esa-sceva/satcom-chunk-collection huggingface dataset:
# Industry 4.0 Roadmap: Implementation for Small and Medium-sized Enterprises
## 1 Introduction  
Small and medium-sized enterprises (SMEs) have been facing tremendous challenges since 2011 due to the Fourth Industrial Revolution in the form of emerging Industry 4.0 technologies and their applications for the industrial sector [1; 2; 3]. Professor [PERSON], Founder and Executive Chairman of the World Economic Forum, coined the phrase \"Fourth Industrial Revolution\" in 2015 in an article published by Foreign Affairs [4]; it was also the subject of his book published in 2017, in which [PERSON] highlights a grave concern: that organizations might be unable to adapt [5]. A literature review in recent years shows that SMEs have to be considered separately from large enterprises with regard to Industry 4.0 implementation, because they are less capable of coping with financial, technological and staffing challenges than large companies [6; 7; 8]. This is something that has been identified not only in research works, but also in government initiatives, such as \"Industrie 4.0\" in Germany, which have highlighted this issue [9]. This has resulted in technology-transfer projects specifically for SMEs to pass on Industry 4.0 solutions and in extensive research works concerning SMEs and Industry 4.0, especially in the first two of the three following areas:* The identification of key Industry 4.0 technologies, challenges and the associated benefits for SMEs (1);
* The maturity level of implementation or classification based on the level of Industry 4.0 implementation in SMEs (2);
* A process model or roadmap for the implementation of Industry 4.0 technologies (3). Most of these research works aim to investigate SMEs from a theoretical point of view. There is, however, a gap in the practical viewpoint, which is greatly needed for business-oriented implementations, as shown in Table 1. This study aims to develop the research in this field by researching the most efficient way to transform old-fashioned manufacturing from a practical point of view. Additionally, the few roadmaps described for the implementation of Industry 4.0 lack a focus on SMEs, which leads to a gap in this area, as shown in Table 1.  
Access to Industry 4.0 technologies must be simplified for SMEs, especially for microenterprises. SMEs cannot afford to lose money, and they therefore need to choose how to invest it very carefully. SMEs need to hit targets when choosing technologies for the transformation to Industry 4.0, and this research focuses on developing a roadmap to implement Industry 4.0 components in an assembly line, describing some practical examples for each technology, allowing an outdated manufacturing line to be turned into a Smart Assembly Line (SAL).
# Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection
## 3 Experimental Results
### System Evaluation Standard
#### 3.1.1 PASCAL VOC 2007 Challenge  
In the experiment, we adopted the system evaluation in PASCAL VOC 2007 Challenge. It is an evaluation standard for object detection, recognition, and classification in computer vision. The full name is \"The Pattern Analysis, Statistical Modeling and Computational Learning Visual Object Classes 2007 Challenge\".  
There is a positive correlation between the assessment of the system and the ability to correctly detect the target. It uses the overlapping area of the detected bounding box, predicted by the system, and ground-truth bounding box, provided by the database, to determine whether it correctly detects the goal, as given in (10). In Figure 20, the yellow line is the ground-truth bounding box (\(B_{gt}\)) provided by the database, and the red line is the detected bounding box (\(B_{det}\)), predicted by the system.  
\[\frac{Area\left(B_{det}\cap B_{gt}\right)}{Area\left(B_{det}\cup B_{gt}\right) }\geq 0.5 \tag{10}\]  
Figure 19: Illustration of the neural network.  
After defining the condition of correct detection, we introduced the F-measure. We took pedestrian detection as an example to define some terms for different detection results. The number of correctly detected pedestrians is called the true positives; false positives represent the number of backgrounds that were wrongly detected as pedestrians; the number of pedestrians that were detected is the false negatives; and the number of real backgrounds that were also detected as non-pedestrian is the true negatives. Precision and Recall are two measurements widely used in computer vision. Precision is the measurement based on the detected results. It represents the proportion of the real number of pedestrians among all the detected bounding boxes, as given in (11).  
\[\text{Precision }=\frac{\text{True positives}}{(\text{True positives}+\text{ False positives})} \tag{11}\]  
Recall is the measurement based on the ground-truth bounding box provided by the database. It represents the proportion of correctly detected number of pedestrians among all the ground-truth bounding boxes, as given in (12).  
