esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Case Study: Bachelor Degree in T&IM
### T&IM Bachelor Degree and the Portuguese Accreditation Agency Recommendations  
The bachelor degree (180 ECTS credits) in Technology and Industrial Management (T&IM, [45; 46; 47]) was conceived in 2006 at the College of Engineering of Instituto Politecnico de Setubal, a Portuguese public HEL. The degree targeted mature students working in the industry sector in the region of Setubal. Considering the characteristics of the students--mature blue color workers with formal and informal skills in their area of professional expertise--and the advanced technological settings provided by the employing organizations (which include automotive, aeronautic and ship repair industries), the 2007-2012 program plan emphasized managerial contents at the expense of engineering and mathematics. This emphasis on management topics is made clear in Figure 6, a circular dendrogram representing T&IM courses and respective departments. Out of the 38 program courses, 18 belonged to the Business Sciences Department.  
Another important characteristic represented in Figure 6 is the dispersal of T&IM core and elective courses among six departments.  
Six years after it began, the Portuguese accreditation agency assessed the T&IM bachelor degree [48]. Study program data reporting for the 2007-2012 period were gathered, a self-assessment report was delivered by the HEL, and an independent panel of experts (representing A3 ES) visited and met with HEI stakeholders.  
Regarding the program plan, the A3 ES produced the following recommendations:  
1. Increase program-degree mathematical content;
2. Steer programming skills towards high-level languages with practical use;
3. Strengthen the program plan with important applied industrial management content, namely, in operations management, supply chain management, and operational research;
4. Excessive number of courses, some with little additional content;
5. Poor integration of topics taught in the different courses.  
Figure 5: Handwritten notes communicating a desirable change to the mapping in Figure 4a. Example of how broader terms curriculum mapping can be used by stakeholders during the program planning discussions.  
Concerning these recommendations, note that: (1) these are considered an _accurate_ expression of weaknesses in the 2007-2012 T&IM program plan; (2) the non-prescriptive (and somewhat vague) style of the recommendations results from accreditation criteria allowing program-degrees to adjust to different HEI missions, to student demographics, and to available resources.
## 7 Performance Evaluation and Simulation Results
In the x-axis we vary the weight \(w_{TS}\) in Equation (9) to give more or less importance to the social metric in front of the traditional QoS metrics. Therefore, in the computation of the multimetric score \(MScore\) used to arrange the available forwarding paths, see Equation (9) we have changed the weight \(w_{TS}\) as follows:  
* When \(w_{TS}=0\) we do not consider the social TS metric in the computation of the multimetric score \(MScore\). That is, the forwarding path selected is the one with the highest QoS performance no matter the level of confidence among relying users. * When \(w_{TS}=0.125\), we have \(w_{QoS}=0.875\), see Equation (10). Since there are 7 QoS metrics (see Equation (9)), in this case all metrics (i.e., each QoS metric and the TS metric) have the same weight in the computation of \(MScore\). That is, each individual QoS metric would have a weight of 0.875/7 = 0.125, which equals \(w_{TS}\). * When \(w_{TS}=1\), we only consider the social TS metric in the computation of the multimetric score \(MScore\) and we do not consider any QoS metric. * We also consider intermediate values of \(w_{TS}\), ranging from 0.2 to 0.8. Results for a Very Low Social Media Interaction. TS Normal Distribution with Mean \(\mu=1\), Standard Deviation \(\sigma=1\). In this set of scenarios, according to Figures 4-7 we can notice a low variation of values as \(w_{TS}\) grows. This has sense because although we increase \(w_{TS}\), the qualification of \begin{table}
