esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Proposed Approach
### Data Reconstruction Module
#### 5.1.3. Third Methodology  
The third method consists of comparing two technical sites coming from two different data (\"Notices\" and \"ODM\") with the same identification number of the notice. If the technical office in ODM contains additional information compared to the one in \"Notices\", these data are copied to reconstruct the new technical site. An example of this methodology is shown in Table 3.
# Classification of Speed Change and Unstable Flow Segments Using Geohash-Encoded Vehicle Big Data
###### Abstract  
Precise and detailed speed information is indispensable for ensuring safe and efficient transportation. This is particularly true within unstable flow (UF) segments, which are especially prone to accidents due to the significant speed variations between vehicles and across lanes, and in the context of evolving transportation systems, where autonomous and non-autonomous vehicles are increasingly mixing. To address the limitations of existing methods in providing such data, this study aims to improve the detail, accuracy, and granularity of road information for micro-segments by leveraging individual vehicle big data. The proposed approach utilizes the geohash algorithm for spatial segmentation and introduces a novel criterion for identifying UF segments based on the relationship between space mean speed (SMS) and time mean speed (TMS). The presented strategy was validated through a comprehensive analysis of DTG (Digital Tachograph) data from freight vehicles on Expressway No. 50 in the Gyeonggi region in the Republic of Korea. As a result, a total of 301 segments were identified, including 178 eastbound and 123 westbound segments. UF segments corresponded to partitions falling beyond the reference standard deviation range. Compared with VDS (Vehicle Detection System) and conzone speeds, the proposed method provided more precise and continuous speed information, surpassing those obtained from conventional link-based approaches.  
geohash; space mean speed; time mean speed; unstable segment; speed deviation Article Classification of Speed Change and Unstable Flow Segments Using Geohash-Encoded Vehicle Big Data [PERSON]  
Korea Institute of Civil Engineering and Building Technology, 283 Goyang-daero, Ilsanseo-gu,  
Goyang-si 10223, Gyeonggi-do, Republic of Korea; [EMAIL_ADDRESS]; Tel.: +82-31-910-0652
## 3 Results Analysis
### Installation Construction Stage
#### 3.8.1 Specific Analysis of the Installation Construction Stage  
Studies have shown that assembly time accounts for about 20%-50% of the total production time, and assembly cost accounts for about 20%-30% of the total manufacturingcost [92]. As a result, there is a need for construction schedule planning for the entire installation construction process [93] and the introduction of tools that can significantly enhance worker efficiency. By enhancing the use of building information modeling (BIM) in the construction of mechanical and electrical engineering layouts [82], the visualization of BIM can effectively calculate the workload on-site [94]. Moreover, it can accurately find the specific points of problems and failures in the system, and it can effectively accelerate the progress of construction [53]. Invoking BIM technology can also effectively control the number of laborers and labor time, avoiding time delays and cost overruns.  
Regarding human perception, [PERSON] et al. [95] found a statistically significant relationship between information format and task performance, with 2D atlases significantly inferior to 3 DCAD models and 3D printing. [PERSON] et al. [96] also investigated how the spatial cognition of pipefitters affects their performance in a scale model assembly task by showing that when supplemented with 3D displays, spatial participants with lower spatial cognition could maintain essentially the same efficient model assembly as participants with high spatial cognition. Therefore, this can be accomplished by attaching information to the product using one of the existing ID methods [97]. Another central issue is safe construction; the use of mechanical lifting aids can significantly reduce the risk of injuring workers by manual labor [98], and combining modular assembly with delay strategies can also reduce the risk of field operations involving injury risks such as cutting and drilling in MEP construction [98].  
The following aspect of cost reduction and efficiency is that the constructor should rationalize the flow segments to improve the installation speed [54]. New algorithms are introduced to determine the optimal number of modules and system division points to minimize the installation cost [61]. Modularization provides more rapid on-site assembly than on-site construction [55]. Reducing construction time and intensive workloads is one of the economic benefits of prefabricated buildings [81]. Studies have shown that labor costs usually account for a large share of the overall project M&E costs [98]. In this regard, further research is carried out by investigating the above-identified problems to achieve cost reduction and efficiency [99].