\[\text{Recall}=\frac{\text{True positives}}{(\text{True positives}+\text{ False negatives})} \tag{12}\]  
However, we cannot evaluate the system by precision or recall alone. If the precision is low and the recall is high, there are fewer false negatives and many false positives, as shown in Figure 21. If the precision is high and the recall is low, there are fewer false positives and many false negatives, as shown in Figure 22. Therefore, we adopted the F-measure as our evaluation standard, which can consider both precision and recall. The formula is shown in (13).  
\[\text{F}-\text{measure}=\frac{2\times\text{Precision}\times\text{Recall}}{( \text{Precision}+\text{Recall})} \tag{13}\]  
Figure 21: Low precision and high recall.  
Figure 20: Ground-truth bounding box and detected bounding box.  
In the following experiments, we used precision, recall, and the F-measure as the comparison standard.
# Vibration Reduction in a Pulse Tube-Cooled High Purity Germanium Detector
## 3 Evaluation Performance of the PTC Vibration Isolation System
Vibration acceleration peak values of the detector mounting interface were less than 5 mg in main frequency and harmonic frequency. Heat links effectively attenuate the vibration in higher frequency modes. Table 4 shows the different position and different measurement case time profile RMS test results. The detector interface vibration came from the heat links and the FPR-supporting plate. Table 3 indicates that the detector vibration level was similar to the cold head flange. Thus, the transmissibility of the cold tip was very low, which indicates that heat links were effective in reducing the vibration transmission of the cold tip. The ratios of detector mounting interface to cold tip a\({}_{\text{rms}}\) (root mean square of acceleration) were 26.8%, 15.6%, and74% in the x, y, and z axes. The detector vibration interface a\({}_{\text{rms}}\) was less than 10 mg\({}_{\text{rms}}\), which met HPGe detector requirements. To estimate the effect of mechanical vibrations from the PTC on detector energy resolution, the detector resolution and the detector signal spectrum characteristic were compared under the PTC switched on or off [15]. The thermal inertia of the detector was large enough to allow tests to be taken over a period of 10 min, with little change in detector temperature. Figure 9 illustrates that the detector signal noise was the same range from 0 Hz to 2000 Hz with the PT cooler switched on and switched off. Figure 9 indicates that the vibration of the PTC basically provided no microphone noise for the detector signal. Thus, in this paper, the vibration isolation system of the PTC met the requirement of the HPGe detectors. Figure 10 shows that detector FWHM energy resolutions of 1.31 keV and 1.98 keV were achieved at 122 keV and 1332 keV, respectively, with the cooler switched on. Figure 11 indicates the resolution improved to 1.0 keV and 1.84 keV at 122 keV and 1332 keV, respectively, under the PTC switched off. Table 5 summarizes the detector energy resolution under LN2, PT cooler on and PT cooler off conditions. Under the LN2 and PT cooler off conditions, the detector energy resolution was basically equal. The energy resolution degraded 2.5% and 8.3% at 1332 keV and 122 keV, respectively. Microphone noise and electromagnetic interference were introduced simultaneously when the cooler was on. Electromagnetic interference was the main reason for degrading detector energy \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Case1**} & \multicolumn{3}{c}{**Case2**} \\ \cline{2-7}
**Position** & **x** & **y** & **z** & **x** & **y** & **z** \\ \hline Base plate & & & & & & \\ (mgrms) & 5.1 & 4.9 & 8.5 & 4.9 & 4.3 & 5.9 \\ Cold head flange (mgrms) & 7.7 & 4.2 & 7.8 & 8.5 & 3.3 & 6.7 \\ Cold tip (mgrms) & 20.9 & 27.5 & 11.4 & & & \\ Detector interface (mgrms) & & & & 5.6 & 4.3 & 8.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Heat links isolation performance measurement results summary. Figure 8: Detector interface vibration isolation performance.
