esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Evaluation of Volatile Aroma Compounds
#### 3.1.2 C\({}_{13}\)-Norisoprenoids  
The highest concentration of \(\beta\)-damascenone, a key C\({}_{13}\)-norisoprenoid, was observed in TM21-Y wine (Table 1), likely due to enhanced extraction of its carotenoid precursors. \(\beta\)-Damascenone, along with \(\alpha\)- and \(\beta\)-ionone, are prominent C\({}_{13}\)-norisoprenoids known for their floral and fruity aromas [10]. \(\beta\)-Damascenone, in particular, plays a crucial role in wine aroma due to its potent influence even at low concentrations because of its rather low odor perception threshold of 0.05 ug/L. Its scent is often compared to stewed apples, plums, honey, dried fruit, and prunes [30]. These aroma characteristics are essential in shaping the sensory profile of Teran red wine, adding to its unique aromatic complexity. Grape skins contain high levels of C\({}_{13}\)-norisoprenoid precursors (79-94% of total content in grapes) [34], but the levels of other norisoprenoids did not significantly increase after extended maceration in this study. In previous research, the highest concentrations were observed after prolonged maceration and maturation [30], but no significant changes were found after unification with pre-fermentation heating [19]. The lowest \(\beta\)-damascenone, \(\beta\)-ionone, and total norisoprenoid concentrations were observed in TPHT-Y wine, suggesting that the heating process favored their degradation or resulted in the denaturation of enzymes responsible for their release. Similarly, a significant decrease in C\({}_{13}\)-norisoprenoids after a pre-fermentation heating treatment was reported [31], which aligns with our findings. Aging generally decreased the levels of norisoprenoids found in young wines, as a result of oxidation and various conversions into other forms, with the lowest total level found in TPHT-Y. The concentration of \(\beta\)-damascenone decreased in aged wines of all the treatments, likely due to rapid release from its precursors and reaction with sulfur dioxide [35]. A similar sequence of events happened to \(\beta\)-ionone, although its concentration increased in TM21-A. In fact, TM21-A contained the highest \(\beta\)-damascenone and \(\beta\)-ionone concentrations among aged wines, suggesting prolonged maceration provided the highest reserves. During aging, other C\({}_{13}\)-norisoprenoids were formed, such as actinidol derivatives, TDN, and TPB. It was noted previously that TDN forms from carotenoid breakdown during wine aging [36] and that increased TDN was observed in 'Cabernet Sauvignon' wine aged in oak barrels [37]. Positive correlations between vitispirane, TDN, and actinidol concentrationsand aging time have been reported [11; 32]. Increased actinidol levels during aging were likely due to the transformation of norisporenoid precursors [33; 38].
## 3 System Model and Description  
In this section, firstly optimal structure for shortest path selection and working of 'Dijkstra' algorithm is discussed. Then, the system models of both the existing and the proposed schemes are demonstrated. FLMPC-One and FLMPC-Two are generalized as FLMPC to elaborate the model of existing protocols. Then the propagation of the underwater acoustic signal is discussed. Later, two protocols SPF-Three and BFS-SPF-Three are proposed to make routing decisions based on 'Dijkstra and Breadth first shortest path-based' algorithm with 3-hop neighbors information. Reliability in transmission and entrance of data packets in void hole regions is eliminated. Moreover, proposed schemes efficiently reduce the EC by making routing decisions (based on a greedy approach). In next subsections, optimal structure for shortest path selection, 'Dijkstra Algorithm' and system model of both existing and proposed protocols are discussed.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 2 An Overview of Rail Defects and of the Techniques for Their Detection
### Rail Defects
#### 2.1.3 Defects Due to Rail Wear and Fatigue  
Rail wear and fatigue defects are due to wearing mechanisms of the rolling surface and/or to fatigue. Well known examples of this category are: (a) _corrugation_; (b) _rolling contact fatigue_ (RCF) damages; (c) _bolt-hole cracks_[1].  
Corrugation is related to the wearing of railhead [14] and does not compromise rolling safety, but affects both track elements and rolling stock, since it increases noise emission, loading, and fatigue [15].  
Rolling contact fatigue damages are much more severe from the point of view of structure integrity, as they could lead to complete rail failure [16; 17; 18]. Independently of any material defect, fatigue cracks initiate on (or very close to) the rail running surface [17]. The RCF damage can be further subdivided in: (a) _checking_ and possible _spalling_; (b) _shelling_; (c) _squats_ (see Figure 5) [19]. The occurrence rate of RCF is proportional to the speed of the train or to its weight [1]. To counteract RCF, the damaged rail can be prematurely removed in the most severe cases or ground to remove the surface-initiated crack. The running surface can also be lubricated; however, it is believed that fluid entrapment in metal can speed up the growth of a surface-initiated crack [16].  
