esa-sceva/satcom-chunk-collection huggingface dataset:
# RESEMBLE: A Real-Time Stack for Synchronized Mesh Mobile Bluetooth Low Energy Networks
## 6 Experimental Assessment
### Discussion  
As shown in this section, RESEMBLE enables several features, i.e., both real-time and non-real-time communications in the same network, node mobility and clock synchronization.  
RESEMBLE does not provide retransmission mechanisms at the Bearer Layer and delegates this functionality to the upper layers, i.e., the ones on top of the Network Layer. In fact, such a functionality is strictly dependent on the Application Layer adopted for each specific industrial application. For instance, CANOpen, an Application Layer adopted by many industrial protocols (EtherCAT, CAN bus, etc.), implements its own retransmission mechanisms, while protocols based on the publisher/subscribers model allow the so-called \"fire and forget\" transmissions, which do not require retransmissions. However, in RESEMBLE, retransmissions are possible.  
In RESEMBLE flooding allows for the same message to be transmitted over multiple paths, but, differently from Bluetooth Mesh [22], real-time messages are transmitted in their dedicated timeslots, thus avoiding collisions.  
As far as the energy consumption is concerned, compared to the standard BLE Mesh [22] protocol, the adoption of RESEMBLE does not make a significant difference. In  
Figure 14: End-to-end delay distributions for the flows generated by the Nodes 0 and 3.  
Figure 13: End-to-end delay distributions for the flows generated by the Nodes 1 and 2.  
fact, the network nodes, when they are not in transmitting mode, stay in receiving mode, with a duty cycle close to 100%, as in [22], in order to listen for both real-time and non-real-time incoming messages. Moreover, as, thanks to the TDMA scheme in RESEMBLE, the transmission instants of each node are known a priori, any node with low energy consumption requirements can go to sleep when it does not have to transmit or receive real-time messages and wake up when needed. This paves the way for the applyication of energy-saving policies, which will be investigated in future work.  
TDMA-based transmission schemes suffer from limited scalability. In RESEMBLE, two main factors affect scalability, i.e., the length of the superframe and the overall network workload. The first factor that affects scalability is the superframe length, which depends on the number of network nodes. In RESEMBLE, each node requires at least one timeslot to transmit real-time messages. This entails a longer superframe and, consequently, higher delays. The second factor is the workload generated by the application and, in particular, the number of flows per node and the flow periods. In general, the higher the workload, the longer the queuing time, i.e., the more time a message spends in the transmission queue. A possible solution to this problem is clustering, as it allows for shorter superframes and limits the message flooding within the cluster, as in [43].
# Durability of High Volume Glass Powder Self-Compacting Concrete
## 3 Results
### Porosity  
The void content of concrete measured using water absorption test is one of the most common indicators used for assessing the durability of concrete. Figures 7 and 8 show the measured porosity for concrete mixes at different ages. The porosity reduced with continued hydration for in all the concrete mixes. The 10G30% SCC mix demonstrated a lower porosity as compared to other glass mixes at similar replacement level as well as the control mixes. Along with the pozzolanic reaction that assisted in the refinement of pore structure, the finer particle size of glass powder could help to improve particle packing in the system, which resulted in a denser and less porous concrete [19]. The findings signify the importance of particle size distribution as the presence of finer particles in the spaces between larger particles also acts to'refine' the porosity. The porosity of 40G30% concrete mix was similar to GP despite having lower compressive strength. This implies that the pozzolanic reaction lead to precipitation of hydrates in the capillary pores which restricted the free movement of water. The volume of permeable voids increased with an increase in the glass content. The similar porosity values of 20G40% and 40G30% across ages show that the properties of concrete mixes with at higher replacement level of cement with glass powder could be maintained by varying the fineness of the glass powder. [PERSON] and [PERSON] (2006) [15] reported similar results of an increase in volume of permeable voids with an increase in the glass powder content. Conversely, [PERSON] and [PERSON] (2017) [36] showed a minimal change in porosity for up to 30% cement replacement level with glass powder, however beyond this replacement level an increase in porosity was reported as observed in 20G40% mix. A nonlinear inverse relationship was observed between coefficient of permeability and porosity as shown in Figure 9.  