\begin{tabular}{l c} \hline \hline Area & 520 m \(\times\) 520 m \\ Number of nodes & 27 and 54 \\ Nodes’ density & 100 and 200 nodes/km\({}^{2}\) \\ Average node speed & 2 m/s \\ Transmission range & 120 m \\ Mobility Pattern & Random Waypoint \\ MAC specification & IEEE 802.11e, EDCF \\ Nominal bandwidth & 11 Mbps \\ Simulation time & 200 s \\ Video codification & MPEG-2 VBR \\ Video bit rate & 150 Kbps \\ Number of video sources & 1 \\ Video streamed & Blade Runner \\ MANET routing protocol & QSMVM, see Section 6 \\ Transport layer protocols & RTP/RTCP/UDP \\ Maximum packet size & 1500 Bytes \\ QoS weighting values (See Equation (3)) & 1/7 \\ Tie strength (TS) normal distribution & \(\mu=1\) to \(\mu=4\), \(\sigma^{2}=1\) \\ Queue sizes & 50 packets \\ Interfering CBR traffic & 300 Kbps \\ Channel noise \(-92\) dBm & \(-92\) dBm \\ Mobility generator & Bonnmotion [30] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation settings of the scenarios evaluated. the \(\overline{TS_{k}}\) parameter of the paths \(k\) (see Equation (8)) most likely will be very low (near 0) since we are in a scenario with a very low tie strength between users. This means that QoS parameters outweigh TS in the computation of \(MScore\), see Equation (9)).
## 3 Methodology
### Visualizing Data
#### 3.2.1 SSB  
Figure 2 illustrates bigram visualizations for relevant and irrelevant documents within the SSB corpus. Figure 2a shows the bigram word cloud and frequency distribution for the irrelevant document set. Common terms such as \"electronic device\", \"energy storage\", and \"neural network\" dominate the visualizations, reflecting adjacent but unrelated technological topics. Figure 2b depicts the relevant document set, highlighting SSB-specific terminology such as \"active material\", \"positive electrode\", \"negative electrode\", and \"electrolyte layer\". These terms are core components of SSB development and are consistently more prominent in both the word cloud and frequency distribution. In this domain, the irrelevant documents exhibit broad and less focused topics, whereas the relevant documents emphasize the core concepts and terminologies critical to SSB innovation.  
Figure 3 provides a complementary visualization of the SSB corpus by using the term co-occurrence networks for irrelevant and relevant document sets. Figure 3a represents the irrelevant documents, where broad terms such as \"vehicle\", \"electronic device\", and \"terminal\" dominate the network, reflecting connections across a range of adjacent topics. The clustering in this network shows weaker thematic cohesion, with terms like \"neural network\" and \"power storage device\" loosely associated, indicating a lack of focus on SSB-specific terminology.  
Figure 3b, on the other hand, visualizes the relevant document set, where key terms such as \"solid-state battery\", \"negative electrode\", and \"solid-state electrolyte\" emerge as central nodes. The network shows strong interconnections among these core SSB terms, including related components like \"cathode\", \"anode\", \"sulfide\", and \"electrolyte layer\". These clusters reveal the thematic coherence of the relevant documents, reflecting the technical focus on SSB research and development. Hence, the term co-occurrence networks effectively highlights the structural differences between the irrelevant and relevant  
Figure 2: SSB top bigrams in (**a**) irrelevant and (**b**) relevant documents.  
document sets. The tightly knit connections in the relevant network provide a visual representation of topic specificity, while the dispersed and less focused clusters in the irrelevant network highlight the challenge of identifying relevance in a large corpus.  
Figure 3: SSB term co-occurrence for (a) irrelevant and (b) relevant documents.