## 3 Methodology
### Data Cleaning  
Patents often have similar claim sets with only minor variations. Hence, the workflow removed these by calculating a similarity score. This requires first vectorizing each document \(i\) in the corpus based on a term frequency-inverse document frequency (TF-IDF) representation  
\[\mathbf{v}_{i}=\big{[}T_{ij}\big{]},i=\{1\dots N\},j=\{1\dots K\} \tag{1}\]  
Figure 3: Results from the search engines of (**a**) WIPO and (**b**) USPTO after cleaning for relevance.  
where  
\[T_{i,j}=tf_{i,j}\times\log\left(\frac{N}{df_{j}}\right) \tag{2}\]  
* \(tf_{i,j}=\) frequency of term \(j\) in document \(i\);
* \(df_{j}=\) document frequency of term \(j\);
* \(N=\) total number of documents;
* \(K=\) total number of unique terms in the corpus.  
The TF-IDF effectively reduced word \"noise\" by reducing the weight of common words and boosting the weight of terms that are distinctive to specific documents. Hence, this technique filters out generic language and highlights domain-specific terminology. Next, the algorithm computed the cosine similarity matrix based on the dot product  
\[S_{p,q}=\frac{\mathbf{v}_{p}\cdot\mathbf{v}_{q}}{\left\|\mathbf{v}_{p}\right\|\cdot\left\| \mathbf{v}_{q}\right\|} \tag{3}\]  
where \(S_{p,q}=0\) if \(p=q\) to remove self-similarity. A filter then removes one of the documents when \(S_{p,q}\geq 0.90\) for any document pair \(p\) and \(q\) in the set of \(N\) documents.  
The author selected the threshold of 0.90 in cosine similarity based on well-established standards in text mining. That is, scores above this level typically indicate near-duplicate documents. The author verified that the cutoff was stringent enough to eliminate redundant claim sets while preserving meaningful variation in innovation content. Consequently, this threshold balanced the need to remove boilerplate language common to patent families with the goal of retaining distinct technical disclosures.  
Next, the author manually reviewed all the remaining patents to assess their relevance to hydrogen-powered aviation. As an expert in patent analysis with more than 80 awarded patents, the author applied deep familiarity with claim structure, legal scope, and technical specificity to determine inclusion. This exhaustive review process ensured that only patents directly relevant to hydrogen aviation were retained. The manual screening step was time-consuming but necessary to safeguard the validity and representativeness of the final dataset used for analysis.
## 5 Discussion
### Limitations and Future Work  
#### 5.3.1 The Psychological Literature Does Not Always Adequately Address Real-World Demands  
We mainly derived our contextualization challenges and our requirements for technologies from basic research in cognitive psychology. As the tasks used in these studies are much simpler than the operation of industrial plants, it is unclear to what degree the results are applicable in more complex environments. An alternative approach is to derive requirements from on-site observations in the domain. Although this would certainly increase ecological validity, it also has pitfalls. It would only be possible to assess the status quo (i.e., how operators work today) and it is hard to draw conclusions about counterfactuals (i.e., what would happen if different technologies were available). People cannot provide valid reports of their cognitive processes and limitations. Moreover, they often cannot imagine what would help them when it is not yet available. Therefore, a mere focus on domain observations is insufficient, and a multi-method approach is required that also takes psychological knowledge about cognitive processes and biases into account.  
It also must be considered that the introduction of cyber-physical production systems (CPPS) will pose its own challenges for operator work [22; 23; 179]. One exemplary challenge is that the requirement of being 'in control' becomes more abstract [20]. For instance, consider anticipation. In traditional supervisory control, operators had to anticipate trends of process parameters, while in CPPS they also have to anticipate when controllers will become unable to self-regulate or the system will exhaust its adaptive capacities. Even in today's systems, people do not sufficiently understand the function of automatic controllers or the conditions that make control non-effective [187]. With more system autonomy, this challenge will be exacerbated and it will be necessary to investigate the cognitive demands specific to this new workplace.
## 2 Materials and Methods
### HRV Data Collection  
HRV is a non-invasive measure which reveals the dynamic activity of individual's ANS [25]. The participants all had 24-h ambulatory HRV recordings recorded daily over a two-week period from 26 February to 12 March 2015. Each participant was instructed on how to attach, start, and stop the recorders at the start of the study (Bodyguard2, Firstbeat Technologies Ltd., Jyvaskyla, Finland) by the site coordinator who retrieved the data from the HRV recorders and uploaded it to the FTP data collection site.  