As the porosity of the concrete increased the permeability of the concrete also increased. As with the use of compressive strength as an indicator for durability, comparisons of overall porosity can sometimes be misleading. A single total porosity number does not provide the pore size distribution or tortuosity of the pore structure. As seen in Figure 9 a porosity of 9% would results in a coefficient of permeability of approximately \(2\times 10^{-11}\) for the GP binder system, while the glass mixes were closer to \(1\times 10^{-11}\).  
Figure 7: Porosity of SCC mixes at 30% cement replacement at different ages.
## 2 Software-Defined GNSS Receiver
### Navigation Solution  
At the navigation message decoding phase, the first step is to detect the sub-frame preambles on the demodulated data. The BeiDou navigation message has both error correction coding and data interleaving. The error correction is performed by the Bose, Chaudhuri, and Hocquenghem (BCH 15,11,1) codes, which are capable of correcting one-bit error within every block of 15 bits. The decoding process can be illustrated by the flowchart shown in Figure 7. It is important to mention here that the BCH decoding and deinterleaving mechanism is same for both (D1 and D2) navigation messages, and the first word of every sub-frame, the first 15 bits are not BCH encoded. Only the next 11 bits are encoded and hence, this word consists of 26 information bits and four parity bits. Also, data interleaving is not performed in this word. Therefore, the first word of every sub-frame has to be processed differently at the decoding  
Figure 6: C/N\({}_{0}\) for the tracked BeiDou satellites.  
stage within the receiver. After successfully decoding the navigation message, a receiver position can be calculated via a least-square method with at least four visible satellites with decoded ephemerides.  
Figure 7: Flowchart illustrating the decoding procedure of BeiDou navigation message.
# Toward Scalable Video Analytics Using Compressed-Domain Features at the Edge
## 2 Background  
There are many different approaches to video moving object detection, both utilizing compressed and uncompressed data. In this section, we briefly introduce the state-of-the-art approaches for moving object detection in both the compressed-domain and pixel-domain.
## 7 Asset Recognition from Images and Populating the Database
### Virtual Tour Approach
#### 7.3.3 Virtual Tour by Web-Based Platforms  
To this purpose, in the Milan project, it was planned that the data would be shared over the network so that operators could perform recognition activities by navigating within the spherical views. Numerous web-based platforms are available to visualize  
Figure 27: Example of virtual tour in the Reconstructor® platform of a site of the Milan project.  
three-dimensional data collected with LiDAR instrumentation, and among them, platforms that provide virtual tour-style navigation, which would allow easy and intuitive navigation between different spherical views, have been analyzed. At the time of starting the project activities, the only platform that simultaneously offered web-based virtual tour-style navigation and at the same time allowed an agile structuring of the views in a tree structure, along with an easy annotation activity, was the Cintoo(r) platform. The use of such a platform also allows tags to be attached to the recognized elements (Figures 28 and 29).  
Figure 28: Example of how the data of the assets are managed in Cintoo®. In particular, the image shows how for every tagged object (in this case, an extinguisher), a data sheet with all the object characteristics is connected. (Example from tagging tests at the University of Brescia).  
Figure 29: Example of how the data of the assets are managed in Cinto®. The image shows the tagging of an electric plug. On the bottom-right-hand side, a plan view of the room is displayed, and the center of the camera position is represented with a white dot (example from tagging tests at the University of Brescia).  
In this way, when an element with its characteristics is recognized, it is highlighted in the model. Therefore, all operators working on the project will be made aware of which items and objects have already been recognized and recorded in the DB. At the time of writing this paper, other platforms with similar features have been introduced in the market, such as Atis Cloud(r) [97] and Benaco(r) (Figure 30) [98,99].
