & & **Hardness** & **(US)** & **[g]** & **Material** & **Thickness** & **Thickness** \\  & & **(Asker C)** & & & **[mm]** & **[mm]** \\ \hline Soft & & 50 & 8.5 & 417 & EVA & 20.5 & 10.5 \\ Hard & & 60 & 8.5 & 422 & EVA & 20.5 & 10.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of visually identical experimental basketball shoes with different midsole hardness.  
Prior to data collection, participants were firstly given time to get used to the test shoes by walking freely around the laboratory. After that, they practiced the DVJ task until they executed the movement correctly [3]. It took about 5 min to familiarize with each of the three drop heights. Once participants were comfortable with the shoes and DVJ task, the data collection commenced. Following each set of DVJs, participants rated their perceived landing impact (0 cm: very low; 15 cm: very high) on a visual analog scale (VAS), which is considered a reliable measure [25] and is widely employed in the subjective assessment of athletic footwear [12; 18; 26]. Seated rests were permitted ad libitum between the six sets of trials.
# A Deep Learning Technique for Optical Inspection of Color Contact Lenses
## 2 Materials and Methods
### Water Content Measurements for Colored Contact Lenses  
The water content (WC) of the contact lenses was measured gravimetrically at room temperature. Initially, all the contact lenses were fully dried to a constant weight prior to equilibrium swelling. Subsequently, the fully dehydrated hydrogel contact lenses were allowed to swell to equilibrium through immersion in a saline solution and maintained at room temperature for 2 days. Thereafter, the samples were removed from the saline solution, excess surface water was blotted off with tissue paper, and the swollen hydrogel contact lens weights were recorded. The WC is calculated as follows:  
\[\text{WC}\left(\%\right)=\left[\left(\text{W}_{\text{s}}-\text{W}_{\text{d}} \right)/\text{W}_{\text{d}}\right]\times 100, \tag{1}\]  
where \(\text{W}_{\text{s}}\) and \(\text{W}_{\text{d}}\) denote the hydrogel contact lens weights in the equilibrium swelling and dry states, respectively. The average values of five measurements were calculated for each sample.
# Life Cycle Cost Analysis and Deterioration Patterns of Limestone Paving
## 6 Discussion  
This research delivers a novel approach to natural stone pavements' life cycle analysis, incorporating the material properties, the deterioration patterns, and consequent life cycle costs and planning. The tested standard properties of the paving stone have been incorporated into the life cycle cost analysis model in the following ways:  
1. The core laboratory tests of the paving stones, including apparent density, water absorption, compressive strength, flexural strength, and abrasion resistance, provided decisive criteria for the assessment of the natural stone pavement's wear and tear trends. The coefficient of variance of the laboratory tests shows that the genuine stone physical properties were initially more widely dispersed compared to those of the standard reference stone. The physical properties of the stone after exposure to the site service regime were found to have a high ratio of variance, as depicted by the coefficient of variance, and intensive accelerated deterioration worsened these properties.
2. The diagnostic field survey focused on construction details, such as cracks in the paving stones, the peeling of stone layers, subsidence and cracking at the edges, and the cracking of joint filler materials. These observations helped characterize the deterioration rate of the stone pavement.
3. Based on the laboratory tests and field observations, two deterioration patterns were developed: a linear deterioration pattern and an exponential deterioration pattern.
4. These deterioration patterns were then used for life cycle cost analysis. The LCC model compared the costs under standard wear conditions (without failure) to the costs under the two accelerated wear patterns.
5. The natural stone pavement LCC approach developed in this research further enhances the state-of-the-art methodologies [21; 23], that focus on life cycle assessment and do not discuss the deterioration patterns under intensive service regimes and the outcomes of failure mechanisms.