Bolt-hole cracks appear in joined tracks; as shown in Figure 6, they originate on the surface closer to bolt-holes and propagate with a \(\pm 45^{\circ}\) angle from the vertical up to reaching web-railhead junctions. These defects may originate from the fretting fatigue caused by the bolt shank against the surface of bolt-holes [1].  
In welded rails, critical points requiring careful inspections are represented by welds (see Figure 7); in fact, internal defects of welds can affect structural integrity and fatigue performance, bringing rapid failures [5].  
Figure 4: Three-dimensional representation of a wheelburn defect.  
Figure 5: Three-dimensional representation of defects originating from RCF: (a) gauge corner checking and spalls can produce (b) shelling and (c) squats.
# Structural Methodologies for Distributed Fault Detection and Isolation
## 5 Case Study
### Distributed Diagnoser Using the MSO-Based Method  
For the ADAPT system, there are 258 MSO sets. To find the minimal number of shared measurements, the global MSO set selection algorithm solves an optimization problem for each subsystem. Table 3 shows the set of measurements that needs to be add for each of the subsystem diagnosers to achieve maximum possible detectability and isolability. In the first subsystem, all the faults are locally detectable and isolable, and therefore, this subsystem does not require any additional measurements from the other subsystems. For each of the other subsystems, we have to transfer exactly one measurement to achieve maximum diagnosability.  
Table 4 shows the set of MSO sets for each local diagnoser using the method only minimizes the number of shared variables, but the subsystems may require equations from the other subsystems. For example, the first subsystem in ADAPT does not require any additional measurement to detect and isolate its faults locally; however, as we can see in Table 4, this subsystem requires several equations from the other subsystems to generate residuals.  
The total time for finding all MSO sets and solving the optimization problems to find a set of MSO sets for each subsystem with minimum shared variables was 118 s, when the experiment was run on a desktop with an Intel Core i7-4790 3.60-GHz processor.
## 3 Experimental Tests
### Test Preparation  
All mechanical strengths were the average of three specimens. The compressive strength and Young's modulus were tested according to ASTM C 39 [38] and ASTM C 469 [39], respectively, at 7 and 28 days. The flexural strength and split tensile strength of each mixture were tested at 28 days only.  
Drying shrinkage tests were measured with a dial gauge suggested by the KS standard [40], which is similar to ASTM C 157 [41]. Rectangular samples of 100 \(\times\) 100 \(\times\) 400 mm were used.  
Figure 3: Material quantity change: (**a**) savings in cement and fine aggregate and (**b**) additions in total coarse aggregate.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Mix-ID**1  & \multirow{2}{*}{**W/C**} & \multirow{2}{*}{**FA/a**} & \multirow{2}{*}{**RCA, \%**} & \multirow{2}{*}{**S**} & \multicolumn{6}{c}{**Materials (kg)**} \\ \cline{5-10}  & & & & & & & **W** & **C** & **FA** & **NCA** & **RCA** & **Admixture 2 (\%)** \\ \hline I & CNC & 0.39 & 45.0 & 0 & - & 187 & 480 & 742 & 907 & 0 & 0.5 \\ II & ERA1 & 0.39 & 32.4 & 100 & 1 & 146 & 374 & 579 & 0 & 1210 & 0.7 \\ III & ERA2 & 0.39 & 38.7 & 100 & 2 & 165 & 424 & 656 & 0 & 1037 & 0.5 \\ IV & ERB1 & 0.39 & 34.3 & 50 & 1 & 152 & 390 & 604 & 622 & 537 & 0.65 \\ V & ERB2 & 0.39 & 39.5 & 50 & 2 & 168 & 431 & 666 & 546 & 472 & 0.5 \\ VI & ERR3 & 0.39 & 38.2 & 100 & 3 & 163 & 418 & 646 & 0 & 1046 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mix proportion of concrete per 1 m\({}^{3}\); W/C: Water to cement ratio; FA: Fine aggregate; NCA: Natural coarse aggregate, a: Total aggregate (FA+NCA+RCA).  
The shrinkage strain was evaluated by an absolute digmatic indicator (ID-S112 model made from Mitutoyo in Japan) with an 0.001 mm resolution. The samples were maintained in an environmental chamber (20 \({}^{\circ}\)C and 60% Relative Humidity).