# A Titanium Alloy Defect Detection Method Based on Optical-Acoustic Image Fusion
## 4 Practical Experiment
### Results and Analysis
Moreover, the optical-acoustic detection image information fusion technology took the optical detection results and acoustic detection results as the input and outputted interactive fusion results and a decision-level test report. The interactive fusion results are shown in Figure 19. The fusion results had an interactive feature, and the operator can easily obtain the type, position, size, buried depth, and other information of each defect by clicking the mouse. In addition, the fusion results were comprehensive and intuitive, which played an important role in the diagnosis of material structure health. The decision-level test report is shown in Figure 20. One the one hand, the test report included the specific detection information of each defect. On the other hand, the test report showed the risk rating of the detection object and the guiding suggestions about subsequent production processes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Type** & **Position(mm)** & **Diameter(mm)** & **Depth(mm)** \\ \hline \multirow{2}{*}{1} & circle & X = 50, Y = 100 & 8 & 5 \\  & **circle** & X = **50, Y = 98** & **8** & **5** \\ \hline \multirow{2}{*}{2} & circle & X = 100, Y = 100 & 6 & 5 \\  & **circle** & X = **99, Y = 98** & **7** & **5** \\ \hline \multirow{2}{*}{3} & circle & X = 150, Y = 60 & 4 & 8 \\  & **circle** & X = **148, Y = 58** & **4** & **8** \\ \hline \multirow{2}{*}{4} & circle & X = 150, Y = 100 & 4 & 5 \\  & **circle** & X = **148, Y = 98** & **4** & **5** \\ \hline \multirow{2}{*}{5} & circle & X = 150, Y = 20 & 4 & 11 \\  & **circle** & X = **149, Y = 18** & **4** & **11** \\ \hline \multirow{2}{*}{absolute error} & & \(|\delta|\leq 2\) mm & \(|\delta|\leq 1\) mm & \(|\delta|\leq 1\) mm \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between the final results of internal defect detection and the real data. Figure 19: The interactive fusion results.
## 3 Results and Discussion  
### Genome Size Estimation of A. remotilora  
_A. remotilora_ was sequenced using an Illumina HiSeq X platform (Illumina, San Diego, CA, USA), resulting in 1680 million reads and 253.7 Gb raw sequence length. After filtering and correction, trimmed 188.2 Gb sequences were derived with a 139 bp average read length and 41.5% GC content. For whole-genome size estimation, two genome estimation tools (GenomeScope and Jellyfish) were used based on the trimmed sequences. Because the estimated efficiency of the genome size is affected by changing _k_-mer values, we evaluated the optimal genome size via varying the _k_-mer frequency values in both methods. Using the GenomeScope method, the genome size was predicted to be between 3.7 Gb and 3.9 Gb. The optimal _k_-mer value for the highest predicted model fit was 23; then, haploid genome size was estimated to be 3.9 Gb (Figure 1a). Using the Jellyfish method, the genome size was predicted to be between 3.7 Gb and 3.8 Gb. The genome size using the optimal _k_-mer value (_k_ = 23) was 3.8 Gb (Figure 1b). The haploid genome size was estimated to be 3.8-3.9 Gb with a 2.42% heterozygosity ratio, and only 541.9 Mb unique genome sequences (13.9%) were detected. It is assumed that _A. remotilora_ (2n = 2x = 36) has many heterogeneous parts between the haplotypes in the parent genome.
## 3 Results and Discussion
In order to assess the biocompatibility profiles of cryogels containing different amounts of CS, an MTT assay was conducted using the primary culture of rat ADMSC. Results of the assay showed that the extracts from GelChCS250 exerted high toxic effect on rat ADMSC (Figure 6). The cell viability of rat ADMSC after treatment with GelChCS250 extract (ranging from 1.56% to 100%) was from 58.3 \(\pm\) 5.1% to 46.9 \(\pm\) 4.2%, respectively. The extracts from GelChCS10 and GelChCS25 exhibited a weak toxic effect on rat ADMSC and displayed more than 55% of cell viability after treatment with 100% extract. The extracts from GelChCS50 and GelChCS100 did not exert toxic effects on the cells and displayed the highest cell viability in comparison to the other GelChCS extracts. The cell viability of rat ADMSC after treatment by 100% extracts of GelChCS50 and GelChCS100 was 69.9 \(\pm\) 2.9% and 76.7 \(\pm\) 1.1%, respectively. Thus, the MTT assay revealed that the cryogels GelChCS50 and GelChCS100 are biocompatible and suitable for further applications in in vivo studies.