# Characterization of Coffee Silver Skin as Potential Food-Safe Ingredient
## 2 Materials and Methods
### Polyphenol Extraction, Total Polyphenol Content (TPC), and Phenolic Profile Determination in CSS  
The extraction of the phenolic fractions from CSS samples was carried out by using different solvent mixtures: deionized water and methanolic aqueous solutions at two methanol concentrations (60:40 and 70:30 _v.v._). The procedure was as follows, 1 g of sample was added to 10 mL of the solvent; the mixture was vortexed for 1 min and then centrifuged (4000 rpm for 10 min). The supernatant was recovered and filtered through 0.45 um nylon filters. For the extraction of the bound phenolic compounds, the insoluble residue from the extraction carried out with the 70% methanolic solution was recovered and re-extracted with 10 mL of a methanol:water:chloride acid solution (70:29:1), vortexed for 1 min, stirred at room temperature for 2 h, and then centrifuged and filtered by using the same conditions as before. The extracted polyphenols were then stored at \(-32\)\({}^{\circ}\)C until further analysis. These extracts were used for the evaluation of total polyphenol content and radical-scavenging activity. The analysis of the phenolic pattern was carried out on the 70% methanolic extract. The total polyphenol content (TPC) was determined following a modified procedure from [PERSON] et al. [24]. Water was added to 0.1 mL of sample extracts up to a volume of 5 mL; 500 \(\mu\)L of Folin-Ciocalteu reagent was then added. After 3 min, 1.5 mL of a 25% (\(w\)/\(v\)) Na\({}_{2}\)CO\({}_{3}\) solution was added, followed by deionized water up to a 10 mL final volume. Solutions were maintained at room temperature away from light sources for 60 min. Absorbance was measured at 765 nm using a Perkin Elmer Lambda Bio 20 spectrophotometer. Gallic acid standard solutions (Fluka, Buchs, CH, Switzerland) were used for calibration. Results were expressed as mg of gallic acid equivalents (GAE) per g of sample.  
The identification and further quantification of phenolic acids were conducted by means of HPLC-DAD analysis (mod. HPLC: AGILENT series 1200 Agilent Technologies, Milan, Italy), in accordance with the method suggested by [PERSON] et al. (2015). A reverse-phase C18 column (5 \(\upmu\) 18 100A 250 \(\times\) 4.6 mm, Phenomenex Kinetex, Bologna, Italy) was used. The solvent phases utilized were water and formic acid 5% (A), and methanol (B). The flux was fixed at 1 mL/min. Table 1 reports the gradient used for the analysis. Data for chlorogenic and caffe acid were spectrophotometrically read at 280 nm while caffeine was read at 330 nm. The quantification was determined using calibration curves in a range from 6.25 to 100 mg/L.
## 3 Proposed Method  
This paper primarily focuses on the multi-task enhancement of degraded images captured in hazy, dusty, and underwater environments. The aforementioned three image categories share several common characteristics: (1) all three categories belong to environmentally induced color-shifted images due to light refraction; (2) they all suffer from reduced clarity caused by environmental medium occlusion; (3) they exhibit similar distribution characteristics in their environmental backgrounds; (4) all three fall within the research domain of image color restoration. Despite these shared traits, there are also distinct differences: (1) the influencing environmental mediums for these categories significantly differ, with dust and haze involving particle refraction, where dust particles are larger than haze particles, while underwater images are color-shifted due to water molecule absorption; (2) variations in medium lead to different image clarity; (3) in the RGB color space, the attenuation patterns of the R, G, and B channels differ, as shown in the figure. Beyond these general distinctions and commonalities, this paper predominantly exploits neural networks to investigate both the differences and commonalities in deep feature domains among the three categories, with the aim of enhancing the enhancement performance within this domain.  
In this section, the overall network architecture employs Cycle-GAN for unsupervised learning, a structure previously applied in various unsupervised contexts [27], which we will not elaborate on extensively. The focus is on introducing the design of our generator model, as shown in Figure 1. Firstly, we provide a description and introduction to the details of FSGS-ViT and MDFN. These two modules serve as the backbone feature extraction and learning units within the network's main structure. Secondly, the Mixed-Scale Knowledge Selection Module (MKSM) is introduced, which serves as a pivotal design element within the multi-task neural network model. Lastly, we present the Adaptive Gate Mixed Module (AGMM).
