esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Physical Characterization Results  
Figure 3 shows the results for thermal conductivity and its relation to bulk density.  
In Figure 3, it can be seen how incorporating HDPE residue as a partial replacement produces a reduction in both bulk density and thermal conductivity values compared to the P0.65 reference material. Thus, it can be observed that bulk density in the P0.65-10% sample has decreased by up to 7.35% compared to the reference material. A similar behaviour was observed by [PERSON] et al., by incorporating polypropylene residues in substitution percentages of 2.5-5-7.5-10% by weight [25]. In the same way, thermal conductivity with the largest amount of recycled raw material is 0.19 W/m\(\cdot\)K, representing a 26.7% reduction compared to the P0.65 sample. This improvement in the thermal behaviour of plaster composites with the addition of plastic waste has already been observed by previous researchers, such as [PERSON] et al., who, by incorporating polycarbonate waste, managed to reduce gypsum composites conductivity by up to 32% [28], or [PERSON] et al., who incorporated shredded cable waste, obtaining conductivities close to 0.23 W/m\(\cdot\)K [16]. Finally, it should be noted that, in all physical properties, a linear trend has been observed in bulk density and thermal conductivity decrease as the HDPE content in the plaster composites increased.  
The results derived from the behaviour study of this material under water action are presented. It can be said that gypsum composites have a low durability against the action of water, as has been observed in previous research [46; 47]. However, in recent decades, plastic residues have been used to reduce the water absorption capacity of these composites, improving traditional gypsum material performance [32]. For this reason, the water absorption capacity of the developed materials has been analysed. These results are presented in Figure 4.  
Figure 3: Results for thermal conductivity and bulk density.  
Firstly, Figure 4 shows how capillary height reached at the end of the test decreases as the samples' HDPE content increases. These results agree with those obtained by [PERSON] et al. when incorporating granular polypropylene residues in gypsum composites in percentages such as those used in this research [25]. The lowest height reached was obtained in the P0.65-10% composite, being 4.8 cm after 10 min, which represents a decrease of 41.5% compared to the P0.65 sample. On the other hand, total water absorption was also reduced, because of the hydrophobic behaviour of the recycled HDPE. This absorption percentage was reduced up to 23.1% compared to the reference plaster in composites with higher recycled raw material content. The observed behaviour followed a parabolic trend, with absorption capacity decrease being sharper after the addition of 6% HDPE waste. This decrease in absorption capacity has been observed by other researchers who have worked with the addition of recycled plastic aggregates, such as [PERSON] et al., who observed a reduction in water absorption in gypsum composites using recycled rubber aggregates [48].
# Ultrasound Elastography in the Evaluations of Tendon-Related Disorders--A Systematic Review
## 3 Results
### Study Characteristics
#### 3.3.4 Tendons  
* Supraspinatus tendon evaluation was present in 9 papers [23; 37; 38; 39; 40; 45; 53; 55];
* Subscapularis tendon evaluation was present in 2 papers [23; 38];
* Infraspinatus tendon was present in 3 papers [23; 37; 45];
* Teres minor tendon evaluation was present in 1 paper [23];
* Biceps tendon evaluation was presented in 4 papers [27; 35; 37; 45];
* Common flexor tendons of the Hand evaluations were presents in 4 papers [24; 26; 37];
* Common extensor tendons of the Hand evaluations were presented in 8 papers [25; 30; 36; 37; 43; 52; 54; 58]
* Abductor and extensor Pollicils evaluation were presented in 2 papers [28; 31]
* Patellar tendon evaluation was present in 9 papers [32; 34; 37; 41; 42; 47; 48; 54; 57]
* Achilles tendon evaluation was present in 11 papers [22; 29; 32; 33; 37; 44; 46; 50; 54; 56; 59].
