esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
In this section, the repeatability and stability of the measurement with different conditions, including the measurement distance and velocity, were analyzed according to the measurement relation of circular track generator and laser tracker.  
For the dynamic limit speed, the fluctuation of the measurement result can be induced, which is related to the resolution of speed of the circular track generator and the dynamic tracking performance of the laser tracker. Because the resolution of the circular track generator is 0.01 r/s, the variation of dynamic limit speed is approximately 0.03 m/s. With the increase in measurement distance, the increasing trend of this variation is linear.  
When the measurement distance is 1 m and the measurement distance is equal (5 mm), the dynamic display value variables are respectively 0.00493 mm, 0.00442 mm, 0.00394 mm, 0.00419 mm, and 0.00372 mm in the multiple identical experiments. According to six measurement results, the steady variate of dynamic display value can be calculated by subtracting the maximum from the minimum, which is 0.00121 mm. For evaluating the repeatability, the range method is employed, and the coefficient of range is 2.53. Hence, the repeatability of the dynamic display value is approximately 0.0005 mm. For the dynamic display value error represented by the difference between the measured radius and the nominal radius of the circle track generator, the stability and the repeatability were respectively 0.003 mm and 0.001 mm after multiple measurements. Therefore, the proposed method has the superior performance.  
By analyzing the results of multiple measurements, the continuous working of the laser tracker was required for executing the dynamic measurement. However, the smallest dynamic indication error was generated when the measurement distance was 3 m. The dynamic indication error using the equal sampling frequency was smallest when the  
Figure 13: Relationship between speed and number of measurement points—(equal sampling frequency).  
measurement distance was 5 m; hence, the range of 3-5 m was quite reliable for the dynamic measurement of the laser tracker.
# Machine Learning Applications for Jet Tagging in the CMS Experiment
## 3 Jet Substructure and Deep Tagging  
As seen in the previous section, the properties of heavy-hadrons make it possible to define criteria to discriminate between jets coming from c and b quarks. In this section, we describe algorithms designed to discriminate between hadronically decaying massive SM particles and large Lorentz boosts, namely W, Z, and H bosons and top quarks, by exploiting the jet substructure that develops following the decay chain of such particles. The first methods used  
Figure 4: ROC curves of the Deeplet and DeepCSV b-tagging algorithms on tf events for which both top quark decay hadronically. In (**a**), \(\mathrm{p_{T}^{jet}}>30\) GeV, while in (**b**), \(\mathrm{p_{T}^{jet}}>90\) GeV [21].  
by the CMS Collaboration to tag boosted heavy objects (top quarks and \(\mathrm{W}/\mathrm{Z}/\mathrm{H}\) bosons) were simple selections based on the mass or jet substructure of the candidate large jet, i.e., those clustered with a radius \(R=0.8\). In the following, we refer to these jets as AK8 jets. Examples of these methods are \(m_{SD}+\tau_{32}\), \(m_{SD}+\tau_{32}+\mathrm{b}\) for the top quark, or \(m_{SD}+\tau_{21}\) for both the top quark and \(\mathrm{W}\) boson. The value \(m_{SD}\) is the mass of the jet after the _grooming_ procedure is applied. This consists in the removal of the softer and uncorrelated radiation from the total energy of the jet. The grooming algorithm used by the CMS Collaboration is a modified mass-drop tagger [23], which is a particular implementation of the soft-drop method [24]. The values of \(\tau_{32}\) and \(\tau_{21}\) are the ratio between two \(\tau_{N}\) variables, the so-called \(N\)-subjettiness [25] defined as:  
\[\tau_{N}=\frac{1}{d_{0}}\sum_{i}p_{\mathrm{T},i}\min[\Delta R_{1,i},\Delta R_ {2,i},\ldots,\Delta R_{N,i}], \tag{3}\]  
where the index \(i\) refers to the jet constituents, while the \(\Delta R\) terms are the distance in the \(\eta-\phi\) plane between a certain jet constituent and the subjets, \(N\) is the number of candidate subjet axes obtained by the exclusive \(k_{\mathrm{T}}\) clustering algorithm when forced to return exactly \(N\) jets, and \(d_{0}\) represents a normalisation constant. One can see that \(\tau_{j}\) is the average distance of the jet constituent from \(j\) axes. For this reason, this variable gives a measure of the compatibility of an AK8 jet with the presence of a given number of subjets inside of it. Another relevant algorithm is the heavy-object tagger with variable \(\mathrm{R}\) (HOTVR), which is a clustering method that makes use of a variable \(\mathrm{R}\) and implements soft radiation removal during clustering. In the last few years, other methods that more efficiently exploit the information coming from the CMS detector have been developed. In particular ML techniques have been used both to combine some of the already-discussed methods and to use particle-level variables. In the following, the three most relevant and better performing algorithms are described in detail.