## 4 Discussion
[30] used spinal cord stimulation at the C3-C4 level combined with tDCS, showing an improvement in arm function in patients with paresis after stroke. [PERSON] et al. [31] made the assumption that, by modifying the descending corticospinal influences and increasing spinal cord excitability, inhibition to the spinal motor neurons could be restored, thereby reducing clinical spasticity. Muscle stiffness is reported to increase with elevated muscle activation [8]. Therefore, its objective evaluation allows accessing the specific mechanism underlying pathology in neurological disorders [32]. We found high reliability of myotonometry over therapy. Our results are consistent with those of other researchers investigating this matter [33]. For our research, we selected arm and forearm muscles (biceps brachii captu longum and flexor carpi radialis), because of their functional importance in daily living. [PERSON] et al. [34] demonstrated that spasticity, measured as involuntary biceps brachii activation during passive elbow extension, contributes to limitations in upper limb activity in children with CP. [PERSON] et al. [35] also confirmed a connection between increased biceps brachii involuntary activation and the decrease of upper limb activity. Based on their conclusion, the relationship between the ability to voluntarily activate a muscle and activity performance explains how spasticity can contribute to the limitations of daily living activities. The normal development of postural tone in ontogenesis indicates a dependence of changes in the proximal muscle tone (in the trunk and head) on the distal muscle tension (upper and lower limbs). The passive tone increases during pregnancy in a caudo-cephalic direction. Therefore, infants are born with maximum physiological hypertoronia. Subsequently, in the first year period, a caudo-cephalic decrease of passive muscle tone is observed. On the contrary, active tone progresses in a cephalo-caudal direction, starting with the assurance of a head position at three months of age [36]. In cerebral palsy, postural tension is incorrect, and there is a misbalanced distribution of proximal muscle tone with respect to the distal [37]. The area that needs further investigation is why there was a better result for distal muscle (flexor carpi radialis) than for proximal (biceps brachii). Analyzing the general disturbances in the distribution of muscle tension in various parts of the body might bring some explanation. This indicates the need for continuing research within this field using a larger study group. There are some limitations to the present study. Firstly, the recruited group in this study was too small. The recruitment process for this study was very long, according to the exclusion criteria. However, children with CP are a small selected group inducted by tDCS. Thus, this research assumes the character of a preliminary study. Secondly, the therapy period could include a longer period (than 10 days of intervention). In a previous study, only the effect of a single session was investigated [21].
## 5 Quality of Protection (QoP) in the Proposed Security Improvement Framework (SIF)
The results of the security analysis using the QoP model allow us to define clear distinctions on the strength of protection of each security policy in the SIF security solution. Among three proposed Security Policies (\(P_{2}\), \(P_{3}\), \(P_{4}\)), \(P_{4}\) performs best. Security Policy \(P_{4}\) includes solutions of three potential security threats (Physical, Replay, and Flooding Attacks). Existing Security Policy KY AES Encryption Key, \(P_{1}\) in present Zigbee protocol, shows the lowest performance among the proposed Security Policies.
## 1 Introduction
Each polarization intensity could be modulated simultaneously and independently by the proposed method. The optimized power rate for each polarization and optimum symbol mapping were analyzed. The expected performance was simulated, and a proof-of-concept experiment was performed.
# PFC Single-Phase AC/DC Boost Converters: Bridge, Semi-Bridgeless, and Bridgeless Topologies
## 2 Categorization of Bridge Boost Converter Topologies
### BBC Topologies
Nevertheless, the addition of two semiconductors introduces two issues: (1) the increasing of losses because of the addition of more elements and (2) a complex control strategy to control \(Q_{1}\). This converter modification presents low ripple in DCM and CCM operation for low power application. Another modification to this topology was presented in [71], where two capacitors and inductors were added between the feeding source and the rectifier bridge. This converter presents low \(THD_{i}\) and high efficiency with cascade PID control for low voltage and power application. Nevertheless, the additional components increase its implementation cost. Figure 1: Conventional BBC topology with PFC. In conclusion, a BBC of conventional topology requires an intermediate filtering equipment for EMI mitigation together with a robust control strategy--this is in order to ensure a correct behavior in low and medium power applications [27; 34; 50; 69; 72; 73; 74; 75]. In addition, SBBC of conventional topology are recommended instead of BBC in applications for medium and high power due to their low EMI [16].