# Highly Sensitive SPAD-Based Receiver for Dimming Control in LiFi Networks +
## 3 Single-Carrier Transmission Schemes with Dimming Control
### Variable On-Off Keying (VOOK)--Digital Dimming
#### 3.2.1 PD Receivers  
To perform VOOK-based dimming, the signal's duty cycle, \(\delta_{d}\), has to be reduced leading to shorter pulses for lower dimming levels than its analogue counterpart, resulting in reduced spectral efficiency as can be seen from Table 1. For instance, when \(\gamma=20\%\), VOOK requires a bandwidth five times higher than that of OOK to maintain a constant data rate. Hence, the maximum achievable data rate for VOOK is limited by the minimum duty cycle, \(\delta_{d}^{\text{min}}\), which is directly associated with the minimum achievable dimming level. Assuming that the matched filter integrates solely over the data duty cycle, \(\delta_{d}\), and the receiver exploits the maximum available bandwidth by adapting the matched filter to different dimming levels, the output of the matched filter for bits \(1\) and \(0\) is \(P_{1}=\Re^{2}P_{R}^{2}\delta_{d}T\) and \(P_{0}=0\), respectively. In the presence of zero-mean Gaussian noise and binary symbols with equal probabilities, the bit error rate (BER) for the VOOK system employing a PIN PD detector can be computed as follows [43]:  
\[\text{BER}_{\text{PIN}}^{\text{vook}}=\mathcal{Q}\left(\sqrt{\frac{\delta_{d} \zeta^{2}3\Re^{2}\
u^{2}\overline{P}_{\text{TX}}^{2}}{\frac{8\text{KT}^{0}R_{ L}}{R_{L}}}}\right). \tag{16}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\boldsymbol{\gamma}\) **(\%)** & **Codeword** \\ \hline
100 & _dddddddddd_ \\
80 & _dddddddd_ \\
60 & _dddddd_ \\
40 & _dddd_ \\
20 & _dd0000000_ \\
0 & 0000000000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: VOOK codewords.  
Solving (16) for \(R_{b}\) at the target BER, \(P_{\text{e}}^{\text{th}}\), and noting the data rate limitation imposed by \(B_{\text{max}}\) and \(\delta_{d}\), VOOK maximum achievable data rate for the PD detector can be written as  
\[R_{\text{max}}^{\text{vocab}}=\text{max}\Bigg{[}\frac{\delta_{d}\zeta^{2}3 \Re^{2}\gamma^{2}\overline{P}_{\text{TX}}^{2}}{[\frac{8 KT^{0}}{R_{L}}][Q^{-1}( P_{\text{e}}^{\text{th}})]^{2}},\delta_{d}^{\text{min}}B_{\text{max}}\Bigg{]}. \tag{17}\]
## 2 Materials and Methods
### Data Collection and Analysis  
To determine the accuracy of the device for detecting termite feeding activity, all bait stations were checked by the signal reader and then followed up by manually opening the covers of stations every three months to examine the status of the information trigger bar and termite presence. The sensor integrity was checked at the same time. If the wood pieces in the information bar were attacked by worker termites, an alarm signal and red spot would appear on the screen of the handheld reader. Accuracy was evaluated by the site inspection results and signal reader's records. Four categories were assigned and recorded according to [PERSON] (2002) [18], as follows:  
TN (true negative), i.e., information bar intact (no alarm) and absence of termites;  
TP (true positive), i.e., alarm signal (red spot) and presence of termites or wood pieces attacked by termites;  
FN (false negative), i.e., no alarm with the presence of termites or no alarm with stations destroyed;  
FP (false positive), i.e., alarm signal (red spot) and absence of termites.  
Two categories of accuracy rate, including accuracy rate (AR)% and accuracy rate of positive signals (ARP)%, were calculated.  
Accuracy rate (AR)% = (TN + TP)/(TN + TP + FN + FP) \(\times\) 100%. Accuracy rate also represents the integrity (longevity) rate of device, which can be used to evaluate the durable character of the device.  
Accuracy rate of positive signals (ARP) % = TP/(TP + FP).  
Standard normal distribution (Z value) and Wilcoxon signed-rank test (\(p\) vaule) of each site were calculated with TN + TP and FN + FP response values using function \"z.test\" (package \"BSDA\") and function \"wilcox.test\" (package \"stats\") implemented in R 4.1.3, respectively.