## 4 Validation of Proposed Method
### Validation of Blind Detection with Frame Synchronization  
To investigate the effect of the frame synchronization process on the blind-detection process, we compared the BER of the blind-detection process alone to that with the frame synchronization. Here, we used the number of reference frames All, where the frame synchronization accuracy was 100%.  
Figure 8 shows the BER results of the blind-detection process with the frame synchronization, where the horizontal axis represents bit rate and the vertical axis represents BER. Figure 8 shows the front-side and back-side forms. Comparing Figures 7 and 8, we can  
Figure 6: Evaluation of frame synchronization for desynchronization attacks: (**a**) front-side and (**b**) back-side forms. Each line shows the results for a different embedding-strength level \(L_{\text{SSL}}\).  
Figure 7: BER of proposed blind detection with respect to bit rate: (**a**) front-side and (**b**) back-side forms. Each line shows the results for a different embedding-strength level \(L_{\text{SSL}}\).  
see that the blind-detection process with frame synchronization reduced the BER by 9% at the embedding-strength level of \(-20\) dB. On the other hand, at \(-15\) to \(0\) dB, where the accuracy of the frame synchronization was 100%, no significant difference was observed with the presence or absence of frame synchronization.
## 1 Introduction  
Over the last years, and motivated by the positive effects experienced by the organizations that collaborate, several studies have been conducted on the scope of Collaborative Networked Organizations (CNO). Distinct research projects [1; 2; 3; 4; 5] have focused on gaining a better understanding of the effective approaches to manage collaboration. One of the topics that is still challenging is the analysis of value systems alignment in CNOs management. This is motivated by the fact that, despite the potential benefits of collaboration, consortia often fail due to internal conflicts, and most of the time, conflicts appear due the existence of different prioritization of values and distinct perceptions of the outcomes of collaboration. Because perception of outcomes depends on the preferences of the evaluator, an effort to have network members with aligned value systems can be a decisive contribution for the collaboration sustainability.  
However, the literature review on CNOs topics reveals that the analysis of value systems alignment is not usually integrated in CNO management practices, in spite of several authors, e.g., [6; 7], having identified the importance of aspects such as compatible goals, complementary skills, and compatible cultures and values to the success and sustainability of an alliance. In this context the following research question was proposed: How can value systems alignment analysis be integrated in CNO management practices, and supported by information systems?  
The aim of this work is thus to develop a set of software services that allow the integration of value systems alignment analysis into the management of CNOs. As a theoretical basis for the specification of the intended software services, the V-Align framework [8] is briefly reviewed, allowing to identify how the framework can be applied in the distinct processes of CNOs management. The proposedsolution was tested in practice through a case study related to the building of a solar power plant in Charanka, India, in the context of the GloNet European research project.
## 3 Results
### Substituted Benzenes
#### 3.3.3 Phenols
However, when we treat an OH and an alkyl substitution on an equal footing, we should consequently invoke the multiple substitution parameters established previously (di-, tri-, and tetra-substituted benzene) [1]. This leads to significant differences between experimental and model values for the tri-substituted methyl-phenols (e.g., 2,3-dimethylphenol), and therefore we concluded that including an additional separate phenol correction is the proper approach. Finally, other GC methods, including the Marrero-Gani and Constantinou-Gani approaches, revealing a heat of formation for phenol of \(-\)98.3 and \(-\)97.8 kJ/mol, respectively, showed a larger deviation beyond chemical accuracy from the experimental value quoted in Table 10. It is likely, viewing the times the methods were developed, that the differences are due to the use of an experimental value like the \(-\)96.4 kJ/mol from Pedley c.s. [34], and not to an inherent difference between the models as such. This once more confirms that the selection of experimental values and their reliability is absolutely crucial when developing a GC model with chemical accuracy. