esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Mechanical Properties of Anisotropic Slate under Different Water Contents
## 4 Analysis of Anisotropic Properties of Slate Parameters
### Elastic Modulus Anisotropy
\begin{array}{cccccc}\frac{1}{E_{1}} &-\frac{\upsilon_{1}}{E_{1}}&-\frac{\upsilon_{2}}{E_{2}}&0&0&0\\ -\frac{\upsilon_{1}}{E_{1}}&\frac{1}{E_{1}}&-\frac{\upsilon_{2}}{E_{2}}&0&0&0 \\ -\frac{\upsilon_{2}}{E_{1}}&-\frac{\upsilon_{2}}{E_{2}}&\frac{1}{E_{2}}&0&0&0 \\ 0&0&0&\frac{2(1+\upsilon_{1})}{E_{1}}&0&0\\ 0&0&0&0&\frac{1}{G_{12}}&0\\ 0&0&0&0&\frac{1}{G_{12}}\end{array} \right\}\times\left\{ \begin{array}{c} \sigma_{x}\\ \sigma_{y}\\ \sigma_{z}\\ \tau_{xy}\\ \tau_{yz}\\ \tau_{zx}\end{array} \right\} \tag{7}\]  
Equation (8) can be obtained by performing the coordinate transformation, where \(E_{\beta}\) is the elastic modulus under the bedding angle \(\beta\):  
\[\frac{1}{E_{\beta}}=\frac{\sin^{4}\beta}{E_{1}}+\frac{\cos^{4}\beta}{E_{2}}+ \sin^{2}\beta\cdot\cos^{2}\beta\left(\frac{1}{G_{12}}-\frac{2\upsilon_{2}}{E_ {2}}\right) \tag{8}\]  
Figure 12: Elastic modulus of slate with different bedding angles: (**a**) dry state, (**b**) natural state, and (**c**) saturated state. Simplification of Equation (8) yields Equation (9). \(S_{1}\) and \(S_{2}\) are both constants. \[\frac{1}{E_{\beta}}=S_{1}\sin^{4}\beta+S_{2}\sin^{2}\beta+S_{3} \tag{9}\]  
Based on these equations and the integration of test results, a relationship curve for the elastic modulus versus the bedding angle is predicted, as depicted in Figure 14. These results suggest that the curve more accurately describes the saturated slate. Observations of \(R^{2}\) under varying confining pressures reveal that, although \(R^{2}\) exhibits fluctuations, its overall trajectory is upward. This study defines the degree of anisotropy in the elastic modulus as \(R_{E}=E_{\text{max}}/E_{\text{min}}\), where \(E_{\text{max}}\) and \(E_{\text{min}}\) are the maximum and minimum elastic modulus values of the sample under identical conditions. The degrees of anisotropy in the elastic modulus for dry, natural, and saturated states are denoted as \(R_{Eg}\), \(R_{Ez}\), and \(R_{Eb}\), respectively. Calculations indicate that \(R_{Eg}>R_{Ez}>R_{Eb}\), with \(R_{E}\) diminishing as confining pressure increases. This  
Figure 14: Relationship curve between elastic modulus and bedding angle: (**a**) dry state, (**b**) natural state, and (**c**) saturated state. Figure 13: Transversely isotropic body in the coordinate system. suggests that a lower anisotropy degree in slate correlates with greater consistency between measured and predicted elastic modulus values.
## 3 Measurement Technique and Data Reduction Methodologies
### Pitot-Rake Measurement  
Pitot-rake measurement was done in order to validate the boundary layer profile established by hot-wire anemometry since the pitot-rake measurement for this kind of simple incompressible flowfield is adequately robust to measure the boundary layer profile via high-accuracy pressure transducers. Figure 6 is a schematic illustration showing the pitot-rake assembly attached to the flat-plate test model surface. There are 30 probes with an outer diameter of 0.5 mm, an inner diameter of 0.3 mm, and a length of 30 mm. A static pressure probe is also attached outside most of the probes. The boundary layer characteristics, including its profile, momentum thickness, displacement thickness, and shape factor, were evaluated by the aforementioned equations. Note that those values were also used to assure that the measured boundary layer profile by the hot-wire anemometry was appropriate by comparing it with those by pitot-rake measurements.