## 6 Results and Discussions  
### SVM-Based Power Curve Model  
The pre-processed and air density corrected training SCADA data points (described in Section 6) were used to train the SVR model based on the above-described methodology and then testing datasets were used to validate the effectiveness of the model. The training and testing datasets of Table 1 are used to train and validate the SVM power curve model where wind speed used as input to estimate the power which is then plotted together as shown in Figure 4. It is worth noting that the Matlab \"fitscvm\" function with the \"OptimizeHyperparameters\" with \"automatic\" options is used to calculate the \"optimal value\" for the BoxConstraint [54], while the K-fold CV approach, as per methodology described in [55], is used to calculate the value of kernel width/scale. The SVM-based power curve model intrinsically represents fitting errors. The SVR-based power curve model is found to be continuous, smooth and accurately estimates the measured power curve below the rated power. This is further confirmed by plotting estimated and measured power values in a time-series, as shown in Figure 5, and is plotted with limited datasets for better visualisation of the results. However, SVM model accuracy depends on the data quantity and quality, as well as the appropriate method of fitting used. Hence, the performance of the SVM model above 16 m/sec suffers because of the lack of adequate quality data points.  
Figure 4: Estimated power curve and measured data.  
The differences between the observed value of the dependent variable and the estimated value are called residuals, and they are vital in determining the deviation between measured data and the regression model. The frequency distribution of the calculated residuals of the SVR power curve is plotted in Figure 6, together with a fitted Gaussian distribution and the results suggest that the distribution of SVR residuals closely follows a Gaussian distribution.  
Furthermore, statistical performance indices, namely, root-mean-squared error (RMSE), mean absolute error (MAE), and coefficient of determination (\(R^{2}\)) are used to measure the performance of the SVR fitting. The calculated values of RMSE (59.93), MAE (46.18) and \(R^{2}\) (0.99) further confirm the accuracy of the SVR power curve model.
# Regional Manufacturing Industry Demand Forecasting: A Deep Learning Approach
## 3 Research Framework
### Research Method  
LSTM was then applied to other fields [53; 54], which proved that it has strong universality. By introducing a \"gating unit\", it can solve the long-term dependence problem, including forget gate, input gate, and output gate. In addition, memory cells also play an important role in LSTM. The network structure is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Research Target** & **Influence Variables** & **Variable Explanation** \\ \hline \multirow{6}{*}{MIDF(\(\gamma\))} & FCI (X1) [26; 27; 28] & International co-operation \\  & General Public Budget Expenditure for & \\  & Science and Technology (X2) [29; 30] & Government subsidies \\  & GDP (Y3) [31; 32] & Economic development \\  & Tertiary Industry (X4) [33; 34] & Service level \\  & Number of Patent Applications & \\  & Granted(X5) [38; 39] & Technical protection \\  & Internal Expenditure on R\&D (X6) [40] & Technology input \\  & Sales Revenue of New Products (X7) [41; 42] & Technology output \\ \hline \hline \end{tabular}
\end{table}
Table 1: The index system for MIDF.  
Figure 4: The network structure of LSTM.  
Figure 3: The index system for MIDF.  
For the same LSTM, its \"At-i structure\" is fixed and shares the same parameters. The upper flow chart in Figure 3 is the specific process of \"At-i structure\" mapping. The steps are as follows:  
Step 1--Forgetting gate: it can judge whether historical information needs to be forgotten or not. The formula is as follows:  
\[f_{t-1}=\sigma\Big{(}W_{f}*[h_{t-2},x_{t-1}]+b_{f}\Big{)} \tag{1}\]  
where \(\sigma\) is the _sigmoid_ function, \(W_{f}\) is the weight matrix of the variable, \(h_{t-2}\) is the hidden layer output at \(t-2\), \(x_{t-1}\) is the input at \(t-1\), and \(b_{f}\) is the deviation vector.  
Step 2--Input gate: the input gate determines what information will be retained in the memory unit. The formulas are as follows:  
\[i_{t-1}=\sigma(W_{i}*[h_{t-2},x_{t-1}]+b_{i}) \tag{2}\]  
\[d_{t-1}=\sigma(W_{c}*[h_{t-2},x_{t-1}]+b_{c}) \tag{3}\]  
\[C_{t-1}=f_{t-1}*C_{t-2}+i_{t-1}*d_{t-1} \tag{4}\]  
where \(C_{t-2}\) and \(C_{t-1}\) are the values of \(t-2\) and \(t-1\) time memory units, respectively, and \(i_{t-1}*d_{t-1}\) is the updated value of the memory unit.  