Moreover, participants were provided instructions for notating daily activities such as sleep and waking times, etc. They were shown how to pause the HRV recorder after waking and had up to one hour to bathe before restarting the recorder. Ambu Blue Sensor L microporous disposable electrodes were located in a modified V5 position. Skation was minimized by encouraging the participants to place the electrodes at three different locations near the V5 position. The inter-beat-interval (IBI) was calculated by the HRV recorder from the electrocardiogram with a sample rate of 1000 Hz. The RR intervals were stored in the HRV recorders memory which was then uploaded to the study FTP site at the end of the study.  
The HRV data were then downloaded to a workstation and analyzed using DADiSP 6.7. IBIs less or greater than 30% of the mean of the previous four intervals were removed from the analysis record. Following the automated editing process, all recordings were reviewed manually by an experienced technician and, where needed, corrected. All of the HRV recordings were segmented into consecutive 5-min segments as established by the HRV Task Force [36] standards. Any 5-min segment with >10% of the IBIs either removed or missing were excluded from the analysis. The local time stamps in the HRV recordings were converted to Coordinated Universal Time (UTC) to insure synchronization between participants in different locations and with the magnetic field data.  
Allowing for a staggered start and end of the HRV data recording because of the different time zones of the participants, HRV data was recorded over a 15-day period in order to collect 14 full days of data. Of the planned 32,256 h of recording (96 participants \(\times\) 14 days \(\times\) 24 h), after accounting for missed recordings, data clearing and editing 95% (30,621 h.) were available. Only 7 participants had fewer than 80% of the planned hours of recording and only two of these were under 70%.
# Graphene Nanoplatelets Impact on Concrete in Improving Freeze-Thaw Resistance
###### Abstract  
Graphene nanoplatelets (GNP) is a newly nanomaterial with extraordinary properties. This paper investigated the effect of GNP on the addition on freeze-thaw (F-T) resistance of concrete. In this experimental study, water to cement ratio remained unchanged, a control mixture without GNP materials and the addition of GNP was ranging from 0.02% to 0.4% by weight of ordinary Portland cement was prepared. Specimens were carried out by the rapid freeze-thaw test, according to the current Chinese standard. The workability, compressive strength, visual deterioration and mass loss of concrete samples were evaluated. Scanning electron microscopy also applied in order to investigate the micromorphology inside of the concrete. The results showed that GNP concrete has a finer pore structure than ordinary concrete; moreover, the workability of GNP concrete reduced, and the compressive strength of specimens was enhanced within the appropriate range of GNP addition; in addition, GNP concrete performed better than the control concrete in the durability of concrete exposed to F-T actions. Specimens with 0.05% GNP exhibited the highest compressive property after 200 F-T cycles compared with other samples.  
graphene nanoplatelets; concrete; freeze-thaw cycles +
Footnote †: journal: Applied Physics Letters
## 1 Introduction  
Titanium dioxide has long been considered an excellent photocatalyst in many aspects. It has many advantages, such as insolubility in acid and alkali, nonoccurrence of light corrosion under sunlight, chemical stability, non-toxicity for humans, and the abundance of its elements in the Earth's crust [1]. When TiO\({}_{2}\) absorbs photons from the light, it generates electron-hole pairs [2; 3; 4]. Subsequently, electrons and holes react with oxygen and water which adsorb on the surface of TiO\({}_{2}\) to form the active oxygen species, such as OH radicals and O\({}_{2}\)\({}^{-}\) ions, for the catalytic reaction. TiO\({}_{2}\) exists mainly in four phases, which are anatase, rutile, brookite, and TiO\({}_{2}\)(B) [5; 6]. Anatase is thought to be the best photocatalyst, among others [7; 8]. Unfortunately, pure anatase is inhibited for use under visible light by its wide bandgap of 3.2 eV, since it can only absorb the bandgap equivalent UV light for electron excitation. Only about 4% of the energy in the solar spectrum belongs to UV light, while 45% of the energy belongs to visible light. Having this in mind, many scientists have tried to improve the photocatalytic activity by expanding the absorption spectrum into the visible light region either by doping [9; 10; 11] or by alloying to enhance light harvesting, and this approach has achieved various degrees of success. Another strategy for photocatalytic efficiency improvement is to form mixed-phase TiO\({}_{2}\). Many studies have shown that mixed anatase and rutile TiO\({}_{2}\) powders exhibit better photocatalytic activity than does either pure phase [12; 13; 14; 15]. An investigation revealed that the conduction band edge of rutile is 0.2 eV lower than that of anatase [16]. When the electrons are excited from the valence band in the mixed phase by UV light, some of the electrons in the conduction band of anatase may transfer to the conduction band of rutile and suppress the recombination of election-hole pairs [17,18]. There are studies in the literature reporting phase transformation of TiO\({}_{2}\), which may help us in preparing anatase/rutile dual phases [19, 20, 21, 22]. In this work, we synthesized mixed-phase TiO\({}_{2}\) photocatalyst by using a flat-flame chemical vapor condensation (FFCVC) method, and the mixed phases and location of the photonic center for visible-light absorption were examined. Also, we determined the trends of methylene blue degradation of samples with different ratios of anatase and rutile. We also confirm that the nanopowder we produced not only absorbs visible light, but also possesses photocatalytic activity, and we conclude the role of the anatase/rutile interface on the enhancement of photocatalytic activity under visible light illumination.