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Phenols** & **Exp.** & **Model** & **Model – Exp.** & **ABS (Model – Exp.)** & **ABS (Exp – G4)** & **G4** \\ \hline Phenol & \(-\)92.5 & \(-\)92.0 & 0.5 & 0.5 & 0.2 & \(-\)92.3 \\ \hline
2-methylphenol (o-cresol) & \(-\)128.4 & \(-\)128.4 & 0.0 & 0.0 & 1.0 & \(-\)127.4 \\ \hline
3-methylphenol (m-cresol) & \(-\)132.3 & \(-\)128.4 & 3.9 & 3.9 & 6.4 & \(-\)125.9 \\ \hline
4-methylphenol (p-cresol) & \(-\)125.8 & \(-\)128.4 & \(-\)2.6 & 2.6 & 2.0 & \(-\)123.8 \\ \hline
2-ethylphenol & \(-\)145.1 & \(-\)149.0 & \(-\)3.9 & 3.9 & 0.0 & \(-\)145.1 \\ \hline
3-ethylphenol & \(-\)146.2 & \(-\)149.0 & \(-\)2.8 & 2.8 & & \\ \hline
4-ethylphenol & \(-\)144.1 & \(-\)149.0 & \(-\)4.9 & 4.9 & 1.9 & \(-\)146.0 \\ \hline
2,3-dimethylphenol & \(-\)157.2 & \(-\)158.2 & \(-\)1.0 & 1.0 & 0.9 & \(-\)158.1 \\ \hline
2,4-dimethylphenol & \(-\)162.9 & \(-\)158.2 & 4.7 & 4.7 & 2.1 & \(-\)160.8 \\ \hline
2,5-dimethylphenol & \(-\)161.6 & \(-\)158.2 & 3.4 & 3.4 & 1.3 & \(-\)160.3 \\ \hline
2,6-dimethylphenol & \(-\)161.8 & \(-\)158.2 & 3.6 & 3.6 & 1.8 & \(-\)163.6 \\ \hline
3,4-dimethylphenol & \(-\)156.6 & \(-\)158.2 & \(-\)1.6 & 1.6 & 1.4 & \(-\)158.0 \\ \hline
3,5-dimethylphenol & \(-\)161.5 & \(-\)158.2 & 3.3 & 3.3 & 2.9 & \(-\)164.4 \\ \hline averaged absolute difference & & & & 2.8 & 1.8 & \\ \hline \end{tabular}
\end{table} 
Table 10: Experimental, present GC model, and G4 model values for the heat of formation for alkylphenols. All values in kJ/mol. Entries 1, 2, and 4 show experimental values originating from [PERSON] c.s, [44]; all other data are from [PERSON] c.s. [34]. The G4 quantum results were taken from [PERSON] c.s. [19].
# The Big Picture: An Improved Method for Mapping Shipping Activities
## 5 Experimental Results and Evaluation
### Vessel Detections Based on EO Images
#### 5.2.1 Dataset  
For this evaluation, we focus again on vessel activity in European waters for the month of October 2021. EO data include images from both Sentinel-1 and Sentinel-2 for the period.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Mean FastDTW Distance (km)**} & \\ \hline
**Trajectory Gap (Hours)** & **1-3** & **Impr. (\%)** & **3-6** & **Impr. (\%)** & **6-24** & **Impr. (\%)** \\ \hline Number of trajectories & 1655 & - & 664 & - & 681 & - \\ \hline Straight-line interpolation & 1.94 & - & 5.38 & - & 16.45 & - \\ A* hist. (0.1) & 1.91 & 1.67 & 4.5 & 16.34 & 12.16 & 26.08 \\ A* hist. (0.3) & 1.91 & 1.44 & 4.47 & 16.91 & 12.11 & 26.37 \\ A* hist. (0.5) & 1.92 & 1.19 & 4.52 & 15.83 & 12.12 & 26.29 \\ A* hist. (0.7) & 1.94 & 0.3 & 4.61 & 14.21 & 12.55 & 23.67 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table Accuracy for the straight-line interpolation, the proposed gap filling method (A* hist.) and the subsequent improvement (%). The mean distance between the reconstructed trajectories and the vessel’s true path (in km) is calculated using the FastDTW metric.  
Regarding AIS data, we use datasets from both Terrestrial stations for the maps showcasing the detection of dark vessels. Furthermore, SAT-AIS are used, combined with EO detections, to demonstrate the effectiveness of the proposed approach. Our CNN model was trained with 2019 data and is able to distinguish vessels from clouds (in the case of Sentinel-2), waves, and land parcels. One of the advantages of the deep learning-based methods is that the model can be trained to detect vessels regardless of the background in contrast to the threshold-based methods. This is mostly highlighted in optical (Sentinel-2) images. A few examples of detections with high confidence can be observed in Figure 12.  