Step 3--Output gate: it consists of two parts. First, the output information is determined by \(h_{t-2}\), \(x_{t-1}\) and the activation function. Then, the _tanh_ activation function acts on the memory unit \(C_{t-1}\) and multiplies it to get the output \(h_{t-1}\). The formulas are as follows:  
\[y_{t-1}=\sigma\big{(}W_{y}*[h_{t-2},x_{t-1}]+b_{y}\big{)} \tag{5}\]  
\[h_{t-1}=y_{t-1}*\tan\ln(C_{t-1}) \tag{6}\]  
By the above iteration, with the \(x_{t}\) input, the model can predict the future value \(y_{t}\).  
Due to the ingenious setting of the forget gate, input gate, output gate, and memory unit, the LSTM network can retain useful information.
# Future Is Unlicensed: Private 5G Unlicensed Network for Connecting Industries of Future
## 3 Opportunities of NR-U for Industry 4.0
### Device Density and QoS  
The introduction of IoT devices for automation purposes in factories, offices, and warehouses has increased the number of sensors per square meter, making a high capacity 5G network almost mandatory for industrial applications. 5G networks have promised to accommodate one million devices per square kilometer. Moreover, the present wireless communicating systems and computers available in the industrial field still lack the level of QoS required for mission-critical applications. Thus, with NR-U, system performance and resource use for different mission-critical services can be tailored to the specific needs within the private network deployment (network slicing). For example, the traffic coming from a UAV will be on a different (and higher priority) network slice than the one serving visitors in an office.
## 4 Experimental Results and Analysis
### Ablation Experiment  
To verify the necessity of each step of the proposed algorithm, we conduct ablation experiments. Non-F-RGM means that the image is segmented directly by RGM and without being processed by the hybrid spatial filter. F-RGM means that RGM is only performed on the original image and the filtering layer. Non-F-RGIIM means that the original image is directly segmented using the RGIIM. Proposed represents our proposed complete algorithm. We perform ablation experiments on the 10 test images described in Figure 7. The number of thresholds is set to \(K\) = 2, 3, 4, 5, respectively.  
Table 4 shows the results of the ablation experiments. The best results are marked in bold. Comparing with Non-F-RGM, the average \(U\) value of F-RGM is larger. It means that our proposed hybrid spatial filter has significantly influence on the improvement of segmentation results. Comparing with Non-F-RGM, the average \(U\) value of Non-F-RGIIM is improved more significantly. It indicates that the proposed RGIIM is equally important for the improvement of segmentation results, and the idea of optimizing the threshold with interval iteration is effective. Unsurprisingly, the proposed algorithm has achieved the highest average \(U\) value. Compared with Non-F-RGIIM, the improvement brought by the hybrid spatial filter is significant especially when \(K\) = 2, 5. The improvement is not obvious when \(K\) = 3, 4, but the result is also acceptable. In summary, every step of our proposed algorithm is indispensable for medical image segmentation.
# IoT-Based Medical Image Monitoring System Using HL7 in a Hospital Database
## 2 Materials and Methods
### Ultrasound Machine to Raspberry Pi HL7 Messaging Process  
Ultrasound technology has advanced dramatically in the last decade, enabling tremendous downsizing in ultrasound equipment design and manufacture. Presently, ultrasound technology ranges from small machines that fit in the palm of one's hand to high-end equipment that can provide complicated ultrasound testing.  
HL7 messages are used to transfer data between systems and make a sequence of segments, with individual segments or groups of segments being optional, obligatory, or repeated (HL7 cardinality). The message timing diagram in Figure 3 illustrates how the ultrasound machine (ULSM) and RSPI3 interact with one another. They interact with each other on demand via intranet connection, based on order and result messages.  
Figure 3: Message timing diagram from an ultrasound machine to Raspberry Pi.  
The ULSM communicates with the RSPI3 based on messaging protocols which use the following sequence:  
1. Connection Message
2. Connection Acknowledgement
3. Order Message
4. Order Acknowledgement
5. Result Message
6. Result Acknowledgement  
They check in with each other via a connection message, NMD'NO2'NMD_NO2, before communicating. If the connection is effective, the other component responds with an acknowledgement message, ACK'N02'ACK (Figure 3) and then the connection is made. Otherwise, it will notify the user that the other machine is down or that the connection has been broken. The RSPI3 sends an order to the ULSM based on the connected patient order with that sample ID. After acquiring the order message, the ULSM places it in a queue. An acknowledgement message, ACK'OR02'ACK, is returned.  
The same procedure is used to send the observation result message (Figure 4).  
The Minimal Lower Layer Protocol (MLLP) and Socket Programming are two protocols that must be followed when transmitting HL7 (Health Level 7) messages from one end point to another. The proposed model uses socket programming processes where listener ports are open in distinct ports on both sides to accept incoming messages. Both systems are linked via intranet. RSPI3 transmits an order list from the LIS to the ULSM, which receives it and stores it in a queue on the local system. ULSM, on the other hand, sends the result to the RSPI3, which receives the list as a queue. HL7 messages are used to communicate between the two sections.  
