esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Antioxidant Activity (DPPH, FRAP, CUPRAC, ABTS Assays) of Formulated Aggregates  
Antioxidant activity of tart cherry/dairy protein aggregates was achieved spectrophotometrically using DPPH, FRAP, CUPRAC and ABTS assays. All measurements were performed using a UV/Vis spectrophotometer. For all assays, calibration curves were made using Trolox. The results were expressed as umol of Trolox per 100 g of aggregates (umol TE/100 g). All aggregates were analyzed in triplicate. The DPPH method was previously described in the study by [PERSON] et al. [27]. Briefly, DPPH solution at a concentration of 0.5 mM was made, and 3 mL of that solution was added to the extract. After incubation that lasted for 15 min in a dark place, the absorbance was read at 517 nm. To determine ferric reducing ability, FRAP reagent was added to 0.2 mL of the extract, and, after it was incubated for 30 min, the absorbance was read at 593 nm. This method was described in detail elsewhere [28]. The protocol for the CUPRAC assay was previously published by [PERSON] et al. [29]. Briefly, 1 mL of CuCl\({}_{2}\) (10 mM), 1 mL of neocuproine (7.5 mM), 1 mL of ammonium acetate buffer (1 M, pH 7.0), extract and distilled water at a total volume of 1.1 mL were mixed and put in a dark place for 30 min, and then the absorbance was read at 450 nm. A slightly modified method from [PERSON] et al. [30] was used to carry out the ABTS assay. Extracts were mixed with ABTS reagent. After 95 min of incubation, the absorbance was read at 734 nm.
## 3 Implementation and Evaluation of the Method
### Sensitivity  
After making the six changes described above for each of the selected signals, the codes of the original signals and the modified signals were compared, obtaining the results in Tables 3 and 4.  
For the first modification made, the only recording that did not obtain a significant change was number six (Recording 6, Modification 1 in Table 3, i.e., \(HD=0.10\)). However, even though the change consisted in modifying the value of a single sample of the signal, the hash code obtained when applying the modification differs by 10%. The remaining five modifications guarantee that at least 40% of the bits of the hash code change. The above implies that the proposed function is quite sensitive to the input data (i.e., a change of a sample in the input signal generates a significant change in the output).
## 3 Results
### Radiation and Convection Losses  
In a TEG module, due to heat transfer, there is always radiation and convection losses. This is because some of the energy is being absorbed or reflected by air molecules. A black body, in the concept of radiation, is a surface that absorbs all incident radiation and reflects none. A black body is a perfect radiator, it has an emissivity of \(\varepsilon=1\), which is the highest possible value. However, TEG modules do not act like a black body. The effect of radiation losses on the segmented TEG model has been investigated [44]. The ideal way to reduce the heat losses, which occur due to radiation and convection, is to either use an insulating material between the legs or evacuate the module completely and use short but wide legs closely spaced [49].
###### Abstract  
Ecosystem-scale water-use efficiency (WUE), defined as the ratio of gross primary productivity (GPP) to evapotranspiration (ET), is an important indicator of coupled carbon-water cycles. Relationships between WUE and environmental factors have been widely investigated, but the variations in WUE in response to biotic factors remain little understood. Here, we argue that phenology plays an important role in the regulation of WUE by analyzing seasonal WUE responses to variability of photosynthetic phenological factors in terrestrial ecosystems of the Northern Hemisphere using MODIS satellite observations during 2000-2014. Our results show that WUE, during spring and autumn is widely and significantly correlated to the start (SOS) and end (EOS) of growing season, respectively, after controlling for environmental factors (including temperature, precipitation, radiation and atmospheric carbon dioxide concentration). The main patterns of WUE response to phenology suggest that an increase in spring (or autumn) WUE with an earlier SOS (or later EOS) are mainly because the increase in GPP is relatively large in magnitude compared to that of ET, or due to an increase in GPP accompanied by a decrease in ET, resulting from an advanced SOS (or a delayed EOS). Our results and conclusions are helpful to complement our knowledge of the biological regulatory mechanisms underlying coupled carbon-water cycles.  