# Complete Blood Cell Detection and Counting Based on Deep Neural Networks
## 4 Experiments and Results  
### Dataset Description  
The Blood Cell Count and Detection (BCCD) dataset [47] is used in this research. It is a public dataset of annotated blood smear images for blood cell detection. It consists of 364 blood smear images with 640 \(\times\) 480 pixels and an annotation file for details on the image. Each image involves arbitrary numbers of RBCs, WBCs, and platelets. The annotation file contains the information related to annotated blood cells, e.g., the coordinates of bounding boxes and categories. Figure 5 shows one of the blood smear images in the dataset with bounding boxes and labeled blood cells.  
Figure 4: Distance-IoU (DIoU) calculation of two overlapping boxes.  
Figure 5: Original image with ground truth bounding boxes and labels.  
The dataset was randomly divided into two subsets: 80% of the images were for training, and 20% of the images were for validation. Table 1 shows the numbers of three kinds of blood cells for training and validation, respectively.
# Performance of SIMO FSO Links over Mixture Composite Irradiance Channels
## 2 Channel Model
### NZB-PE Model  
The PDF of \(I_{p,m}\) due to NZB-PE effects can be efficiently approximated as [4]:  
\[f_{I_{p,m}}(I_{p,m})=\frac{\psi_{m}^{2}}{(A_{0,m}g_{m})\psi_{m}^{2}}I_{p,m}^{ \psi_{m}^{2}-1}\;for\;0\leq I_{p,m}\leq A_{0,m}g_{m}, \tag{10}\]  
with \(\psi_{m}=w_{z,\alpha y,m}/2\sigma_{\text{mod},m}\), \(\psi_{x\,or\,y,m}=w_{z,\alpha y,m}/2\sigma_{x\,or\,y,m}\), \(g_{m}=\exp\left(\frac{1}{\psi_{m}^{2}}-\frac{1}{2\psi_{x,m}^{2}}-\frac{1}{2 \psi_{y,m}^{2}}-\frac{\mu_{x,m}^{2}}{2\sigma_{x,m}^{2}\psi_{x,m}^{2}}-\frac{ \mu_{y,m}^{2}}{2\sigma_{y,m}^{2}\psi_{y,m}^{2}}\right)\), and \(\sigma_{\text{mod},m}^{2}=\left(\frac{3\mu_{x,m}^{2}\sigma_{x,m}^{4}+3\mu_{y, m}^{2}\sigma_{y,m}^{4}+\sigma_{x,m}^{6}+\sigma_{y,m}^{6}}{2}\right)^{1/3}\). The parameters \(\mu_{x,m}\), \(\mu_{y,m}\), denote the mean values and \(\sigma_{x,m}\) and \(\sigma_{y,m}\) symbolize the variance of the horizontal and elevation displacement, respectively. Moreover, \(A_{0,m}=\left[erf(u_{m})\right]^{2}\), and the equivalent beam radius at the receiver is given as \(w_{z,\alpha y,m}^{2}=\sqrt{\pi}erf(u_{m})w_{z,m}^{2}/(2u_{m}e^{-u_{m}^{2}})\), \(u_{m=}\sqrt{\pi}r_{m}/\sqrt{2}w_{z,m}\), where \(erf(.)\) represents the error function [6] (equation (8.250.1)), while \(r_{m}\) stands for the receiver aperture radius and \(w_{z,m}\) represents the waist of the Gaussian beam spatial intensity profile. The beam-width over relatively long distances of propagation can be approximated by \(w_{z,m}\approx\theta_{m}z_{m}\), where \(\theta_{m}\) describes the increase in beam radius, during the propagation of the optical beam in the \(z_{m}\) distance away from the transmitter.
