esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
Figure 1 presents the PRISMA flowchart of this systematic review.  
A total of 1801 articles were retrieved from the initial search on Web of Science (718 articles), Scopus (869 article) and IEEE Xplore (214 articles).  
The initial step of the screening phase (i.e., step 1) yielded 1351 articles by removing the duplicates (348 articles) or the articles without abstracts (102 articles).  
Figure 1: Study flowchart.  
Based on abstracts (i.e., step 2), 390 articles were removed since they were not published in English, or they were overviews or reviews, editorials, prefaces, and announcements of special issues, workshops, or books. Moreover, 920 articles were removed because they did not report evidence of the explicit use of IT-based applications requiring smart cities' infrastructures to facilitate the mobility of older adults.  
Finally, the full texts of the remaining 41 articles were screened (i.e., step 3) and 13 articles were excluded because they did not meet the inclusion criteria. Therefore, 28 articles [68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95] were included in this systematic review.  
Of these 28 articles, some reported the same research projects: articles [79; 80; 85; 87; 88] were related to a Portuguese funded project called SmartWalk; articles [73; 77] were related to a European funded project called City4 Age; and articles [78; 93] reported on the same application.  
Considering the articles included for this systematic review, 18 were published in conference proceedings [68; 69; 70; 72; 73; 74; 79; 80; 81; 82; 83; 84; 85; 86; 87; 89; 91; 92] and ten were published in scientific journals [71; 75; 76; 77; 78; 88; 89; 90; 93; 94; 95].  
In terms of publication years, the included articles were published between 2013 (i.e., two articles [68; 69]) and 2021 (i.e., one article [95]). The diagram in Figure 2 demonstrates that the trend of the development of IT-based applications requiring smart cities' infrastructures to facilitate the mobility of older adults is increasing, and more than two-thirds of the articles (i.e., 19 articles [77; 78; 79; 95]) were published in the last four years.  
Figure 3 reveals the country of origin for all primary studies. Europe (in particular, Portugal, six articles [79; 80; 85; 87; 88], and Spain, five articles [68; 73; 75; 76; 77; 77]) contributed the largest number of articles (i.e., 20 articles [68; 69; 73; 75; 80; 83; 84; 85; 87; 88; 89; 92; 93]). In turn, Asia contributed five articles [74; 81; 82; 90; 91], North America (i.e., United States of America) contributed two articles [86; 94] and South America (i.e., Brazil) contributed one article [95].
## 1 Introduction
Yb\({}^{3+}\) is a NIR luminescence center with a \(\Delta E\) value of 300-680 cm\({}^{-1}\) for the Stark energy level pairs of the \({}^{2}\)F\({}_{5/2}\) excited state [19; 20]. This enables more accurate temperature measurements at lower temperatures than Nd\({}^{3+}\). Among various hosts for Yb\({}^{3+}\), Gd\({}_{3}\)Al\({}_{2}\)Ga\({}_{3}\)O\({}_{12}\) (abbreviated as GAGG) allows the Yb\({}^{3+}\) ions to have the highest optical conversion efficiency [21], and its thermal conductivity does not dramatically degrade compared to other pure garnets [22]. As a member of the garnet family, GAGG also has a broad transparency range, good thermomechanical properties, thermal stability, and chemical stability. Transition metal Cr\({}^{3+}\) has been repeatedly demonstrated to exhibit strong PL in various garnet hosts [23]. The Cr\({}^{3+}\)-Yb\({}^{3+}\) pair also represents a dual-center system with energy transfer capabilities [24]. Compared to Yb\({}^{3+}\) single-doped GAGG, the advantage of co-doping with Cr\({}^{3+}\) is that visible light (e.g., at 660 nm, which is within the 1 st BTW) can excite Cr\({}^{3+}\) instead of directly exciting Yb\({}^{3+}\) with infrared light. This approach enables the complete measurement of the Yb\({}^{3+}\) PL spectrum. A Yb\({}^{3+}\) and Cr\({}^{3+}\) co-doped Gd\({}_{3}\)Al\({}_{2}\)Ga\({}_{3}\)O\({}_{12}\) (GAGG) NIR thermometer was developed in this work. The optical performance of the sample at 660 nm excitation is evaluated through temperature-dependent photoluminescence spectra and the fitting calculations. The shape of the Cr\({}^{3+}\) PL spectra and the Cr\({}^{3+}\)\(\rightarrow\) Yb\({}^{3+}\) energy transfer process are discussed with Racah parameter calculations and fitting of the luminescence lifetime, respectively. This work demonstrated that Yb\({}^{3+}\) and Cr\({}^{3+}\) co-doped GAGG thermometers have the potential for accurate temperature measurement in the field of biology.