# THBase: A Coprocessor-Based Scheme for Big Trajectory Data Management
## 6 Query Processing Module
### Single-Object Query  
A single-object query retrieves the trajectory \(T_{so}\) by the following parameters: \(\mathit{modid}_{so}\) and \(\mathit{tr}_{so}\), where \(\mathit{modid}_{so}\) is the MOID of a MO and \(\mathit{tr}_{so}\) denotes the time range. Since the single-object query does not contain spatial condition, it can be completed by directly scanning the corresponding rowkey range. Algorithm 1 introduces the detailed steps. Firstly, we use \(\mathit{modid}_{so}\) and \(\mathit{tr}_{so}\) to construct rowkey query range \(\mathit{rkr}_{so}\) (line 1). Next, we scan T-table through \(\mathit{rkr}_{so}\) to get intermediate results \(\mathit{irs}_{so}\) (line 1). Finally, \(\mathit{irs}_{so}\) are filtered by \(\mathit{tr}_{so}\) to get result \(T_{so}\) (it only needs to filter the start and end trajectory segments) and then return it (line 1 to 1).  
```
Input: Query MOID \(\mathit{modid}_{so}\), Query time range \(\mathit{tr}_{so}\); Output: Trajectory \(T_{so}\);
1 \(\mathit{rkr}_{so}\)\(\leftarrow\)getRowkeyRange(\(\mathit{modid}_{so}\), \(\mathit{tr}_{so}\));
2 \(\mathit{irs}_{so}\)\(\leftarrow\)T-table.scan(\(\mathit{rkr}_{so}\));
3 \(\mathit{T}_{so}\)\(\leftarrow\)\(\mathit{irs}_{so}\)filter(\(\mathit{tr}_{so}\));
4 return\(T_{so}\);
```  
**Algorithm 1**Single-Object Query
# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 3 OCR Process
### Segmentation
#### 3.2.3 Character Segmentation
The research used several evaluation metrics, such as F-score, precision, and recall, to evaluate the performance of the proposed method. The results show that the proposed method outperforms traditional methods in terms of segmentation accuracy. Some previous works have proposed segmentation-free approaches for Arabic and Urdu OCR, but they do not produce accurate results on clean text. The authors of [18] apply a machine learning model on clean Urdu and Arabic datasets, producing 91% and 86% accuracy on clean UPTI datasets. The authors of [34] review current approaches and challenges unique to Urdu OCR and suggest that future research should focus on developing more-sophisticated algorithms, improving training data, and addressing the unique challenges of the Urdu script. They also propose that integrating Urdu OCR with other technologies, such as machine learning and computer vision, would provide new opportunities for research in the field. Handwritten digit recognition is discussed in [81]. It has various applications and is used in different fields such as postal mail sorting, bank check numbering, amount processing, and number entries of various forms such as taxes, insurance, and utility bills. There are handwritten images of 10 digits in the dataset from 0 to 9, and this dataset is taken from MNIST, which contains 60,000 and 10,000 images for training and testing purposes, respectively. Then, some phases and approaches are used to process the images to train and test the model, which helps to get better accuracy and gives a maximum recognition rate. Discussed phases and approaches are preprocessing and feature extraction, and then classification is used. In classification, machine learning approaches, i.e., Decision Tree, Support Vector Machine (SVM), and Artificial Neural Network (ANN), are used. The deep learning approach implements a Visual Geometry Group model with 16 layers (VGG16 model); this model is used for deep-learning image-classification problems. By using the proposed techniques, we get better recognition and a high accuracy rate; i.e., in decision tree 86%, SVM 91%, ANN 97%, and CNN 98.84% accuracy is achieved.
# Barriers and Chemistry in a Bottle: Mechanisms in Today's Oxygen Barriers for Tomorrow's Materials
## 3 Passive Barriers
### Coatings  
The coating of bottles can be seen as an alternative form of a multilayer technology. Instead of being sandwiched between two PET-layers, the barrier layer is now on the inner or outer surface of the bottle, but the same Equation (7) applies. These coatings can be both inorganic or organic.  