water-use efficiency; phenology; gross primary product; evapotranspiration; remote sensing 2017 9 664 10.3390/rs070664
# Visualization of a Multidimensional Point Cloud as a 3D Swarm of Avatars
## 3 Methodology
### Paradigm and Semantic Assignment of Variables
Spatial coordinates are obtained by selecting the appropriate subset \(F_{\text{spatial}}\in\mathbb{R}^{k\times n}\), as shown in Equation (2):  
\[X_{\text{spatial}}=F_{\text{spatial}}\cdot X, \tag{2}\]  
while visual parameters are derived from \(F_{\text{visual}}\in\mathbb{R}^{m\times n}\), according to Equation (3):  
\[X_{\text{visual}}=F_{\text{visual}}\cdot X. \tag{3}\]  
Finally, the spatial data are further transformed for display using a view matrix \(V\), as in Equation (4):  
\[X_{\text{view}}=V\cdot X_{\text{spatial}} \tag{4}\]  
where \(V\) is typically a \(k\times k\) rotation matrix. In practical applications, particularly for interactive 3D or 4D exploration, we use an affine \((k+1)\times(k+1)\) matrix acting on homogeneous coordinates, combining both rotation and translation (see Section 3.2). This dual mapping--avatars for intuitive features, spatial positioning for technical features--provides a flexible and human-centered way to visualize high-dimensional data (see Figure 2). Guidelines and limitations. The semantic assignment should be made carefully, considering both user needs and analytical goals. Important features should not be omitted or relegated solely to PCA-based projection, as their interpretability may be lost (principal components represent blends of original variables). The division between intuitive and technical features is context-dependent and may vary by task or user group. We recommend iterative testing of assignments, consultation with domain experts, and documenting mapping choices. When PCA is used to process unassigned (anonymous) variables, users should inspect principal component loadings to understand which original features contribute most strongly to the resulting spatial dimensions. Although our current tool does not include interactive visualization of these loadings, such analysis can be performed using standard statistical software. This framework strikes a balance between the interpretability of human-centered visualization and the structural insight provided by dimensionality reduction, remaining transparent about the trade-offs involved.
## 2 Experimental Setup  
Figure 1a shows the layout of the four-axis ultra-precision lathe, which contains an air bearing spindle (C-axis), a rotary table (\(B\)-axis) and two cross slides for linear motion (\(X\)-axis and \(Z\)-axis). Figure 1b presents the realized homemade four-axis ultra-precision lathe produced in the Center for Precision Engineering, Harbin Institute of Technology, China. The specifications of the lathe are listed in Table 1. Figure 1c illustrates the configuration of sinusoidal microstructures fabrication, which demonstrates that sinusoidal microstructures are fabricated on a curved surface with a curvature of 200 mm, and the sinusoidal microstructures have a peak to peak distance of \(\lambda\) = 700 \(\upmu\)m and peak to valley distance 4A = 18 \(\upmu\)m.  
The FTS system has a stroke of 32 \(\upmu\)m, a closed-loop linearity less than 0.1% full scale and a closed-loop resolution of 10 nm. Furthermore, the repositioning accuracy is within 10 nm, the load-free resonance frequency is 4 kHz, and the bandwidth of the system is higher than 1 kHz. For the fabrication of a sinusoidal modulation microstructure, the output amplitude of a sinusoidal sweep signal is 5 \(\upmu\)m, and the bandwidth of the system is higher than 500 Hz. A single crystal natural diamond tool with a round arc edge is utilized for the fabrication of the microstructures. To avoid the interference between the diamond tool and the curved workpiece, the geometric size of the diamond tool needs to be selected according to the minimum feature size of the microstructure surface. Table 2 lists the tool parameters used in the experiment. All the experiments are conducted three times, which demonstrates high repeatability.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Spindle axial runout & \(\leq\)30 nm \\ \hline Spindle radial runout & \(\leq\)50 nm \\ \hline \(X\)-direction rail repeat positioning accuracy & 0.1/100 \(\upmu\)m/mm \\ \hline \(Y\)-direction rail repeat positioning accuracy & 0.1/100 \(\upmu\)m/mm \\ \hline Verticality of rail & \(\leq\)1\({}^{\prime\prime}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The specifications of the four-axis ultra-precision lathe.  
Figure 1: The four-axis ultra-precision lathe with rotary \(B\)-axis and FTS system. (**a**) Layout; (**b**) Realization; (**c**) Configuration of sinusoidal microstructures fabrication.