# Keyframe Insertion: Enabling Low-Latency Random Access and Packet Loss Repair
## 2 State-Of-The-Art
### Client-Based Methods  
Purely on the client device of the end user, so without modifications to the content or the network, techniques for fast channel switching can be categorized as prefetchingtechniques and playback modification techniques. With respect to packet loss mitigation, error concealment can be considered an alternative to the proposed technique.  
A lot of prefetching techniques have been studied, which try to predict the channels where the user is going to change towards for Internet Protocol (IP)-based techniques [6] and for Digital Video Broadcasting (DVB) [7]. Some techniques prefetch a low-resolution version to support a preview mode [8]. Prefetching can also be performed by sequentially sampling video segments from a set of target channels that a client might switch to in the near future [9]. These video segments downloaded from the combination channel will be cached in a local buffer. When low-latency requirements are considered, prefetching would require a lot of simultaneous open streams resulting in a large bitrate and processing burden on the client device.  
Alternatively, research shows that there is a possibility to vary the playback speed to enable smaller buffers and thus reduce latency [10]. This concept can be coupled to an early start of audio combined with slow playback of video [11]. Although useful, such techniques would only allow small changes to the buffering behavior of the end-to-end system and should be considered complementary to the proposed technique.  
Whenever network-layer packets get lost, corrupted Network Abstraction Unit (NAL) packets should be discarded entirely. Numerous error concealment algorithms have been and are being developed for H.264/AVC [12; 13]. For more recent standards like H.265/HEVC and Versatile Video Coding (H.266/VVC), there is little work on error concealment techniques [14]. The main problem with performing error concealment on these standards is the Temporal Motion Vector Prediction Tool (TMVP). Performing error concealment on pixels provides decent looking results, but errors in concealed motion vectors leads to large and annoying artifacts.
## 3 Results and Discussion
### Wheat Yield
In terms of grain yield, CT outperformed NT only for Faraj and Nachit varieties under 20 kg N ha\({}^{-1}\) and I.C and Louisa varieties under 40 kg N ha\({}^{-1}\), whereas in terms of straw yield, CT achieved better results than NT only for the Faraj variety under the three nitrogen doses and Louisa variety under 40 kg N ha\({}^{-1}\). Ignoring varieties and comparing the two tillage types for the three nitrogen doses, grain and straw yields were always higher for NT compared to CT, independent of the nitrogen dose (Tables 9 and 10). Furthermore, combining nitrogen dose and comparing tillage types for the different varieties, the ranking of the varieties was the same for both tillage types in terms of grain yield, with Nachit identified as the best variety (4.30 t ha\({}^{-1}\) under CT and 4.62 t ha\({}^{-1}\) under NT) and Louiza identified as the variety providing the lowest yield (3.19 t ha\({}^{-1}\) under CT and 3.17 t ha\({}^{-1}\) under NT). The behavior of varieties was different considerably in terms of straw yield. Although the Louiza variety had the lowest yield under both NT and CT, it the ranking of the Nachit variety changed drastically from the best variety under CT (9.57 t ha\({}^{-1}\)) to the second lowest yield under NT (7.97 t ha\({}^{-1}\)). Grain yield results show that all three nitrogen doses resulted in higher yields in NT compared to CT (Table 9), with mean values of 4.3, 4.1, and 3.9 t ha\({}^{-1}\) as opposed to 3.9, 3.8, and 3.6 t ha\({}^{-1}\) corresponding to 20, 40, and 60 kg N ha\({}^{-1}\), respectively. The same applies to straw yield, with mean values of 8.8, 8.2, and 8.1 t ha\({}^{-1}\) compared to 8.3, 7.8, and 7.4 t ha\({}^{-1}\) corresponding to 20, 40, and 60 kg N ha\({}^{-1}\), respectively. Louisa, M.G, and LC had higher grain yields (3.42, 4.52, and 4.74 t ha\({}^{-1}\)) under NT than under CT with the minimum nitrogen dose (20 kg N ha\({}^{-1}\)) (Table 9). On the other hand, the varieties Faraj and, in particular, Nachit require more nitrogen under NT than under CT (40 kg N ha\({}^{-1}\)) to achieve an increased grain yield (4.32 and 5.14 t ha\({}^{-1}\), respectively) (Table 9).