Organic coatings that have been developed consist of thermosets like acrylics or epoxy-amine coatings [95; 96]. They are usually coated on the outside with a thickness of approximately 50 \(\upmu\)m [97]. They typically improve the oxygen barrier by a factor of 2. This usually does not suffice in most bottle applications, but can effectively be applied to films [4].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Barrier Polymer** & **O\({}_{2}\) Permeability (cm\({}^{3}\) mm m\({}^{-2}\) day\({}^{-1}\) atm\({}^{-1}\))** & **Source** \\  & **RH 50\%, 23 \({}^{\circ}\)C** & **Source** \\ \hline Polystyrene (PS) & 100–150 & [4] \\ Polyethylene (PE) & 50–200 & [4] \\ Polypropylene (PP) & 50–100 & [4] \\ Poly(lactic acid) (PLA) & 10 & [86] \\ Poly(vinyl chloride) (PVC) & 2–8 & [4] \\ Poly(ethylene terephthalate) (PET) & 1–5 & [4] \\ Polyamide-6 (PA6) & 1,4 & [87] \\ Poly(ethylene naphthalate) (PEN) & 0.5 & [4] \\ poly(m-xylylene adipamide) (MXD6) & 0.05 & [88] \\ Poly(vinyl alcohol) (PVOH) & 0.02–1 & [89] \\ Ethylene vinyl alcohol (EVOH) & 0.04–0.4 & [86] \\ Poly(vinylidene chloride) (PVDC) & 0.01–0.3 & [4] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Oxygen permeabilities of common polymers in packaging.  
Thin silica coatings provide an excellent oxygen barrier, capable of reducing the diffusion through PET tenfold [4]. The silica is deposited by physical vapor deposition (PVD) or plasma-enhanced chemical vapor deposition (PECVD) on the inner bottle wall. Both processes lead to a thin layer with a thickness between 10 nm and 100 nm [98; 99]. The maximum thickness of the coating is limited with respect to food safety, color and flexibility. The same PECVD-technique can also be exerted with hydrocarbons to form a thin diamond-like carbon layer [53; 100]. Both coatings have very good performances, but require high investment costs and have limited flexibility and crack resistance [98; 101]. Table 2 shows the permeabilities of the different coatings with respect to oxygen.  
As stated above, combinations between coating and multilayer is possible as well. Current flexible OLED-solutions for example exist of a combination of coatings in a multilayer structure. Typically, PET is coated with inorganic layers that are separated by organic adhesives (acrylates). This provides a decent oxygen barrier, but also a very good water barrier, which is often even more critical in organic electronics [102].
# Comparison of DFN Modelled Microfracture Systems with Petrophysical Data in Excavation Damaged Zone
## 1 Introduction
### Research Site  
The excavation damaged zone study area in ONK-TKU-3620 is located at a depth of approximately 345 m along the ONKALO(r) access tunnel (Figure 1). ONK-TKU-3620 is approximately 50 m long by 10 m wide, oriented north-south and tilted upwards at a 5% angle [3]. The south end of the study area is dominated by pensisic specimens, mostly veined gneiss (VGN) with some inclusions of diatexitic gneiss (DGN). The north end of the study area consists mainly of pegmatoid granite (PGR) [8]. Based on the 3-D lithological model of Koittola [8], the gneissic rocks make up approximately 63% of the volume, whereas the pegmatoids account for the remaining 37%. The rock mass in the study area in general was considered structurally sound, with only minor natural fracturing [8], making it suitable for studying excavation-induced damage. A lithological model of the study area, the location of the study area in ONK-TKU-3620, all drillholes in the study area, and the locations of the drill holes sampled in this investigation are shown in Figure 2.  
Figure 1: Location of ONK-TKU-3620 in ONKALO® marked with a red circle. Viewed from the south. Image courtesy of Posiva Oy.
## 3 Results
### Results from Spectral Analysis  
The graph of H/V spectral ratio analysis shows two distinct peaks, visible in Figure 8:  
* For SGL1 (basement), the fundamental frequency is at f\({}_{0}\)\(\approx\) 0.35-0.41 Hz and the amplification is A\({}_{0}\)\(\approx\) 6.1;
* For SGL2 (sixth floor), the fundamental frequency is f\({}_{0}\)\(\approx\) 2.2 Hz and the amplification is A\({}_{0}\)\(\approx\) 6.6.  