# Effect of Chemical Composition of Bio- and Petroleum-Based Modifiers on Asphalt Binder Rheology
## 3 Results
### Rheological Characterization of Modified Binders
#### 3.2.3 Frequency Sweep Test
Upon further aging (2 PAV and 3 PAV), the trends for highest and lowest \(\delta\) are again similar to that of PAV aging. However, the separation in \(\delta\) became more evident (Inset Figure 7c,d). At 3 PAV, K-modified binder has the lowest \(\delta\), followed by E, D, S5, C, and G. The \(\delta\) values for G-modified binder suggest a greater viscous component in the complex shear modulus, indicating a flowing behavior, thus possibly lower cracking susceptibility. Figure 7: Black space diagrams at reference temperature of 15 \({}^{\circ}\)C for aging conditions (**a**) RTFO; (**b**) PAV; (**c**) 2 PAV; and (**d**) 3 PAV. Glover-Rowe (GR) Parameter  
GR values are indicated in the black space diagrams, shown in Figure 8, for all aging conditions. Modified binders containing A, D, and E show similar characteristics to unmodified binder (S5) at all aging conditions (Figure 8a). Differently, in case of modifier C, higher resistance to low-temperature cracking was observed. A GR of 180 kPa is a criterion for damage onset for non-load-associated low-temperature cracking (shown in the red-dotted line in Figure 8), while 450 kPa is indicative for severe cracking (shown in the red-solid line in Figure 8) [23]. As per these thresholds, damage onset initiates around 2 PAV while severe damage takes place at around 3 PAV condition for S5 and binders containing modifiers A, D, and E.  
In Figure 8b, for G-modified binder the evolution of GR parameter suggests higher resistance to aging compared to S5. The GR value for 3 PAV of G is similar to that of 2 PAV for S5. In contrast, GR for K-modified binder increases at a much faster rate than S5, suggesting potential early damage. The GR values of PAV and 2 PAV for K are similar to 2 PAV and 3 PAV of S5, respectively. Figure 8a,b show that G-modified binder does not reach onset of severe cracking criterion after 3 PAV while K-modified binder surpasses the criterion after 2 PAV.
## 2 Related Works
### TFHE-Based SNNI  
TFHE [37] is an HE scheme that represents ciphertexts in the torus modulo 1. TFHE can execute rapid binary operations on encrypted binary bits. Furthermore, TFHE is equipped with a fast bootstrapping technique. Compared to LHE schemes, TFHE hastwo advantages. Firstly, unlike the LHE schemes approximating the ReLU by expensive polynomial operations, TFHE can exactly evaluate the ReLU function by boolean operations. Secondly, TFHE allows unbounded homomorphic operators that can be performed on ciphertext, thanks to the fast bootstrapping technique. However, TFHE only works on binary gates, and current TFHE-based SNNI frameworks work with binarized neural networks (BNNs) [38]. BNNs restrict the weights and inputs of their linear layers to the set \(\{-1,1\}\). It employs the activation function sign, which is straightforward to implement in boolean circuits. FHE-DiNN [39] is the first TFHE-based SNNI framework. While FHE-DiNN performs inference on MNIST faster than CryptoNets, its accuracy is notably lower than CryptoNets because it is limited to supporting smaller BNNs.  
Subsequent works, i.e., TAPAS [40] and SHE [41], improved the efficiency of FHE-DiNN, but their core idea was merely based on FHE-DiNN, i.e., using TFHE over BNN. Due to the limitation of operating with BNN, the accuracy of TFHE-based frameworks falls short when compared to the plain NN (running inference on the original NN). For example, ref. [42] evaluated the most prevalent HE schemes for SNNI, namely CKKS, BFV, and TFHE; while CKKS and BFV achieve comparable accuracy with the plain NN (96.34%), TFHE's accuracy was significantly lower (82.70%). As shown in Table 1, while other approaches achieve comparable accuracy with the plain NN, the TFHE-based approach falls short in accuracy. Furthermore, since the packing technique does not easily align with the TFHE scheme, it might introduce extra inefficiencies in terms of running time and memory overhead when we want to process large amounts of data concurrently.  
Compared to the TFHE-based approach, HeFUN is more accurate (we achieve the same accuracy with the plain NN) and efficient (supports SIMD). Furthermore, HeFUN also achieves unbounded depth and can exactly evaluate the ReLU activation function.