# A System for Controlling and Monitoring IoT Applications
## 5 Communication between Blocks
### Communication between RPi, Server and DB  
Data must be stored in the DB so communications between the RPi, the main server and the DB are mandatory. The RPi-Server communication is carried out through the Internet (Wi-Fi) using the TP-LINK TL-WN722N wireless USB adapter as was explained in Section 3. Users connect to the domain of the main server with the web application. This web application must remotely interact with the RPis. To avoid contracting a static IP or a domain (only one domain is required for server and web application), a program in Python language was made. It identifies the public assigned IP address to the RPi and stores it in the DB each time that it changes.
## 3 Results  
### Measurements of Albedo  
Figure 6 exemplarily shows the course of solar radiation, the solar zenith angle, and the albedo of paddy rice for the rainy season (19 November 2012) and for the dry season (15 April 2013). Fluctuations in albedo were encountered when clouds passed through the experimental area. A higher albedo of paddy rice was observed in the rainy season than in the dry season, with values of 0.29 and 0.25, respectively, at noon. Nevertheless, the obtained values were higher than the mean value calculated from literature data (Table 1).  
Figure 6: Total solar radiation G\({}_{\text{up}}\), zenith angle \(\theta\), and albedo of paddy rice \(\alpha\) during the rainy season (19 November 2012) and the dry season (15 April 2013) at IRRI in Los Bános, Republic of the Philippines (14\({}^{\circ}\)9\({}^{\prime}\)5\({}^{\prime\prime}\) N, 121\({}^{\circ}\)15\({}^{\prime}\)37\({}^{\prime\prime}\) E).  
Based on Equation (17), the albedo can be modeled for the course of the day as a function of the solar zenith angle:  
\[\alpha=0.2704+1.816\cdot 10^{-5}.e^{0.1761\cdot\theta} \tag{21}\]  
With an \(R^{2}\) of 0.7880 and _RSME_ of 0.0299.
# A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins
## 3 Context, Research Questions, and Search Strategy  
### Context  
As with DT definitions, there is no uniform definition of a HDT. We use the following definition. A Human Digital Twin is any application that uses personal and environmental data to model the behavior of individuals, enabling personalized coaching or interventions.  
Although there are many versions of the HDT architecture [5; 14; 22; 23], in general, the following components or layers can be identified [24]. A data acquisition layer, where IoT sensor data, wearable device readouts, stored data, or manual input are collected. A storage layer that usually includes data denoising and preprocessing modules. An analysis and computation layer. In this layer, ML is used to evaluate input and create predictive  
Figure 1: Algorithm F1-score and accuracy comparison using the average of the results obtained by using personalized models and general models, as presented in [21].  
models. And finally, there is a user-interaction layer. In this layer, the state simulation is implemented, and communication with the physical twin or other stakeholders is realized.  
As an example, the proposed architecture of the HDT version of our VFC platform [17] is shown in Figure 2. This platform communicates with the application that participants install on their mobile phone and can be monitored using an administration dashboard. Neither of these components is presented in this figure. The data acquisition layer has three components. During the registration process, participants submit a selection of personal data using the mobile application. During the participation period, data from a wearable device is collected using the API provided by the wearable device manufacturer. In addition, weather and location data will be collected using the mobile phone and available weather services.  
In the preparator, the wearable data is transformed into a format that can be used by the goal modeler. In this layer, it is also possible to apply feature selection algorithms to optimize the input of the motivator. In the analysis and computation layer, three processes take place. (i) Using supervised learning and data collected during the intake period, classification models are generated to predict the likelihood of participants achieving their daily activity goal. (ii) Daily activities are monitored and fed into the models to obtain predictions. (iii) The predictions are evaluated to detect model drift and participant adaptation, in which case models will be regenerated or reassigned.  