###### Abstract  
In this work, an identification technique of a simple, measurements-based SPICE (Simulation Program with Integrated Circuit Emphasis) model is presented for small low-cost Peltier cells used in thermoelectric generator (TEG) mode for low-temperature differences. The collection of electric energy from thermal sources is an alternative solution of great interests to the problem of energy supply for low-power portable devices. However, materials with thermoelectric characteristics specifically designed for this purpose are generally expensive and therefore often not usable for low cost and low power applications. For these reasons, in this paper, we studied the possibility of exploiting small Peltier cells in TEG mode and a method to maximize the efficiency of these objects in energy conversion and storage since they are economical, easy to use, and available with different characteristics on the market. The identification of an accurate model is a key aspect for the design of the DC/DC converter, in order to guarantee maximum efficiency. For this purpose, the SPICE model has been validated and used in a design example of a DC/DC converter with maximum power point tracking (MPPT) algorithm with fractional open-circuit voltage. The results showed that it is possible to obtain a maximum power of 309 uW with a Peltier cell 2 \(\times\) 2 cm at a \(\Delta\)T of 16 \({}^{\circ}\)C and the designed SPICE DC/DC converter performance proved the improvement and optimization value given by the TEG model identification.  
energy harvesting; thermoelectric generator; TEG; MPPT; DC/DC; SPICE model +
Footnote †: journal: Applied Soft. 2019, 9, 3744; doi:10.3390/app9183744
## 4 Experiments and Results
### RQ3 Which Algorithm Provides the Most Suitable Tracing?
#### 4.7.2 Sbert  
The advantage of SBERT is that it understands context. On the other hand, factors that could not be identified by SBERT are based on other words that are not important in the link. An example is CVE-2004-0629. CVE-2004-0629 is associated with CAPEC52. CAPEC52 is Embedding NULL Bytes. The description of CVE-2004-0629 is as follows:  
Buffer overflow in the ActiveX component (pdf.ocx) for Adobe Acrobat 5.0.5 and Acrobat Reader, and possibly other versions, allows remote attackers to execute arbitrary code via a URI for a PDF file with a null terminator (%00) followed by a long string [44].  
When CVE-2004-0629 is entered, SBERT recommended many of the CAPEC-IDs related to \"flash.\" This may be based on the word \"Adobe.\" In addition, since it is written as buffer overflow, there were many CAPEC-IDs recommended for buffer overflow. However, the important part of this CVE description is \"a null terminator (%00),\" and it cannot be identified unless this word is well utilized. Incidentally, TF-IDF was successful in this linking. One possible solution is to increase the amount of training data. In this study, the training data consisted of about 150 sentences. However, merely increasing the number is inadequate. Learning the relationship between CVE and CWE decreased the accuracy. Additionally, this study used CosineSimilarityLoss, but TripletLoss may be more appropriate. Therefore, finetuning the data collected by the cybersecurity group with TripletLoss should improve the accuracy.  
**RQ3. Answer**  
TF-IDF was the best algorithm overall based on the two values of recall and MRR because it can identify the security terms that are important to the link and measure.
# Leveraging Large Language Models for Sensor Data Retrieval
## 3 Architecture of the System
### Data Retrieval
#### 3.2.2 Word Embeddings Indexing  
Once the tables have undergone cleaning and formatting, the following phase involves computing the word embeddings of each column and indexing them for further searches. The top part of Figure 3 shows the word embedding indexing step.  
During the indexing step, every preprocessed column passes through an LLM, generating a dense vector for each one. The vector's dimensionality varies, typically between 384 and 1024 dimensions depending on the model employed. In cases where the column's content exceeds the token limit of the model, as described in the previous step, the content is split, and a different word embedding is created for each chunk, averaging these vectors to create a single representative embedding for the column.  
Following word embedding generation, the vectors are stored in an index using the Faiss library ([[https://faiss.ai/](https://faiss.ai/)]([https://faiss.ai/](https://faiss.ai/)), accessed on 15 January 2024). Faiss provides an efficient means of storing and executing similarity searches based on dense vectors.