# An Event-Driven Serverless ETL Pipeline on AWS
## 6 Discussion
### Performance  
We monitored the execution time on each subtask that was implemented in the worker lambda function, bearing in mind that AWS does not provide clear specifications, apart from memory, in their execution environments. Generally, as expected, higher memory functions perform orders of magnitude faster in terms of raw processing. More precisely, the results suggest that the processing power is linearly proportional to the memory since the execution time halves with each memory upgrade.  
Figure 4 illustrates the end-to-end times from the source to sink of a single event increasing in size. Specifically, for an event with a payload size of 100 MB, it takes slightly more than 2 min. to process and persist in data sink, which results in around 750 KB/s processing speed that is acceptable, but could be problematic at high data volumes. The cause of this overhead is discussed further below.  
Figure 3: Mean execution times, failed invocations, cold starts, and success rate for distinct Lambda worker operation categorised by lambda function’s available memory.  
Figure 4: End to end data flow times for event payload size and lambda worker memory. Table rows show time in seconds per payload size. Cells without a value indicate that the lambda worker failed to process the payload.  
Similarly, Figure 5 demonstrates the time that is spent performing on each worker subtask. In larger data volumes, sending the data to SQS takes up most of the invocation time, as it consists of blocking network operations. This is due to the need for chunking the data in order to overcome the SQS payload size limits. Alternatives are discussed in future work, as that intensive network operation can undermine successful processing of larger data volumes. Notably, while the worker function forwards the transformed data to the queue, the writer function is invoked in parallel upon notification of the first chunk. Consequently, the total end-to-end data delivery time is reduced.
## 5 Conclusions  
In this paper, a novel artifact handling method is proposed for virtual view synthesis, which fully utilizes the information in the depth image. For OFRs, we extend the reference view and fill it with a modified inpainting method. Pixels that are considered artifacts are detected during depth image preprocessing and then projected to the correct space. To deal with cracks and pixel overlapping, we present an adaptive one-to-four method to optimize 3D warping and select the correct pixel to display in the virtual image based on the depth information. The depth term and background term are introduced into the calculation of the priority to fill the disocclusion. The foreground and background are divided according to the depth value of the pixels around each hole instead of the global threshold. Furthermore, the source region is limited to prevent foreground pixels from being incorrectly sampled into the disoccluded region. Adaptive patch size is applied to improve the quality and accuracy of the image inpainting, taking into account the various textures in the image. The experimental results demonstrate that the proposed method shows satisfactory results in subjective and objective evaluation. Our future work will focus on two aspects. The first is to improve the visual quality of the synthesized view by using the temporal correlation between the adjacent frames of the reference video and the complementary relationship of multiple reference views. The second is to introduce the deep learning algorithm into the hole filling process. Through the training of deep networks, the computation time of the algorithm will be effectively reduced without degrading the quality of the virtual image. In addition, artifact handling based on deep learning can be robust to complex scenes through extensive training in different scenes.  
Figure 14: Objective comparison results: (**a**) PSNR of BA54; (**b**) PSNR of BA52; (**c**) SSIM of BA54; and (**d**) SSIM of BA52.  
**Author Contributions:** Conceptualization, X.C. and H.L.; methodology, H.L. and Y.W.; software, S.R.; validation, H.L., H.X. and S.R.; data curation, H.L.; writing--original draft preparation, X.C., H.L. and Y.W.; writing--review and editing, X.C., H.L. and H.C.; supervision, X.C.; project administration, X.C. and Y.W.  
**Funding:** This research was funded by the National Major Project of Scientific and Technical Supporting Programs of China during the 13 th Five-year Plan Period, grant number 2017 YFC0109702, 2017 YFC0109901 and 2018 YFC0116202.  
**Acknowledgments:** The authors would like to thank the Interactive Visual Media Group at Microsoft Research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Conclusions  
Identifying building functional types is important in urban planning assessment and spatial refinement governance. POI data are characterised by having rich social attribute information and high timeliness and are used for urban functional classification research. Combining POI with online building footprint data is a popular way to identify building function types. It is difficult to accurately identify building functions that do not contain POIs in areas with sparse POI distributions. To address this problem, this paper proposes an urban building function identification method based on the spatial relationship between POIs and entities. Its steps can be summarised as follows: (1) the functions of buildings containing POIs are identified by calculating the frequency density ratio of POI types. (2) The spatial topology and similarity between POIs, buildings, and road network entities are combined to calculate the area, shape, distance and orientation similarities between buildings with and without POIs in the same road network. (3) The entropy weighting method is used to determine the weight of each similarity and, finally, (4) the functions of building containing POIs are applied to buildings without POIs based on the maximum similarity.  