## Survival Model Development
### Choice of Distribution Function  
A basic challenge in parametric survival analyses is selecting a baseline distribution model that can best represent the observations (collected data). Furthermore, a choice between Accelerated Failure Time (AFT) and Proportional Hazards (PH) forms of a model must be made when considering the effects of covariates. In this study, an effort was made to find the most suitable baseline distribution model for the Wisconsin superstructure data. The Weibull, log-logistic, lognormal, and Hypertabastic models were evaluated and compared using AIC. As shown in Table 2, the Hypertabastic model had the lowest AIC value indicating that it was the best fitting model. The AFT model was selected over the PH because the non-parametric hazard and survival functions were intersecting, thus indicating non-proportionality of hazards.  
The main advantage of Hypertabastic survival model over the more common models such as Weibull and lognormal is in its flexibility to assume a wide variety of hazard shapes [25]. [PERSON] et al. [19] reported that the Hypertabastic hazard function can be \"increasing followed by decreasing with time, increasing toward an asymptote, monotonically decreasing with time, increasing with upward concavity followed by increasing with downward concavity, increasing with upward concavity followed by a linear increase, or continuously increasing with upward concavity\".
## 4 Experiments and Results
### MovingMNIST Dataset  
The MovingMNIST dataset [11] comprises 10,000 video sequences, each consisting of 20 frames. These sequences were artificially generated to provide a controlled environment for testing the prediction algorithms. Within each sequence, two handwritten digits, independently selected from the original MNIST database, were moved across the frame. This setup created a challenging scenario that combined the complexity of recognizing handwritten digits with the additional dimension of motion prediction.  
Each frame in the sequence was rendered at a resolution of 64 x 64 pixels, striking a balance between detail and computational efficiency. The images were single channel, that is, presented in black and white, which allows researchers to focus on shape and motion without the added complexity of color information. This monochromatic representation is particularly suitable for studying the fundamental aspects of spatiotemporal prediction without the potential confounding factors introduced by color variations.  
The motion of the digits within each sequence followed specific rules designed to create diverse and challenging scenarios:  
* Independent Movement: Each digit moved independently of the other, simulating real-world scenarios in which multiple objects can move in uncorrelated patterns.
* Consistent Velocity: Throughout a given sequence, each digit maintained a constant speed and direction. This consistency allows the evaluation of a model's ability in capturing and predicting uniform motion.
* Bouncing Mechanics: Upon reaching the edges of a frame, the digits bounced off and changed direction while maintaining their speed. This bouncing behavior introduces an element of nonlinearity into the motion patterns, challenging the prediction models to anticipate and account for sudden changes in direction.
* Randomized Initial Positions: The digits were randomized at the beginning of each sequence. This randomization ensured a wide variety of motion patterns across the dataset, preventing the models from overfitting to specific trajectories.  
The preprocessing was conducted as follows:  
* Step 1. Conversion to Tensor: The numpy array was converted to a PyTorch tensor.
* Step 2. An additional dimension was added to represent the channel (as the images were grayscale, this resulted in a single channel).
* Step 3. Pixel values were normalized to the range of [0, 1] dividing them by 255.
## 2 Related Work  
### PPG Signals for Non-Intvasive Blood Glucose Estimation  
This section summarizes recent studies on the use of PPG signals for non-invasive BGL estimation. By analyzing the characteristics of the PPG waveform, researchers have explored its potential as a tool for glucose monitoring through ML techniques.  
[PERSON] et al. developed a system employing PPG data obtained from 26 people and processed it using an XGBoost (XGB) model, resulting in a clinically accurate glucose estimate [4]. However, the reliance on body mass index (BMI) and technology complexity presented issues. Similarly, [PERSON] et al. used a back-propagation neural network (BPNN) with dual-wavelength PPG and bioelectrical impedance characteristics to obtain excellent accuracy, although they encountered limits in detection range and sensor integration complexity [5].  