# Feature Importances: A Tool to Explain Radio Propagation and Reduce Model Complexity
## 3 Problem Description and Model Used  
### Features and the Associated Propagation Mechanisms  
The propagation environment and the 23 features that describe it were introduced in our previous research [15, 16, 17]. The area is formed from rectangular buildings, whose dimensions (in addition to the roads' dimensions) are randomly distributed, corresponding to an urban area. All measurements are taken along the roads and at the crossroads of the area. The 23 features describe the Line of Sight path (Figure 1), the area around the receiver (Figure 2), and the positions of the transmitter and the receiver, in addition to the distances between them (Figure 3). Looking closer at the group of the 10 features of the Line of Sight path, nine [18] refer to specific segments (three features for each of the three segments created). For a transmitter placed well above the rooftop, the features describing the third segment, and primarily the position of its tallest building, are expected to have the greatest influence on the power received. The 10 th feature of this group describes the whole Line of Sight path. Its value is equal to the number of buildings that obstruct the direct ray (which is equal to five for Figure 1). These buildings act as sources for the mechanism of multiple reflections. Thus, this feature is expected to have a greater impact when the height of the transmitter is reduced. Features 11 to 18 describe the area around the receiver. As shown in Figure 2, they return the heights of the nearest buildings. If the point in question corresponds to a road, the returned value is equal to 0. Figure 1: The first 10 features [18]. Figure 2: Extraction of features 11 to 18.
# Utility-Centric Service Provisioning in Multi-Access Edge Computing
## 2 Related Work
In contrast to the mentioned papers, our paper studies the utility-centric service provisioning in MEC. The utility is a measure of user satisfaction and is defined as a non-increasing function of service latency (both network latency and processing latency) [27; 28]. Based on this utility function, in [27], the authors presented utilitarian server selection, a new method to facilitate service placement and to select the best service replica for each user request while satisfying network transit cost constraints. In [28], the authors addressed composite service placement problems and propose utility-maximizing solutions based on genetic algorithm. However, in these works, they target the cloud data centers, in which there are no hard constraints on both storage and computation resources. Moreover, they do not jointly take into account the service placement and request scheduling problems. Meanwhile, our work studies utility-centric service provisioning in MEC, which jointly optimize service placement and request scheduling decisions within both the storage and computation resource constraints in MEC systems.
# Synthetic Approaches to 1,3,4-Oxadiazole-Containing Boronic Derivatives
## 3 Synthesis of Boronic Derivatives from Reagents Containing 1,3,4-Oxadiazole Moiety  
### Direct Connection of Boron with 1,3,4-Oxadiazole Ring  
There are relatively few direct methods reported in the literature for synthesizing 1,3,4-oxadiazole boronic derivatives. This is likely due to the inherent challenges associated with introducing a boronic group onto the oxadiazole ring. These challenges arise from the electron-deficient nature of the 1,3,4-oxadiazole ring [6], which makes electrophilic substitution difficult at the C2 and C5 positions. The most common approach involves2-bromo-1,3,4-oxadiazole as a starting material and proceeds via a two-step process: metal-halogen exchange followed by electrophilic substitution with a trialkylborate (Scheme 4). The first step, bromide-lithium exchange, is performed in the presence of a strong base, \(n\)-BuLi, at low temperature (below \(-40\)\({}^{\circ}\)C) in an inert solvent such as THF or toluene. The resulting lithium intermediate then reacts with trimethyl or triisopropyl borate to introduce the boronic group. Next, the esters are cleaved under acidic conditions to obtain the boronic acid product (R = H) in yields of 84% [18] and 71% [19].  
Another useful boron derivative of oxadiazole is the MIDA boronic ester, which can be prepared via a convenient, one-pot, two-step synthesis from carboxy-MIDA boronic ester and either benzhydrazide or thiosemicarbazide. Subsequent cyclization, mediated by toluenesulfonyl chloride, affords corresponding oxadiazoles in 64% and 56% yield, respectively (Scheme 5A). The carboxy-MIDA boronic ester is synthesized from ethynyl- MIDA boronic ester through oxidation with excess periodic acid, catalyzed by RuCl\({}_{3}\).3H\({}_{2}\)O in MeCN (Scheme 5B) [20]. MIDA boronic esters offer several advantages: they are stable, easily handled, compatible with various reaction conditions, and tolerant of harsh reagents [10].