We acquire Sentinel-1 and Sentinel-2 data from the Copernicus open access hub using the SentinelSat Python API ([[https://scihub.copernicus.eu](https://scihub.copernicus.eu)]([https://scihub.copernicus.eu](https://scihub.copernicus.eu)), (accessed on 6 September 2023)). We also use the Alaska Satellite Facility repositories for Sentinel-1 imagery ([[https://asf.alaska.edu](https://asf.alaska.edu)]([https://asf.alaska.edu](https://asf.alaska.edu)), (accessed on 6 September 2023)) as a back-up repository. For Sentinel-1 data, we download IW GRD products coming from both S1A and S1B satellites, and for Sentinel-2 data, we download all bands available for S2A and S2B products. The AIS datasets were provided by MarineTraffic ([[https://www.marinetraffic.com](https://www.marinetraffic.com)]([https://www.marinetraffic.com](https://www.marinetraffic.com)), (accessed on 6 September 2023)); due to the nature of the research and due to commercial restrictions, supporting data are not available.
## 3 Methods
### Electrical Resistivity Tomography  
This geophysical prospecting technique consists of determining the distribution of the electrical resistivity of the subsoil from a very large number of measurements collected from the ground surface. The electrical resistivity quantifies how a material resists or conducts electric current [20; 21]. The different electrical behavior of geological materials allows us to obtain 2D resistivity models, making ERT one of the most effective non-destructive tools to study and characterize subsurface discontinuities [20; 22]. In recent years, this technique has attained widespread success in stratigraphic, hydrogeological, and environmental studies [3; 23; 24; 25; 26; 27; 28; 29].  
ERT involves the installation of numerous electrodes along a line (profile), with a given separation that determines the resolution and prospecting depth to be reached. A smaller separation between electrodes increases resolution, whereas a larger separation increases prospecting depth [22]. To make one measurement, only four electrodes are needed. Two of them act as 'current electrodes' and the other two act as 'potential electrodes'. The way in which the electrodes are selected is termed 'electrode configuration'. In stratigraphic studies, the Wenner-Schlumberger configuration is usually selected [30; 31]. This configuration has good behavior and stability against resistivity changes, both vertical and horizontal ones, so it is useful for the investigation of horizontal or slightly inclined layers that can present lateral changes of facies and/or verticalized structures, as in our case [30; 31].  
Electrodes are connected to the measuring equipment (resistivity-meter), and through a predefined sequence, the groups of electrodes are selected. For each electrode quadrupole, a voltage and an intensity measurement are made. With these two readings, the 'apparent' resistivity of the ground is calculated, which is attributed to a certain geometric point in the subsoil.  
We used a multi-channel, multi-electrode DC resistivity meter system manufactured by Deutsche Montana Technologie (RESECS model). The apparent resistivity values measured in the field were inverted to obtain electrical resistivity models by using the RES2 DINV software [2]. This software applies a least-squares method with a damped smoothing, modified with the quasi-Newton optimization method. This inversion method constructs a subsoil model using rectangular prisms and determines the resistivity values for each of them, minimizing the difference between the observed and calculated apparent resistivity values [2; 32]. We produced a NW-SE electrical tomography profile, with a total length of 1110 m using 112 electrodes spaced at 10 m (Figure 2).
# Cmpk2 Gene and Protein Expression in Saliva or Salivary Glands of Dyslipidemic Mice
###### Abstract  
Salivary biomarkers are promising molecules for diagnosing systemic diseases. Cytidine/uridine monophosphate kinase 2 (_CMPK2_) is associated with various systemic diseases. However, little is known about the role of the _CMPK2_ gene in saliva and dyslipidemia. This study investigated the relationship between serum lipid levels and _Cmpk2_ mRNA expression in the saliva of dyslipidemic mice. Additionally, immunofluorescence staining was employed to assess the localization of the _CMPK2_ protein in the submandibular gland. Two types of dyslipidemic mice were utilized: mice fed a high-fat and high-cholesterol (HFHC) diet and genetically dyslipidemic ApoE-deficient mice. The mice at 9 to 46 weeks were analyzed for serum lipid levels, _Cmpk2_ mRNA expression in saliva, and _CMPK2_ protein localization in the submandibular glands. Both dyslipidemic mice displayed elevated low-density lipoprotein cholesterol and total cholesterol in serum. ApoE-deficient mice apparently exhibited increased _Cmpk2_ expression in saliva. Immunofluorescence staining indicated that _CMPK2_ proteins were primarily localized in the serous acini, potentially associated with the secretion of _Cmpk2_ mRNA in saliva. These findings suggest that _Cmpk2_ mRNA increases and is detectable in the saliva of dyslipidemic mice, providing a viable experimental model to assess the potential use of _CMPK2_ as a biomarker for dyslipidemia.  
saliva; diagnosis; _Cmpk2_; dyslipidemia; lipid abnormality
## 4 Fault Current Limiting Devices
### Superconducting FCL
#### 4.2.6. Resistive Type FCL: Superconducting Magnetic Energy Storage (SMES) with the SFCL  
In [40], instead of employing the TVC in the converters, an SMES has been applied together with the SFCL, as shown in Figure 10(a). The main application of the SMES is to smooth the active power fluctuation after the fault current limiting by the SFCL. However, in [39], it has been noted that the SFCL is not solely able to effectively improve the PCC voltage.  