[PERSON] et al. introduced a smartphone-based solution, converting fingertip videos into PPG signals for BGL prediction using regression models. While effective, this method depended on high-quality smartphone sensors, limiting accessibility [6]. [PERSON] and [PERSON] utilized a CNN-LSTM hybrid model for temporal PPG analysis, achieving 99% accuracy in clinically acceptable zones but were constrained by small datasets and manual hyperparameter tuning [8].  
[PERSON] et al. introduced a novel multimodal framework for estimating BGL using PPG and electrocardiogram (ECG) data. Using deep neural networks (DNNs) and spatiotemporal decision fusion techniques, their method achieved Zone A + B accuracy of 99.49% but faced scalability challenges due to the small number of participants and the complexity of combining ECG and PPG signals [7]. [PERSON] et al. combined PPG signals, blood pressure data, and demographic information into a Bagged Ensemble Trees (BET) model in another research. They achieved 61.88% predictions in Zone A of the Clarke Error Grid (CEG), but reliance on manually inputted blood pressure data limited practicality [10].  
More recently, [PERSON] et al. created MMG-Net, a multimodal network that uses PPG, electrodermal activity (EDA), and skin temperature data to improve BGL prediction. Their method achieved great accuracy but raised concerns regarding real-world viability due to the reliance on several sensors and the limited sample size [12].  
These studies demonstrate the potential of PPG signals for BGL monitoring while also emphasizing constraints such as dataset size, sensor requirements, and the need for comprehensive feature selection to increase prediction accuracy and efficiency.
# An Efficient 5G Data Plan Approach Based on Partially Distributed Mobility Architecture
## 3 The Proposed 5G Procedure  
### Initial Attachment  
When U.E. is turned on, in legacy LTE architecture, the initial attachment occurs to register the user to the network and establish a PDN connection as described in [46]. Then the UE establishes a connection with the eNB as a synchronization procedure and data delivery. The eNB passes these connection messages to the MME to update the location of the UE to HSS. Then the transmission path will be created after the MME inform the SGW to generate that path. Now, the S-GW plays the mobility anchor's role by modifying the bearer with P-GW and letting the MME respond to the UE and accept its attached request. Finally, the UE sends an attached message to the MME to pass to S-GW to modify the bearer. However, the attachment procedure in our proposed architecture is simplified due to some adjustments in the network architecture. First, session activation in the gateway is eliminated, the U.E. registers its information and gets the IP address from the C.E. in the network. Now U.E. only needs to reach C.E. to obtain all necessary information, and therefore, the total time for attachment is reduced. Second, one M-GW can reduce the number of steps to establish a tunnel from U.E. to G.W. if U.E. has packets to be transmitted or received. The attached procedure is detailed in Figure 3 and described as follows:  
The U.E. sends an Attach Request message including International Mobile Subscriber Identity (IMSI) to eNB. Then eNB forwards Attach Request message to C.E. through the Initial U.E. context message after adding other parameters like Tracking Area Identifier (TAI) or E-UTRAN Cell Global Identifier (ECGI). Authentication and authorization, and location update procedures are assumed to be unchanged. As soon as these steps are successful, C.E. now allocates U.E. IP address from its IP pool, then C.E. selects the gateway closer to U.E. An Attach accept message containing M-GW information for establishing an uplink GTP tunnel (G.W. IP, UL TEID, QoS), and U.E. IP address is sent to eNB. eNB configures radio bearer to U.E. and status of U.E. change from De-registered to Registered. At the same time, C.E. also sends a Session control message with eNB information (IP eNB, DL TEID, QoS) to create a downlink GTP connection. After this step, if U.E. needs an Internet connection immediately, a trigger message will be sent, and uplink/downlink GTP tunnels between the base station and gateway will be established. This procedure is defined in the data delivery concept below.  
Figure 3: The initial attach procedure in the proposed 5G network.