## 4 Open Issues
### Resource Self-Management  
Resource management is of paramount importance for QoS provisioning because the corresponding resource budgets need to be guaranteed in order to achieve certain QoS levels. This is particularly true for WSANs where computing, communication and energy resources are inherently limited. Generally speaking, a higher level of QoS corresponds to a need of more resources, e.g. CPU time, memory size, bandwidth and/or energy. Resource management in WSANs is challenging, because of the ever-increasing complexity of WSANs, highly dynamic feature of WSANs, and changing and unpredictable environments in which WSANs operate.  
To overcome these challenges, self-management technologies [17; 18] are needed. This implies that the system will address resource management issues in an autonomous manner. With respect to changes in resource availability, resource manager will automatically adapt resource usage in a way that the resulting overall QoS is optimized. This has to be performed in an efficient way. Since the resources are limited, the overhead of resource management should be minimized. In order to maintain scalability, distributed mechanisms have to be explored in this context.  
A promising way to go is to exploit feedback scheduling [4; 19-21]. Taking advantage of well-established control theory and technology, feedback scheduling offers a promising approach to flexible resource management in dynamic and unpredictable environments. Previous work has showed that feedback scheduling is capable of handling uncertainties in resource availability through automatically adapting to dynamic changes. It is anticipated that this technology can be used in WSANs to realize _resource self-management_ and to provide QoS guarantees. The predictability of the system can be enhanced thanks to the use of control theory. Nevertheless, how to map resource management to control problems is still subject of future research.
# Optimal Data Collection Time in LoRa Networks--A Time-Slotted Approach
## 4 Towards Time-Slotted LoRa Communications
### The Schedule  
We call _schedule_ a series of frames containing transmissions (slots) from all the nodes in the network. Nodes' transmission(s) are accommodated in one or more slots. TS-LoRa does not perform any scheduling since the nodes are serially placed into slots as soon as they register with the gateway using the minimum (energy-wise) possible SF. In our current problem, we can still follow the same rationale but this is not enough. This strategy could lead to long data collection times since some frames might be overcrowded. For example, if many nodes are placed close to the gateway, all of them would most likely use SF7 resulting to a large number of slots in that specific frame (See Figure 3a). To tackle that issue, scheduling is performed to find a better arrangement of nodes to slots (other than the serial one) so as to minimise the data collection time without violating duty cycle regulations (whenever they are applied).  
Assuming that nodes typically have more than one packet to send, transmissions from each particular node have a minimum periodicity limited by the duty cycle regulations as it is already mentioned. The time slots in between this periodicity can, therefore, be used to schedule transmissions from other nodes. In general, if the airtime (i.e., transmission time) for a SF is \(T_{f}\) and the duty cycle is 1%, the minimum period of time between two successive transmissions of the same node is \(T_{f}\times 100\) and approximately \(N_{f}=\frac{997_{f}}{T_{f}+2\tau}\) transmissions from other nodes with the same SF can be scheduled in the meantime, where \(\tau\) is the guard time at the beginning and at the end of a slot. If more nodes need to be assigned, the frame for SF \(f\) is extended. Moreover, a scheduler follows always a simple rule. A node (or transmission) is allocated to the frame that leads to the earliest completion of the data collection. Figure 3 illustrates such an example of allocation using three nodes with two transmissions each. The optimal solution is to schedule Node C transmissions to a frame with a higher SF since this results in a shorter data collection time (See Figure 3b).  
If the scheduler allocates each node transmissions to a different frame, this may result to a sub-optimal solution (See Figure 3c).
## 3 Methods
### Data Preprocessing
#### 3.1.2 Remote Sensing Image Data  
Initially, we conducted radiometric calibration and atmospheric correction on the Sentinel L1C image. Subsequently, we transformed it into more precise L2A data and performed reprojection conversion to guarantee the precision of spatial information. To ensure consistency in data processing and improve the accuracy of analysis, we resampled all bands of the Sentinel images to a uniform resolution of 10 m. Simultaneously, we made the decision to modify the resolution of the Google images, opting to set it at 6 m. This is due to the fact that a resolution that is too high can result in an excessive amount of data and a lack of compatibility with elements, such as the resolution of smart body motion and the alignment of feature maps. Hence, we opted for a resolution of 6 m in order to enhance the overall performance and efficiency of data processing.  