The FSR calculation between SGL2 and SGL1 using Equation (6) in the 0.1-20 Hz range, as shown in Figure 9, reveals a distinct peak at approximately 2.2 Hz, with typical damping of around 5%, indicating good structural resilience, which corresponds to the fundamental frequency of the structure.  
The results of applying the RDM (Equation (7)) are visualized in Figure 10. The frequency \(f\) is measured in Hz, while the damping coefficient \(\zeta\) is expressed as a percentage. Based on the analysis of earthquake accelerograms for SGL1 (basement), we obtained f\({}_{0}\)\(\approx\) 0.44-0.46 Hz and \(\zeta\) = 3.6-5.3%. For SGL2 on the sixth floor, these values are, respectively, f\({}_{0}\)\(\approx\) 2.3-2.7 Hz, and \(\zeta\) = 4.3-6.5%.  
Although the ambient vibration data before the earthquake are unavailable, the lower damping observed at the basement level (3.6-5.3%) is interpreted as potentially reflecting foundation flexibility or measurement artifacts due to lower motion amplitudes.  
Figure 8: Spectral characteristics of HVSR for SGL1 in the basement and SGL2 on the sixth floor.
## 3 Results and Discussion
### Dynamic Light Scattering (DLS) Analysis  
The hydrodynamic size and PDI of the biosynthesized AgNPs were investigated by DLS analysis in a colloidal solution. The average AgNPs size obtained is \(74\pm 39\) nm, with a PDI value of 0.193 (Figure 4), indicating the monodispersed nature of the formed AgNPs. DLS is one of the most commonly used techniques to measure the size characterization of NPs. Nevertheless, hydrodynamic size measurements obtained through DLS were not only conducted on the metallic core, but also with all substances that bind or covered the surface of NPs [44], which resulted in a bigger NPs sizes. Further explanation of the size characterization of AgNPs was discussed in the HR-TEM study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Species** & **Route** & **Size (nm)** & **Reference** \\ \hline _Lactobacillus_ sp. & Supernatant & 30–100 & [19] \\ _Lactobacillus acidophilus_ & Cell biomass & 30 & [35] \\ _Lactobacillus plantarum_ & Cell biomass & 33.4 & [35] \\ _L. acidophilus_ & Supernatant & 4–50 & [17] \\ _Lactobacillus delbrueckii_ & Supernatant & 54–113 & [36] \\ _Lactobacillus_ sp. & Supernatant & 14 & [37] \\ _Lactobacillus mindensis_ & Supernatant & 20 & [38] \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Lactobacillus_ mediated synthesis of silver nanoparticles.
## 3 Methodology
### Loss Function
Given the ground-truth motion flow field \(D[F]\), the loss is defined as follows:  
\[L=\sum_{i=1}^{N}\alpha^{N-i}\|D[F]-f_{i}\|_{1} \tag{8}\]  
To enhance training flexibility and balance the contribution of each loss component to the overall optimization objective, this study introduces weight coefficients into the loss formulation, incorporating four distinct terms: reconstruction loss (\(L_{Re}\) ), perceptual loss (\(L_{Pe}\)), grid regularization loss (\(L_{Grid}\)), and optical flow-guided loss (\(L_{Light}\)). The weighted total loss function is constructed as shown in Equation (9), where \(\lambda_{1}\) to \(\lambda_{4}\), respectively, control the relative importance of each loss component:  
\[L=\lambda_{1}L_{\text{Re}}+\lambda_{2}L_{\text{Pe}}+\lambda_{3}L_{\text{Grid}} +\lambda_{4}L_{Light} \tag{9}\]  
During the initial training phase, we explored various combinations of loss weights. The results indicated that moderately increasing the weight of the optical flow-guided loss (\(\lambda 4\)) helped enhance deformation guidance and improve image reconstruction quality. However, assigning excessive weights to the perceptual loss or grid regularization loss tended to cause training instability or gradient oscillations. To achieve a balance among training stability, structural accuracy, and generalization capability, we ultimately adopted an equal-weight configuration: \(\lambda_{1}=\lambda_{2}=\lambda_{3}=\lambda_{4}=1.0\). This setting demonstrated consistently good convergence and robustness across multiple validation experiments. Compared to certain weighted configurations that led to unstable training dynamics, the equal-weight strategy exhibited greater stability and controllability in practical deployment, making it a suitable and engineering-friendly choice.