# Linked Data Triples Enhance Document Relevance Classification
## 5 Learning Model Methodology  
To discover the influence of semantic and structural text features on news-feed classification effectiveness, we develop three baseline deep learning models: (i) with pre-trained word embeddings as inputs only, (ii) with sentence embeddings as inputs only, and (iii) with bi-directional encoder representation of transformers. Then, probabilistic LDA-based topic distributions, named entities (NEs), and linked data (triples) are added as extra input features to each of the baseline models. Several combinations of features sets are formed to test the performance of the resulting classifiers. Before building these models, common pre-processing steps, like tokenization, special character filtering, and lowercasing, are applied as necessary on the raw text of the news items. The tokens are represented as strings which are then converted into random unique integers to encode them as vectors. The details of each model is given in the following sections.  
GloVe [9] and InferSent [11] were selected for bag-of-word and sentence representations, respectively. As per Reference [11], these representations show superior performance  
Figure 2: An example of _Topic-Entity Triple_ extraction from a sentence in Energy-Hub corpus. A pre-trained spaCy model is applied to get syntactic dependencies of each term and subsequently form Triples. Each triple is passed through a filter to compare with extracted named entities, and topic keywords in order to consider them as Topic-Entity Triples.  
in several NLP tasks compared to other encoders in their class. The BERT model is selected from transformers. BERT and InferSent are considered as state-of-the-art models in text classification problem. Our proposed model architecture enhances the document representations using latent topics, named entities (NEs), and linked data triples (formed from topics and NEs).
## 4 Coarse-to-Fine Free-Form Surface Machining Error Decomposition and Compensation Experiments
### Compensation for the Fine System Error  
On the surface of the free-form surface part, the machining errors corresponding to each measurement point were not the same. Therefore, to improve the error compensation effect of the part, the fine system error \(X_{4}\) was compensated along the opposite direction of the outer normal vector of the measuring point, and the compensated coordinate data of the measuring point were obtained. The new machining surface was reconstructed according to the coordinates of the compensated measuring points, the corresponding NC code was generated for compensated machining, the tool path was checked without error and then imported into the CNC system of the machine tool and, finally, compensated machining was carried out. After the compensation machining was completed, the machining error of the free-form surface part was measured again using on-machine measurement techniques to obtain the machining error of the part after error compensation, and its value and distribution are shown in Figure 15.  
The experimental data show that the maximum absolute machining error before compensation was 0.0580 mm, the maximum absolute machining error after compensation was 0.0159 mm, which was a 72.5% reduction, the mean absolute machining error before compensation was 0.0472 mm, and the mean absolute machining error after compensa  
Figure 14: Fine system error \(X_{4}\).  
Figure 15: Machining error of the free-form surface part after error compensation.  
tion was 0.0059 mm, which was an 87.5% reduction. By comparing the machining error measurement data before and after the compensation of the free-form surface part, it was found that, by utilizing the machining error decomposition method proposed in this paper to decompose the machining error first and then compensating for the decomposed systematic error, this machining error compensation method was able to improve its machining accuracy to a greater extent.
## 3 Results  
The developed terminology server relies on open-source standards and technologies and not on a specific vendor. The SPHN is in full control of the content and the versioning, and the service is as automated as possible to ease its use and maintenance. Data providers, such as hospital IT personnel, can download terminology bundles from the central terminology server on BioMedIT and load them into their local triplestore. In case they do not have a triplestore, the Apache Jena Fuseki container can be used. For researchers using the BioMedIT environment, the locally installed terminology service comes with the basic setup of a project space. The solution is minimizing the resources needed for maintenance as each repeating step is automated as much as possible. New versions of the existing terminologies only need to be downloaded by the DCC staff from the original source and be provided as an input for the CI/CD pipeline.  
Currently, the SPHN DCC Terminology Service includes SPHN-compatible, machine-readable versions of the following terminologies:  
* CHOP (including historical versions since 2016)
* ICD-10-GM (including historical versions since 2014)
* SNOMED CT (including historical versions since 31 January 2021)
* LOINC (including historical versions since v2.69)
* ATC (2021 version)
* UCUM  
For individual researchers, DCC provides an additional webservice in the BioMedIT Portal ([[https://portal.dcc.sib.swiss/](https://portal.dcc.sib.swiss/)]([https://portal.dcc.sib.swiss/](https://portal.dcc.sib.swiss/)), accessed on 30 October 2021), where these terminology files are available for Swiss researchers (via SWITCH edu-ID) to download.