## 3 Experiments Results and Discussion  
### Setting  
A dataset of 2200 breast ultrasound images was acquired in Fudan University Shanghai Cancer Center, Shanghai, China from January 2019 to December 2019. The equipment used to obtain ultrasound images included the Aixplorer ultrasound system (SuperSonic Imagine S.A., Aix-en-Provence, France) at 7-15 MHz and the Resona 5S ultrasound system (Shenzhen Mindray Bio-Medical Electronics Co. Ltd., Shenzhen, China) at 5-14 MHz. All images were stored in DICOM format. Each ultrasound image has a tumor segmentation that has been precisely outlined by an experienced radiologist as the ground truth. The image size range is from 721 \(\times\) 496 to 931 \(\times\) 606. All images are resized to 256 \(\times\) 256 before being fed into the network.  
All images are arranged in chronological order of the patients' diagnosis. We used the first 2000 cases for training and the remaining 200 cases for testing, which ensured the independence of the patients in our training dataset and testing dataset.  
For the quantitative evaluation, our work employed the dice value (dice) (%).  
\[\text{Dice}=\ \frac{2|X\cap Y|}{|X|+|Y|}, \tag{12}\]  
where \(X\) and \(Y\) represent the same qualities as in (10).
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 3 Proposed Hybrid Graph Embedding and Adaptive Convolution Networks
### Classification  
The HGE-ACN framework integrates processed characteristics and predicts output at several levels, as shown in Figure 5 below. Features from multiple spatial and temporal dimensions are fused and classified to capture fine-grained and coarse-grained patterns. The feature vector encodes spatial-temporal dynamics of the full input sensor data sequence. Classification modules with dense layers, softmax layers, and three levels, the Word Level (individual sign recognition), Phrase Level (sign sequences), and Sentence Level (complete sentence translation), process the fused output feature vector.  
Figure 5 shows that the system delivers three-level hierarchical recognition outputs for varied use cases. Individual signs are examined by frame or short sequence to predict the most likely word for a motion. This level is ideal for language-learning and vocabulary-building apps. The phrase level recognizes small sequences of related indicators in temporal order to produce meaningful phrases. It works for brief commands and AAC. The technology generates fully structured sentences with word order changes and auxiliary phrases to match spoken and written language. Real-time communication, accessibility, and sign language translation improve at this level. The three-level output structure makes the system flexible for education, accessibility, and real-world human-computer interaction for simple and complicated sign language recognition tasks.
# Predicting Runoff from the Weigan River under Climate Change
## 2 Data Sources and Methods
### Research Methodology
#### 2.3.4 Slope Change Ratio of Accumulative Quantity  
The SCRAQ (Slope Change Ratio of Accumulative Quantity) was first proposed by [PERSON] [29], and many scholars [30; 31] have improved and applied it. This method is mainly based on the linear relationship between runoff, precipitation and evaporation during the period with an abrupt change in the year before and after, and then uses the slope change rate of each cumulative amount to calculate their contribution rate to the variation in runoff. The calculation formula is as follows:  
The Contribution Rate of Precipitation Changes to Runoff Changes:  
\[C_{P}=R_{SP}/R_{SR}=(S_{Pa}/S_{Pb}-1)/(S_{Ra}/S_{Rb}-1) \tag{6}\]  
The Contribution Rate of Temperature Changes to Runoff Changes:  
\[C_{T}=R_{ST}/R_{SR}=(S_{Ta}/S_{Tb}-1)/(S_{Ra}/S_{Rb}-1) \tag{7}\]  
The Contribution Rate of Human Activities to Runoff Changes:  
\[C_{H}=1-C_{P}-C_{T} \tag{8}\]  
where \(C_{P}\), \(C_{T}\) and \(C_{H}\) represent the contribution rates of changes in precipitation, temperature and human activities to changes in runoff, respectively. \(R_{SP}\), \(R_{SR}\) and \(R_{ST}\) represent cumulative precipitation, cumulative runoff and cumulative temperature slope change rate, respectively. \(S_{Pb}\) and \(S_{Pa}\) respectively, represent the slopes of the annual linear relationship of cumulative precipitation in the two periods before and after the inflection point, and \(S_{Rb}\), \(S_{Ra}\), \(S_{Tb}\) and \(S_{Ta}\) are the same.