Figure 3: The technical framework.  
In order to enhance training efficiency and reduce resource use, we performed pre-cropping of the remote sensing images before training. We used square buffer zones as sites for cultivation. By using pre-cropping, we successfully included multi-threaded data loading during training by utilising PyTorch data loaders. This resulted in a decrease in CPU utilisation and a faster training procedure.
# Towards Interactive Analytics over RDF Graphs
## 4 Using HIFUN as an Interface to RDF Dataset  
There are several ways in which HIFUN can be used, such as for studying rewriting of analytic queries in the abstract [14] or for defining an approach to data exploration [39]. In this paper, we use HIFUN as a user-friendly interface for defining analytic queries over RDF data sets. To understand the proposed approach, consider a data source \(S\) with query language \(L\) (e.g., \(S\) could be a relational data set and \(L\) the SQL language). In order to use HIFUN as a user interface for \(S\), we need to (a) define an analysis context, that is a subset \(D\) of \(S\) to be analyzed, and some attributes of \(D\) that are relevant for the analysis and (b) define a mapping of HIFUN queries to queries in \(L\).  
Defining a subset \(D\) of \(S\) can be done using a query of \(L\) and defining \(D\) to be its answer (i.e., \(D\) is defined as a view of \(S\)); and similarly, the attributes that are relevant to the analysis can be defined based on attributes of \(D\) already present in \(S\). However, defining a mapping of HIFUN queries to queries in \(L\) might be a tedious task. In [39] such mappings have been defined from HIFUN queries to SQL queries and from HIFUN queries to MapReduce jobs.  
The main objective of this paper is to define a user-friendly interface allowing users to perform analysis of RDF data sets. To this end, we use the HIFUN language as the interface. In other words, we consider the case where the data set \(S\) mentioned above is a set of RDF triples and its language \(L\) is the SPARQL language. Our main contributions are: (a) the proposal of tools for defining a HIFUN context from the RDF data set \(S\) and (b) defining a mapping from HIFUN queries to SPARQL queries. With these tools at hand, a user of the HIFUN interface can define an analysis context of interest over \(S\) and issue analytic queries using the HIFUN language. Each such query is then translated by the interface to  
Figure 3: Running example expressed as a HIFUN context.  
a SPARQL query, which in turn is evaluated over the RDF triples of \(D\) and the answer is returned to the user.
## 3 Results
### Performance of the Task  
The performance of the task was measured using the time to complete the task and the number of errors made in performing the task. These two variables were adopted to indicate the efficiency and quality of the task being performed. Table 3 shows the statistics of the time to complete the task. The ANOVA results of this variable indicated that the effects of type of task (\(p\) < 0.0001), gender (\(p\) < 0.05), and type of instruction (\(p\) < 0.05) were all statistically significant. None of the interaction effects of those three factors were significant at \(\alpha\) = 0.05 level. The [PERSON]'s multiple range test results showed that the time to complete the assembly task (806.11 \(\pm\) 234.96 s) was significantly (\(p\) < 0.05) higher than that of the disassembly task (486.63 \(\pm\) 161.80 s). The time to complete the assembly and disassembly tasks for females (683.35 \(\pm\) 272.48 s) was significantly (\(p\) < 0.05) higher than that of males (612.75 \(\pm\) 239.35 s). The time to complete the assembly and disassembly tasks for AR instruction (714.55 \(\pm\) 235.11 s) was significantly (\(p\) < 0.05) higher than those of the printed manual (627.88 \(\pm\) 273.94 s) and video instruction (596.69 \(\pm\) 252.05 s). The time to complete the task between using printed manual and video instruction was not statistically significant.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **PPTd** & **PPTa** & **MDTd (s)** & **MDTt (s)** \\ \hline Female & 16.70 (1.16) & 43.40 (3.08) & 62.35 (4.55) & 55.80 (8.16) \\ Male & 16.86 (1.75) & 41.55 (6.31) & 60.55 (6.24) & 51.73 (6.86) \\ \hline \end{tabular} Note: PPTd: dominant hand test of the PPT; PPTa: assembly test using both hands of the PPT; MDTd: placing test using dominant hand of the MDT; MDTt: turning test using both hands of the MDT.  