## 2 Materials and Methods
### Substrates  
To evaluate the proposed methodology that investigates the impact of compaction on the coupled water and heat flux, empirical information from five substrates was used. The substrates used in this study are those employed by [PERSON] et al. [2]. Figure 1 presents photographs of each substrate and Table 1 provides a detailed characterization of their physical properties. These five substrates were chosen because they are commonly used in green roofs (S1-S3), and because they are easily found in the local market (S4,S5). A detailed characterization of the physical, hydrodynamic, and thermal properties of these substrates, for their uncompacted state, is presented elsewhere [2]. For the compacted state, the substrate's hydrodynamic and thermal properties were estimated using the theory presented in Sections 2.2 and 2.3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Substrate**} & \multicolumn{4}{c}{**Texture**} & \multirow{2}{*}{**OM (\%)**} & \multirow{2}{*}{**OM (\%)**} & \multirow{2}{*}{**\(\rho_{s}\) (g/cm\({}^{3}\))**} & \multirow{2}{*}{**\(\rho_{max}\) (g/cm\({}^{3}\))**} & \multirow{2}{*}{**\(\mathbf{c_{u}}\) (\(\mathbf{\mathrm{\text{-}}}\))**} & \multirow{2}{*}{**\(\mathbf{c_{c}}\) (\(\mathbf{\mathrm{\text{-}}}\))**} \\ \cline{2-2} \cline{5-10}  & **Sand (\%)** & & & & & & & \\
**0.05–2 mm** & **0.002–0.05 mm** & **<0.002 mm** & & & & & \\ \hline S1 & 88.1 & 8.5 & 3.3 & 12.0 & 2.20 & 0.99 & 48.4 & 0.7 \\ S2 & 65.6 & 29.2 & 5.2 & 30.7 & 2.13 & 0.44 & 67.5 & 0.5 \\ S3 & 82.8 & 13.3 & 3.9 & 4.1 & 2.51 & 1.20 & 43.0 & 5.9 \\ S4 & 50.7 & 34.4 & 14.9 & 10.2 & 2.44 & 1.13 & 240.0 & 0.6 \\ S5 & 53.4 & 37.6 & 9.0 & 35.5 & 2.32 & 0.98 & 89.1 & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the substrates: texture determined using the United States Department of Agriculture (USDA) soil classification system, organic matter (OM), soil particle density (\(\rho_{s}\)), maximum density (\(\rho_{max}\)), uniformity (\(C_{u}\)) and curvature (\(C_{c}\)) coefficients for the substrate’s uncompacted state. Modified from Reference [2].
## 3 Discussion
### Comparative Roles of GPVI and Syk in Platelet Activation  
A remarkable finding was that Syk inhibition also affected parameters of thrombus formation on surfaces that were considered to act independently of GPVI (i.e., GFOGER-GPP, M4) or with a low GPVI dependency (collagen-I, M7; collagen-III, M9). As another approach to examine this, a PLS model was constructed and used for principal component analysis. The PCA plots indicated a narrow cluster for all high GPVI-activating surfaces (M1-3,6), with the data of M4,5 partly centering out. A prediction of the role of GPVI for other surfaces gave a mixed outcome for the fibrillar collagens (M7,9), whereas this was negative for the degraded collagen-I (M8). Importantly, the prediction model revealed a consistent GPVI independence for the Syk-inhibited samples, regardless of the type of microspot composition. Accordingly, this analysis supported the indication of low-level GPVI and Syk activity at these weakly thrombogenic surfaces.  
In recent years, evidence has accumulated on the role of GPVI signaling in platelets in (also) contacting non collagen surfaces. For instance, GPVI dependency has been discovered for platelets interacting with laminin [37], fibrin [29; 38], or fibrinogen [39; 40]. In this context, it is also likely that for (GPP)\({}_{\text{n}}\)-containing surfaces, Syk-dependent platelet responses can be traced back to residual GPVI activity. On the other hand, based on early studies, it cannot yet be excluded that (part of) the Syk-dependent platelet responses in thrombus formation at \"weaker\" surfaces are mediated by signaling via integrin \(\alpha_{\text{IIb}}\beta_{3}\)[41; 42; 43], hence bypassing GPVI. This needs to be studied using specific GPVI-inhibitory tools.