In [41], the authors modified the configuration of the SFCL-SMES studied in [40]. Instead of utilising two separate SCs (one in the SFCL and another in the SMES), one common SC has been employed in two DC choppers, as shown in Figure 10(b). During fault condition, the DC chopper of the FCL enters into the fault current path. After fault removal, the DC chopper of the SMES operates, and the remaining active power fluctuations are restricted as discussed in [40].  
Figure 10. (**a**) The DC-resistive superconducting fault current limiter [36]; and (**b**) the resistive-flux-coupling type superconducting fault current limiter (SFCL) [38].
# Faulty Links' Fast Recovery Method Based on Deep Reinforcement Learning
## 2 Wide-Area Measurement System Communication Network SDN Architecture  
### System Architecture Design  
Based on SDN's capability to acquire real-time global network topology and link status information, this paper designed an SDN-based WAMS communication network architecture to guide data packet forwarding within the network topology. As illustrated in Figure 1, the architecture primarily consists of the DDPG-LBBP algorithm in the application plane, SDN controllers in the control plane, and OpenFlow switches in the data plane.  
(1) Application plane: The DDPG-LBBP algorithm resides in the application plane, where it is responsible for formulating path optimization strategies within the network topology. Firstly, the DDPG-LBBP algorithm obtains network status information, such  
Figure 1: SDN-based architecture for WAMS communication networks.  
as link bandwidth, utilization, delay, and packet loss rate, through the northbound interface. Based on this information, the algorithm leverages a neural network to produce training outputs, which are used to calculate action values--specifically, link weights. The controller then computes the traffic transmission path from the source node to the destination node using these link weights. Next, the controller gathers network topology status information and forwards it to the DDPG-LBBP algorithm for reward value computation. The algorithm updates the parameters within the neural network based on feedback from the reward value. After multiple iterations, the training process is completed. Finally, upon the convergence of the DDPG-LBBP algorithm's training, the weights of all links in the network topology are determined and output. These weights are then utilized via the northbound interface to guide the controller in formulating the optimal path strategy between the source and destination nodes.
2. Control plane: The control plane is composed of SDN controller entities, which are responsible for tasks such as acquiring link information and generating flow table entries. The controller periodically acquires network topology information via the southbound interface and provides real-time network status updates to the DDPG-LBBP algorithm through the northbound interface. Based on the link weights generated by the application plane, the controller calculates the data transmission paths between nodes, issues flow table entries to each switch, and directs data forwarding to the data plane.
3. Data plane: The data plane mainly consists of network forwarding devices, such as OpenFlow switches. It features flexible flow tables and dynamic packet processing capabilities. It is responsible for dynamically configuring and managing the behavior of the data plane based on instructions from the control plane. It can handle packet forwarding, routing, and processing according to the control plane's commands.
## 2 Materials and Methods
### Turbidity Estimation of the Treated Wastewater  
Coagulation-flocculation experiment was carried out using jar test apparatus equipped with six jars. In the first stage, different coagulant dosage was used (50, 100, 200, 300, 400 and 500 mg/L) with a fixed pH of 6 and initial turbidity of 200 NTU. 150 mL diluted synthetic food processing wastewater was then added with various coagulant dosages. The solution was mixed for 1 min at 200 rpm, followed by 80 rpm of slow mixing for 30 min. After mixing, the solution was left for 1 h to allow sedimentation. The effectiveness of the coagulation process was determined via turbidity removal which was measured by using a portable turbidity meter (HACH 2100Q). Turbidity removal was calculated using Equation (3) and optimum coagulant dosage was determined. The experiments were repeated for pH variation (pH 2, 4, 6, 8, 10 and 12) at optimum coagulant dosage. pH was controlled by the addition of diluted H2 SO4 to reduce the pH and NaOH to increase the pH.  
\[\text{Turbidity removal (\%) = ((Initial turbidity - Final turbidity)/Initial turbidity) \times 100\%} \tag{3}\]