If necessary, the recommendation system can trigger an intervention that is generated in the user-interaction layer. Here, the motivator selects an intervention based on the personal data collected on intake and current state. This will be communicated through the messenger service to the mobile app. After that, the participant can provide feedback that will be used as input for the reinforcement learning component of the motivator.
# Calculation Method of New Assembled Corrugated Steel Initial Support Structure of Highway Tunnel
## 4 Practical Engineering Application
### Project Overview  
The Qipanshan Tunnel is situated along the Chengchuan-Jiangchuan Highway in Yunnan Province. It comprises two separate lanes. The left lane spans from stakes ZK47 + 212 to ZK48 + 460, totaling 1248 m in length with a maximum burial depth of 131 m. Similarly, the right lane extends from stakes YK47 + 205 to YK48 + 460, covering a distance of 1255 m and reaching a maximum depth of 119 m, as illustrated in Figure 13. The tunnel site is positioned on the western flank of the Heiyashan syncline, characterized by rock strata dipping at 130\({}^{\circ}\) with a 25\({}^{\circ}\) angle. The rock mass exhibits a slightly tensile, dispersed configuration, featuring rough fractured surfaces without filling. Surface erosional features, such as water-eroded ditches, grooves, and buds, are prominently developed.  
The CSP selected for the initial support structure of the Qipanshan Tunnel measures 380 mm \(\times\) 140 mm \(\times\) 5 mm and is assembled into an arch support structure using flanges and bolts. An 8 cm deformation allowance is provided behind the CSP initial support, which is grouted after full assembly. Anchor bolts, 3 m in length, are positioned at both the arch foot and waist. The tunnel spans 12.1 m and is constructed using the top heading and bench excavation method, with the detailed lining design depicted in Figure 13a.  
Monitoring is conducted in the test section of the corrugated steel lining at the right inlet, specifically at section YK47 + 355, with the tunnel buried at a depth of 81 m and surrounded by grade IV rock.
# Extraction of Visible Boundaries for Cadastral Mapping Based on UAV Imagery
## 3 Results
Figure 6: Comparison in the number of extracted and filtered objects using different scale and merge parameter values, to the number of objects identified in the reference data set. Figure 7: _Cont._  
A simplification algorithm was applied to both extracted objects and filtered objects. The results showed that if extracted objects are smoothed and smoothed objects are later simplified, the localization of simplified objects is almost equal to that of the extracted ones (Figure 8). The initial tests show that possible shifts in location are possible when a direct implementation of the simplification algorithm to extracted visible objects is used. Figure 8: (**a**) Extracted objects smoothed by making use of the Snakes algorithm. (**b**) Simplification of extracted objects by making use of the Snakes smoothing algorithm and Douglas-Peucker simplification algorithm. (**c**) Extracted objects simplified with Douglas-Peucker algorithm (in black) and compared to object simplifications on (**b**). Figure 7: (**a**,**c**,**e**) Extracted objects at scale level 80 and merge level 95 for (**a**) GSD 25 cm, (**c**) GSD 50 cm, and (**e**) GSD 100 cm. (**b**,**d**,**f**) Filtered objects of scale level 80 and merge level 95 based on minimum object area from the reference data. The buffer overlay method was used for the accuracy assessment. The accuracy assessment method was applied to the extracted objects, simplified extracted objects, filtered objects and simplified filtered objects. The results show that there is no significant difference in accuracy assessment results when comparing extracted and simplified objects (Table 3, Table 4, and Table 5). At a buffer width of 2 m, a GSD of 50 cm and a GSD of 100 cm provide a higher percentage of correctly extracted objects compared to a GSD of 25 cm. The percentage of correctly extracted objects was 66% for both a GSD of 50 cm and a GSD of 100 cm (Tables 4 and 5). However, the filtering approach contributed to the increased correctness (decreased completeness) and overall quality, for all GSDs. From the filtered objects, the best results for correctness were recorded at a GSD of 50 cm (Figure 9). The percentage indicated the correctness was 77%, while for the completeness it was 67%.