The experiments show that the building function identification accuracy, recall and F1 value of the proposed method were 90.28%, 97.52% and 93.76%, respectively. Compared with existing methods (Kernel density method [29], Tyson polygon area share method [33]), the proposed method is >10% more accurate overall. In addition, the POI, building, and road data used in this paper were all obtained from online platforms and are easily accessible, making the proposed method very easy to generalise and apply. In the future, we will conduct experiments in different regions to further validate the generalisability of the method.  
Conceptualisation, G.L.; methodology, Y.W.; resources, L.Y.; data curation, L.Y.; writing--original draft preparation, Y.W.; writing--review and editing, G.L.; visualisation, Z.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University-Industry Collaborative Education Program (Grant number: 220603776263313), Industry-university-research Innovation Fund for Chinese Universities (Grant number: 2023 BD033), Guangxi Science and Technology Program (Grant number: Guike AB24010025) and the Natural Resources Digital Industry Academy Construction Project.  
Not applicable.  
Publicly available datasets were analyzed in this study. POI data and building footprint data can be found here: [[https://www.amap.com/](https://www.amap.com/)]([https://www.amap.com/](https://www.amap.com/)) (accessed on 10 January 2023). Road data can be found here: [[https://www.openstreetmap.org/](https://www.openstreetmap.org/)]([https://www.openstreetmap.org/](https://www.openstreetmap.org/)) (accessed on 16 December 2022).  
The authors declare no conflicts of interest.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### ETX-Based Solutions
#### 4.2.2 Multi-Fuzzy Model (MI-FL)  
Author [61] proposed a Multi-fuzzy model which overcomes the limitation of the fuzzy logic system in which individual metrics combination illustrates objective function to select the best parent. The multi-fuzzy model supports three metrics, i.e., node metrics, link-oriented metrics, and channel-oriented metrics. Node metrics include node energy, the number of neighbors connected, and ETX. Link-oriented metrics include link stability, mobility, and hop counts, whereas, the channel-oriented metric includes channel capacity.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Working Principle** & **Issues Addressed** & **Advantages** & **Disadvantages** \\ \hline ME-RPL [44] & Mobility information is added in the DIO message of mobile IoT to differentiate static and mobile nodes. & Lack of facility of identifying mobile nodes. & ME-RPL shows lower PLR and higher stable routes as compared to RPL. & No discussion provided for mobile nodes selected as Preferred Parent and its effect on network performance. \\ \hline Elastic Tirckle Timer Algorithm [59] & The ’listen-only’ period is dynamically selected based on the number of neighboring nodes. & Issues with the Trickle timer. & 76\% convergence time, power consumption of 1.5 Mj is obtained with \(Rx\) value of 20\% and average PDR of 87\%. & The effects of diverse \(I_{min}\) and \(I_{max}\) intervals over its performance evaluation have not been discussed. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
Author [61] has enhanced BMRF (Bi-directional Multicast RPL Forwarding) [62], and the proposed algorithm used is (EBMRF) Enhanced BMRF in which duplicated packets are suppressed. The occurrence of duplicate packets is mainly because of packet forwarding to multiple nodes by the parent node. Parent node multicasts packets to the child nodes whereas packets from downward nodes are considered to be unicast. In this EBMRF, a unique ID is included in each multicast packet which will suppress duplicate packets by identifying the packet ID. In addition, the delay problem in which collisions were caused by the use of the same channel for sequential communication is solved by selecting the idle channel which is free for transmitting data.