The HL7 message is encoded in bytes and sent to the other side (Figure 5).  
Figure 4: HL7 communication process, ultrasound machine to Raspberry Pi.  
Figure 5: HL7 protocol layer-based connection.
###### Abstract  
This study investigates the impact of various nucleating agents on the crystallization behavior, thermal stability, and mechanical properties of poly(3-hydroxybutyrate-\(co\)-3-hydroxyhexanoate) (P3 HBHHx) with 6 mol% 3-hydroxyhexanoate (3 HHx) units. Nucleating agents, including boron nitride (BN), poly(3-hydroxybutyrate) (PHB), talc, ultrafine cellulose (UFC), and an organic potassium salt (LAK), were incorporated to enhance the crystallization performance. Differential scanning calorimetry (DSC) revealed that BN and PHB significantly increased the crystallization temperature and reduced the crystallization time by half, with BN exhibiting the highest nucleation efficiency. Isothermal kinetics modeled using the Avrami and Lauritzen-Hoffman theories confirmed faster crystallization and reduced nucleation barriers in nucleated samples. Polarized optical microscopy (POM) revealed that the nucleating agents altered the spherulite morphology and increased the growth rates. Under fast cooling, only BN induced crystallization, confirming its superior nucleation activity. Thermogravimetric analysis (TGA) indicated minimal changes in thermal stability, while mechanical testing showed a slight reduction in stiffness without compromising the tensile strength. Overall, BN emerged as the most effective nucleating agent for enhancing the P3 HBHHx crystallization kinetics, providing a promising strategy for improving processing efficiency and reducing the cycle times in industrial applications.  
p 2025 15 6120 +
Footnote †: journal: Applied sciences  
Article
## 3 Approach
### Demonstration Candidate Filtering
#### 3.3.2 Joint Correlation Score Function
```
Input:\(\mathcal{D}_{train}=\{x_{i},y_{i}\}\), where \(i=1,2,\cdots,n\): The training set; \(q\): the query sentences contained in \(\mathcal{D}_{train}\); \(\mathcal{M}()\): label representation function; Output:\(x_{demonstration}\): The best-suited demonstration example for each query. 1 foreach\(\{x_{i},y_{i}\}\in\mathcal{D}_{train}\)do
2 label representation: \(y_{i}\rightarrow\mathcal{M}(y_{i})\)
3 add placeholders: \([MASK]\xrightarrow[MASK]{insert}\{x_{i},y_{i}\}\)
4 add special tokens: \(\mathcal{T}_{BART}=\{[CLS],x_{i_{r}}[MASK],y_{i},[SEP]\}\)
5 fill in placeholders: \(\mathcal{T}_{BART}\xrightarrow[\underset{generation}]{\mathcal{T}_{c}}\)
6 filtering: \(\mathcal{\hat{T}}\leftarrow\underset{\mathcal{T}_{i}\in\mathcal{T}_{c}}{argmax} \mathcal{P}_{C}(y|x_{q}\oplus\mathcal{T}),\mathcal{T}\in\mathcal{T}_{c}\)
7 foreach\(q\) in \(\mathcal{D}_{train}\)do
8 for\(\{x_{in}^{1},\cdots,x_{in}^{N}\}\in\mathcal{D}_{similar}\)do
9 transform input to prompt: \(y\rightarrow\mathcal{M}(y)\) (\(x_{in}^{i},\mathcal{M}(y_{i}),t_{i})\rightarrow\mathcal{\hat{T}}(x_{in},y_{i})\)
10 foreach\(x_{i}\in\mathcal{D}_{support}\)do
11 calculate cosin similarity: \(S(x_{q},x_{i})=dist(e(x_{q}),e(x_{i}))\)
12 calculate mutual information: \(I(S_{1},S_{2})=\sum_{w_{j}\in S_{1}}\sum_{w_{j}\in S_{2}}PMI(w_{i},w_{j})\)
13 calculate correlation score: \(Score(x_{q},x_{i})=I(x_{q},x_{i})+\mathcal{S}(x_{q},x_{i})\)
14 filtering: \(\underset{x_{i}\in\mathcal{D}_{support}}{MAX}\big{\{}Score(x_{q},x_{i})=I(x_{q },x_{i})+\mathcal{S}(x_{q},x_{i})\big{\}}\)
15 return: \(x_{demonstration}\)
```  
**Algorithm 1**Demonstration filtering based on joint correlation scoring