# Community Structure Detection for Directed Networks through Modularity Optimisation
## 3 Results  
### Synthetic Networks  
The performance of our proposed community detection method is tested against other established methods in literature using synthetic networks generated by the Lancichinetti-Fortunato-Radicchi (LFR) benchmark [28]. We have generated seven non-overlapping directed unweighted networks with the following parameters: 500 nodes; default minus exponent for degree sequence and community size distribution, \(t1=2\) and \(t2=1\), respectively; maximum in-degree \(mask=50\); and minimum and maximum number of nodes per community, \(minc=20\) and \(maxc=100\), respectively. Each of the networks had a different mixing parameter (\(\mu\in\{0.1,0.2,\ldots,0.7\}\)), where larger \(\mu\) indicates more connections across different communities. Overall, the collection of synthetic networks used allows validating the efficiency of our algorithm in networks with different properties of community structure.  
We compare our results to three popular algorithms that optimise modularity for directed networks: extremal optimisation [11], fast algorithm [10] and Tabu search [29], as implemented in Radatools [27]. We have executed each algorithm individually 100 times or until they reach a time limit of 24 h, and the best network division is reported for comparison. In terms of the proposed community detection approach, we solve 10 MINLPs (\(N_{MINLP}=10\)), we perform 100 iterations on the MIP step of the algorithm (\(N_{MIP}=100\)) and 60 nodes are relaxed for each solve of the MIP model (\(N_{relax}=60\)). We implemented the models in GAMS [30], we use SBB [31] to solve the MINLP models and CPLEX [32] to solve the MIP models, and the optimality gap is set to 0, i.e., global optimum is achieved if the solver is allowed to run for unlimited time. The CPU time limit for each model was set to 1500 s. The deterministic nature of the proposed approach means only one single execution is required.  
Figure 1 shows how the solution compares to the true community structure using the Normalized Mutual Information metric (NMI) [33]. We note that DiMod uncovers the true network communities even in larger mixing parameters. The Extremal Optimisation algorithm performs relatively well, while Fast algorithm and Tabu search fail to identify the ground truth communities before \(\mu=0.5\).
# Leaf Image Recognition Based on Bag of Features
## 6 Conclusions  
In this paper, we propose a new feature for plant recognition based on leaf image using DPCNN and BOF and propose a method combining BOF_SC and BOF_DP. In the proposed method, features of leaf are adopted, and SVM is taken as the classifier. Firstly, the proposed features BOF_DP were compared with the existing features on the Flavia dataset. After that, four famous leaf datasets were used to validate the performance of the proposed system. Experimental results show that BOF_DP has a better effect than other features, and our method is superior to other methods in recognition accuracy. However, to the DPCNN model, the parameters may not be optimal. In future work, we will try to find the best way to set the parameters automatically and improve the recognition accuracy.  
Writing--original draft preparation, Z.W. and J.C.; writing--review and editing, Z.W. and J.C.; project administration, Y.Z.; resources, J.K. and Y.M. All authors have read and agreed to the published version of the manuscript.  
This study was jointly funded by China Postdoctoral Science Foundation (Grant No. 2013M532097), National Natural Science Foundation of China (Grant No. 61201421), the Foundation of National Glaciology Geocryology Desert Data Center (Grant No. Y929830201), and the 13 th Five-year Informatization Plan of the Chinese Academy of Sciences(Grant No. XXH13506).  
All Authors declare that they have no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Species** & **Total Images** & **Training/Testing Images** & **Accuracy (\%)** \\ \hline
**SC**[14] & 153 & 9745 & 4839/4906 & 60.44 \\
**SIFT**[14] & 153 & 9745 & 4839/4906 & 82.52 \\
**DPCNN**[28] & 153 & 9745 & 4839/4906 & 92.81 \\
**MEW**[32] & 153 & 9745 & 4839/4906 & 84.92 \\
**PCNN**[59] & 153 & – & –/– & 91.20 \\
**Proposed Method** & 153 & 9745 & 4839/4906 & 94.19 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the proposed method with existing approaches on MEW dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Species** & **Total Images** & **Training/Testing Images** & **Accuracy (\%)** \\ \hline
**SC**[14] & 220 & 16,848 & 8397/8451 & 53.93 \\
**SIFT**[14] & 220 & 16,848 & 8397/8451 & 72.26 \\
**DPCNN**[28] & 220 & 16,848 & 8397/8451 & 94.07 \\
**MEW**[32] & 220 & 16,848 & 8397/8451 & 84.62 \\
**GTCLC**[55] & 42 & – & –/– & 86.80 \\
**SMF**[49] & 50 & 1500 & 750/750 & 84.32 \\
**DBNs**[56] & 50 & – & –/– & 96.00 \\
**MARCH**[51] & 220 & 5720 & 2860/2860 & 86.03 \\
**ROM-LBP**[45] & – & – & –/– & 83.71 \\
**DPSRC**[57] & 220 & 16,846 & 15,746/1100 & 91.12 \\
**RSSC**[58] & 220 & 16,846 & 14,846/2000 & 90.64 \\
**RSSC**[58] & 220 & – & –/– & 92.94 \\
**Proposed Method** & 220 & 16,848 & 8397/8451 & 94.22 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of proposed method with existing approaches on ICL dataset.