## 1 Introduction
's dataset of 14,266 fire photos and 12,073 non-fire images as well as a superpixel learning set of 54,856 fire and 167,400 non-fire superpixels on a test set of 1178 fire and 881 non-fire samples. The experiment yielded a 95% accuracy and 40 fps frame rate in fire classification for the entire frame and a 97% accuracy and 18 fps frame rate in super pixel localization. Additionally, testing on low-power devices in Nvidia Xavier-NX showed that this model is acceptable for real-world deployment applications, with 40 fps over full-frame classification with ShuffleNetV2-OnFire. In general, datasets utilized in fire detection studies consist of data collected and analyzed from actual fire sites, and these data are frequently inappropriate for early fire detection. Furthermore, fire data from various contexts are restricted for use in closed fire-vulnerable spaces such as factories, offices, and specified locations. As a result, a dataset that synthesizes fires in digital twin simulations of fire-vulnerable locations is required, and this dataset should be developed to reflect the illumination, location, and diffusion processes in varied contexts from early to mid-fire. The fire early detection system proposed in this study uses RGB-D cameras to obtain RGB-D images that are nearly identical to those of actual site CCTV cameras, generates virtual fires that are difficult to distinguish from real fires based on particle simulation, and creates various fire situations for highly accurate detection and low false alarm rates. Consequently, a vast amount of optimal customized learning data are generated to train an artificial intelligence model for early fire detection.
## 2 System Model
### Information Relay and Dissemination  
Once a collision alert is generated, it is critical that this information is transmitted to nearby vehicles and relevant authorities in a timely manner. The proposed system employs a tier-2 algorithm to relay this information across the VANET. Vehicles located at the edge of a cluster, based on their SNR values, act as relays to forward the alert to other clusters and the central base station.  
The SNR for each vehicle is calculated as:  
\[\text{SNR}_{i}=\frac{P_{\text{signal}}}{P_{\text{noise}}}\]  
where \(P_{\text{signal}}\) and \(P_{\text{noise}}\) represent the signal power and noise power, respectively.
## 3 PR Calculation in a GNSS Receiver
### How the GNSS Receiver Implements the PR Computation  
In order to process tracked signals independently, GNSS receivers assign each one a dedicated channel. As said, to obtain \(\rho\) it is necessary to know the transmitted time \(T_{t}\) for each satellite and to correct all the misalignments of the receiver and the satellite clock with respect to the system time. Two possible implementations of this measurement are Common transmission time and Common reception time [18; 19]. The former one is based on the satellites' transmission time. Obviously the channels are not synchronised each other at the receiver side, so on each channel the same bit of a subframe (and relative time of arrival of it) have to be identified. The receiver selects a reference channel by using the first arriving bit, and calculates the relative delay \(\Delta_{i}\) with respect to the bit of \(i\)-th satellite, \(\Delta_{i}=T_{RX,i}-T_{RX,1}\). This allows to write the range \(r_{i}\) of the \(i\)-th satellite as \(r_{i}=\rho_{1}+c\,\delta t_{i}+c\,\Delta b+c\,\Delta_{i}\), where \(\rho_{1}\) is the PR of the reference channel or in other words, the satellite closest to the user, \(\Delta b\) is the unknown bias for the not perfect synchronisation between clock on board the \(i\)-th satellite and the clock of the receiver. Finally, \(\delta t_{i}\) is the correction of the offset of the \(i\)-th satellite clock from system time.  
In the second implementation (Common reception time) the receiver calculates \(\Delta_{i}=T_{u}-T_{RX,i}\), where, in this case, \(\Delta_{i}\) is the time interval between the instant of reception of the subframe for the \(i\)-th channel \(T_{RX,i}\) and \(T_{u}\), that is the common receiving time when the receiver decides to measure the PR over all channels. All the PRs are derived with respect to the reference channel, which is the one with the minimum travel time. Once \(\Delta_{i}\) are computed for all the channels, \(r_{i}=\rho_{1}+c\,\Delta b+c\,\left(\Delta_{1}-\Delta_{i}\right)+\delta t_{i}\).  
To measure the time interval \(\Delta_{i}\), the receiver continuously counts the samples processed per each channel and maintains a monotone counter in 20 ms increments derived from receiver's reference oscillator [17]. The mechanism of counting partially protects the PR measurements from correlation distortions. In next section this is proved by simulation since a theoretical explanation would require a deep analysis of both DLL and counting operations.  
The Common reception time is the method usually employed in commercial GNSS receivers.