# Linear Precoding Designs for MIMO VLC Using Multi-Color LEDs under Multiple Lighting Constraints
## 5 Simulation Results
Figure 6 shows the BER performance of our proposed precoder and the chromaticity-fixed precoder versus luminous flux with different interference ratios \(\eta\) when CCT = 5000 K. Compared with the chromaticity-fixed precoder, our proposed precoder always shows better BER performances since we consider the quadrangle chromaticity region. Moreover, it could be seen that the performance gap decreases with the increasing of crosstalk ratio. As the luminous flux increases, the BER curves show a down and up tendency because the clipping distortion happens when the luminous flux gets too high. Figure 5: The throughput performance of the proposed precoder for different modulation orders. PAM, pulse amplitude modulation. Figure 6: The BER performance of the proposed precoder and chromaticity-fixed precoder versus luminous flux with different interference ratios \(\eta\) when CCT = 5000 K.  
Then, we compare our proposed precoder with the conventional ZF precoder. The ZF precoder can be written as:  
\[\mathbf{F}_{ZF}=\mathbf{H}^{T}\left(\mathbf{H}\mathbf{H}^{T}\right)^{-1}\text{ diag}\left(\gamma\right). \tag{29}\]  
where \(\text{diag}(\cdot)\) denotes a diagonal matrix. The value of \(\gamma\) is optimized to satisfy the lighting constraints for indoor scenarios. The BER performances of these two precoders versus luminous flux with different CCT values is demonstrated in Figure 7. It can be seen that the performance of our proposed precoder outperforms the traditional ZF precoder, and the BER curves also show a down and up tendency. Furthermore, the BER performances of our proposed precoder for six different CCT values (i.e., 3000 K, 3500 K, 4000 K, 4500 K, 5000 K, 6500 K) when \(\eta=0.01\) are shown in Figure 8. The curves for different CCT values also own the same tendency as the above figures. With a larger CCT value, the BER performance is better. This is because the percentage of blue light is the smallest portion of  
Figure 8: The BER performance of the proposed precoder for different CCT values when \(\eta=0.01\). Figure 5.0 The BER performance of the proposed precoder and ZF precoder versus luminous flux with different CCT values when \(\eta=0.01\). white light, and it gets higher as the CCT value increases; then, the power allocation of each color becomes more balanced, and better BER performance can be achieved.
## 3 Materials and Methods
### Determination of OTA Concentration
#### 3.6.2 OTA Quantification by HPLC-FLD  
The OTA was quantified by HPLC-FLD using an Agilent 1260 Infinity II LC system (Agilent Technologies, Little Fall, DE, USA) and an Agilent Poroshell 120 EC-C18 column (3.0 mm \(\times\) 150 mm, 2.7 \(\upmu\)m) (Agilent Technology, Santa Clara, California, USA). The mobile phase consisted of 0.1% formic acid dissolved in water (A) and acetonitrile (B). The injection volume was 10 \(\upmu\)L, and the flow rate was set at 0.5 mL/min. The column was maintained at 30 \({}^{\circ}\)C. The elution program was as follows: B: 0-5 min, 50%; 5-10 min, 70%; 10-15 min, 90%; 15-18 min, 50%. The excitation and emission wavelengths for fluorescence detection were 334 nm and 460 nm, respectively.