# Pathological Gait Classification Using Early and Late Fusion of Foot Pressure and Skeleton Data
## 4 Dataset Used  
In this study, a publicly available foot pressure and skeleton dataset was utilized from [16]. This dataset comprises one normal gait and five abnormal pathological gaits (antalgic, lurching, steppage, stiff-legged, and Trendelenburg). Twelve healthy males participated in the data collection. All of them were laboratory staff members and fully understood the data collection system. They had watched videos of each pathological gait and trained until they became familiar with simulating them. Data collection was conducted under strict supervision. The data were acquired using a recently released single-depth camera (Azure Kinect, Microsoft, USA), along with foot pressure data collected from a pressure plate (GW1100, GHWell, Yangju-si, Republic of Korea). Although the size of the dataset was sufficient, it had certain limitations. For example, for the DL models, the size is low. Additionally, the dataset was created for only one gender. Moreover, there are no details about the dataset regarding which age groups participated, and the dataset was not created by real patients.
# Analytical Evaluation of Laser Cleaning Effectiveness in the Context of Contemporary Muralism
## 2 Materials and Methods
### Materials
#### 2.1.3 Cleaning Method  
Each sample was divided into three sections (Figure 1b). The first section was cleaned using the Q-Switch laser, the second section was cleaned using the Long Q-Switch, and the last section was preserved for future use. Operational parameters are listed in Table 4.  
The cleaning process was performed using the parameters presented in the table.  
Laser pulse overlap (O) is calculated with the formula in [27]:  
\[\text{O}=\left(1-\frac{\text{v}_{\text{s}}}{\text{d}_{\text{f}}\times\text{f} _{\text{REP}}}\right)\times 100\%,\]  
where v\({}_{\text{s}}\) is the scanning velocity, d\({}_{\text{f}}\) is the circular focus diameter, and f\({}_{\text{REP}}\) is the repetition rate (frequency). For the QS laser instrument, the scanning velocity was estimated to be approximately 25 mm/s, and the pulse overlap on the surface was about 50%. Similarly, the scanning speed of the LQS laser was estimated to be around 10 mm/s, resulting in an overlap area of about 55%. It should be noted that the scanning speed is based on the experience of the laser operator and is not fixed, allowing for real-time adjustments to optimize cleaning results.  
Initially, one cleaning cycle was conducted for each sample and, if necessary, the process was repeated up to a maximum of three cycles to achieve an acceptable result while avoiding severe damage to the bottom layers. After every cleaning cycle, the result was assessed using an optical microscope.
## 2 Materials and Methods  
### Composition of Nanocomposites  
Two types of single-wall carbon nanotubes were used to fabricate nanocomposites. They differed in the method of synthesis and, accordingly, in morphology. Nanotubes of the first type (SWCNT1) were synthesized by the electric arc method on a Ni/Y catalyst, purified in air with washing with HCl, and functionalized with carboxyl groups in an HNO\({}_{3}\)/H\({}_{2}\)SO\({}_{4}\) mixture, with washing until neutral. The average nanotube diameter was 1.4-1.8 nm, the length was \(\sim\)0.3-0.8 \(\upmu\)m, and the specific surface area of the product was 400 m\({}^{2}\)/g. The purity of SWCNT1 was 97%. Nanotubes of the second type (SWCNT2) were synthesized by gas-phase synthesis and purified similarly to the first type of SWCNT1.  
The average diameter was \(\sim\)2-3.5 nm, their length was more than 5-10 \(\upmu\)m, and the specific surface area was 420 m\({}^{2}\)/g. The purity of SWCNT2 was 91%.  
As a matrix for nanocomposites, proteins were used: powders of lyophilized bovine serum albumin (99% purity, BioClot, Germany) and bovine collagen Type II (99% purity, MakMedi Ltd., Moscow, Russia).