# First Retrieval of Sea Surface Currents Using L-Band SAR in Satellite Formation
## 4 Data Processing  
LuTan-1 is currently the only spaceborne InSAR system operating in the L-band. The configuration of the satellite formation enables optimal baseline operation over a wide temporal range. It should be noted that temporal coverage is closely related to the specific formation design, which is beyond the scope of this study. To the best of our knowledge, there are no studies that have employed LuTan-1 data for ocean current  
Figure 5: Reference data. (**a**) ECMWF wind field at 10 m height above sea surface. (**b**) CMEMS sea surface velocity. (**c**) CMEMS sea surface height.  
inversion. This study presents, for the first time, a comprehensive workflow for sea surface current inversion using LuTan-1 data. The primary steps include image registration, random phase noise suppression (multi-look processing), and removal of the cross-track phase components. Owing to the limitations in satellite navigation accuracy, distributed systems exhibit more significant orbital errors compared to single-satellite systems. In interferometric processing, this manifests primarily as baseline errors, which exacerbates the difficulty of removing the flat Earth phase and height phase. To address this issue, we propose a parameter model-based approach for residual flat Earth and height phase removal. The overall workflow for ocean current inversion using LuTan-1 data is illustrated in Figure 6. Two SAR images acquired by the distributed satellite system are co-registered to obtain a coherence map and an interferometric phase map, following the method detailed in Section 4.1. Utilizing the metadata from the satellite system, including satellite ephemeris data and local sea surface height information, the slant range difference between the two satellites for the same sea surface target is calculated. This allows the theoretical flat Earth phase to be derived. The interferometric phase is then corrected by subtracting this theoretical flat Earth phase, resulting in the residual phase denoted as \(\phi_{1}\). The residual phase \(\phi_{1}\) undergoes phase filtering and phase unwrapping processes. Subsequently, it is fitted using a weighted least-squares approach, as described in Equation (19), where the weighting coefficients correspond to the coherence values. The fitted phase is then subtracted from \(\phi_{1}\), yielding the fully flattened phase. Finally, after geocoding, the flattened phase is converted into the sea surface velocity.
## Materials and Methods
### Remote Estimation of Canopy Interceptance  
In addition to canopy reflectance, canopy interceptance (\(i_{\mathrm{o}}\)) is a crucial parameter for the estimation of SIF escape probability [22]. Although \(i_{\mathrm{o}}\) is not easily accessed directly from remote sensing measurements, it can be indirectly estimated from remotely sensed data using equations such that of [25]:  
\[i_{\mathrm{o}}=1-\exp(-G(\theta)\cdot LAI\cdot CI/COS(\theta)), \tag{6}\]  
where \(G(\theta)\) is the mean value of leaf projection function for direction \(\theta\). It is generally fixed to 0.5 for global-scale remote sensing applications [33]. LAI and CI are the leaf area index and clumping index, respectively. They are directly available from satellite remote sensing products. To explore the uncertainties of the remote sensing products on the estimation of SIF escape probability, artificial errors (\(-50\%\) and \(+50\%\) constant value error of LAI\({}^{\mathrm{*}}\)CI) were generated and then were used to estimate SIF escape probability.
## 4 Experiment
### Baseline Methods  
In this part of the work, we have performed many comparative experiments to verify the advantages of our model. The following baselines are compared to our work.  
**Logistic regression (LR)**: Logistic regression (LR) is a classic machine learning model. We trained an LR model with the features mentioned earlier to predict bridge nodes.
**Support vector machine (SVM)**: Support vector machine (SVM) is a generalized linear classifier based on supervised learning. We also used support vector machine (SVM) with a linear kernel as the classification model. The model uses the same features as the logistic regression model.
**Multilayer perceptron (MLP)**: An MLP consists of multiple fully connected layers. We built a five-layer multilayer perceptron as a representative to test its performance.
**Inf-GCN**: Inf-GCN [29] is a GCN-based method proposed to find influential nodes in complex networks, which performs very well in its area. The author claims that they can reach an F1 score of 90.7 in the best case. We used a Inf-GCN model on the bridge node detection task to compare it with our work.
**Variants of BND-GCN**: We implemented two variants of BND-GCN, denoted by BND-GAT and BND-GraphSAGE. Related parameters of these models have been introduced in the previous section.