\end{table}
Table 2: Mean (SD) scores of the PPT and MDT.  
Table 4 shows the number of errors made during performing the task. All those errors occurred in the assembly tasks. The number of errors made by the participants ranged from 0 to 4. The participants did not make any errors in the disassembly tasks. The mean number of errors made by each participant for printed manual, video, and AR instructions were 1.33, 0.62, and 0.33. The participants who followed the printed manual and video instruction made an error four times and almost two times the errors of those following the AR instruction. The ANOVA results showed that the type of instruction significantly (\(p<0.01\)) affected the number of errors made by the participants. The difference between male and female participants in making errors was not significant. [PERSON]'s multiple range test results indicated that the AR instruction led to significantly (\(p<0.05\)) lower number of errors than that of the printed manual. The difference between that of AR and video instructions and between that of video instruction and printed manual were both insignificant.
# Federated Learning-Based Resource Management with Blockchain Trust Assurance in Smart IoT
## 2 Challenges for Resource Management in the Future IoT Based on 6G SAGIN
### Representation of Multi-Dimensional and Multi-Domain Heterogeneous Resources  
In future networks, the objects of communication resources management will have new characteristics, such as higher frequency bands, wider bandwidths, and more 3D beams. In the ICT network, various resources in computers will also be unified with communication resources, including CPUs, GPUs, and various storage resources (such as memory, external memory, etc.).  
In addition, energy will be an important resource that needs to be managed in the future IoT. Although individual IoT devices consume less energy, the total energy consumption of massive IoT devices is staggering. The power supply and energy storage volume of heterogeneous IoT devices are also very different. For example, there are various power supply methods such as batteries, lithium batteries, and solar panels.  
The management resources in different domains may also differ, and the system metrics they are more concerned with may also be different. The various resources in different domains need to be managed and scheduled in a unified manner, which firstly requires solving the problem of representing these heterogeneous resources. Under this premise, it can be considered in a balanced compromise.
## 5 Case study  
### Parameters  
Basic parameters of WT, PV panel, diesel generator, super capacitor and a lead-acid battery are illustrated in Table 6.  
Different ES devices and backup sources possess various switching time characters. As for super capacitor, the switching time can be as short as microseconds (set as 20 ms in this paper). Meanwhile, BESS has a longer switching time compared to super capacitor, set as 1 min in this paper, via integrated consideration of component character and power quality requirement. For diesel generators, a warm-up process is required before supplying regulated power to the load, normally 40% of the rated power. This time varies according to the ambient temperature, ranging from 4 to 8 min, and can be up to 20-30 min under extreme conditions (i.e., \(-40\)\({}^{\circ}\)C temperature). The switching time of the diesel generator is set as 5 min in this paper. It is worth noting that the switching time of all power source devices relies on parameters of the control system and power-electronic converters, hence \(t_{\text{switch}}\) can be adjusted and modified accordingly in specific applications and this research aims to propose a generic approach.
###### Abstract  
The use of 3D scanning technologies for surface scanning of objects is limited by environmental conditions and technology requirements based on their characteristics. Among the emerging fields is technical diagnostics in areas of hard-to-reach places with varying surface characteristics of objects of different materials, where the use of commercially available 3D scanning technologies is limited by space. Furthermore, in these areas it is not convenient to use external reference elements or to move the equipment during the digitization process. This paper presents a novel markerless 3D scanning system capable of digitizing objects in confined spaces without requiring external reference elements or repositioning the device relative to the object and aims to address this challenge by designing a 3D scanning technology using the Active Shape from Stereo technique utilizing laser vertical line projection. For this purpose, a testing and prototype design and a software solution using a unique method of calculating 3D surface coordinates have been proposed. In addition to hard-to-reach places, this solution can be used as a desktop 3D scanner and for other 3D digitizing applications for objects of different materials and surface characteristics. Furthermore, the device is well suited to inspecting 3D printed objects, enabling quick, markerless checks of surface geometry and dimensions during the process of 3D printing to ensure printing accuracy and quality.  
reverse engineering; 3D scanning; stereovision; rapid prototyping; 3D scanner; digitization; markerless; point cloud; polygonization Article