esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Wearable physiological monitors have become increasingly popular, often worn during people's daily life, collecting data 24 hours a day, 7 days a week. In the last decade, these devices have attracted the attention of the scientific community as they allow us to automatically extract information about user physiology (e.g., heart rate, sleep quality and physical activity) enabling inference on their health. However, the biggest issue about the data recorded by wearable devices is the missing values due to motion and mechanical artifacts induced by external stimuli during data acquisition. This missing data could negatively affect the assessment of heart rate (HR) response and estimation of heart rate variability (HRV), that could in turn provide misleading insights concerning the health status of the individual. In this study, we focus on healthy subjects with normal heart activity and investigate the effects of missing variation of the timing between beats (RR-intervals) caused by motion artifacts on HRV features estimation by randomly introducing missing values within a five min time windows of RR-intervals obtained from the nsr2 db PhysioNet dataset by using Gilbert burst method. We then evaluate several strategies for estimating HRV in the presence of missing values by interpolating periods of missing values, covering the range of techniques often deployed in the literature, via linear, quadratic, cubic, and cubic spline functions. We thereby compare the HRV features obtained by handling missing data in RR-interval time series against HRV features obtained from the same data without missing values. Finally, we assess the difference between the use of interpolation methods on time (i.e., the timestamp when the heartbeats happen) and on duration (i.e., the duration of the heartbeats), in order to identify the best methodology to handle the missing RR-intervals. The main novel finding of this study is that the interpolation of missing data on time produces more reliable HRV estimations when compared to interpolation on duration. Hence, we can conclude that interpolation on duration modifies the power spectrum of the RR signal, negatively affecting the estimation of the HRV features as the amount of missing values increases. We can conclude that interpolation in time is the optimal method among those considered for handling data with large amounts of missing values, such as data from wearable sensors.  
heart rate; IoT wearable monitor; health +
Footnote †: journal: Sensors
## 5 Experiments and Analysis of Results
### Experimental Setup  
In this paper, we first generated the SSL certificate and public and private keys required by the TLS protocol through openssl-3.0.4, and configured the public key to the server and the private key to each client to ensure that the server side and the client side can normally use the TLS protocol in the experimental process to load certificates and connect to the establishment of other functions.  
After that, one server-side and five clients were set up, and the specific behavior was that each client transmitted 10,000 128-byte data to the server side.
# A Watermark-Based In-Situ Access Control Model for Image Big Data
## 4 Proposed Scheme
### Client Conformance  
Client conformance for access control can be processed as follows:  
1. Accessors request images via a particular client tool (e.g., image browser).
2. The browser prompts to ask for and obtain a secret key \(k^{\prime}\) and a role \(r^{\prime}\) corresponding to an accessor.
3. The browser extracts a QR code, obtains access control strategies (i.e., \(ACL::=\langle ROLE, AREA\rangle\)). All \(a\in ACL.AREA\) are obtained for \(r^{\prime}\in ACL.ROLE\). That is, \(A2R(a)=r^{\prime}\).
4. The browser computes \(f(k^{\prime})\), and decrypts all areas for \(r^{\prime}\) (i.e., \(a\)). Note that the key is not stored in the browser, and only \(f(k^{\prime})\) is computed temporarily by the browser and destroyed after browsing.
5. Calculate all \(j>l\), \(k[j,c]=g^{j-l}(k[l,c])\), \(k[l,c]=k^{\prime}\) and decrypt left areas at lower levels. That is, \(a\in ACL.AREA,A2R(a)\
eq r^{\prime}\) by \(k[j,c]\).
6. The browser displays all \(a\) to the accessor.
7. Accessors close the browser, and the browser image returns to its original encryption status.  
Figure 4: Hierarchical key-role-area access control model (\(HKRAGraph\)).  
**Remark 3**.: _Servers will maintain consistency with client tools for function \(f(\cdot)\) (i.e., the same \(f(\cdot)\)). Once the consistency is retained, \(f(\cdot)\) can be evolved further regularly to provide forward security. Alternatively, an extra pairwise key (e.g., \(key_{p}\)) between servers and client tools can be introduced into \(f(\cdot)\) as \(f(\cdot,\cdot)\) (e.g., \(f(\cdot,key_{p})\)). We stress that client tools do not locally and permanently store accessor keys. Instead, decryption keys for encrypted areas in images are computed temporally upon browsing._
# Novel Approach in Vegetation Detection Using Multi-Scale Convolutional Neural Network
## 2 Materials and Methods  
### Proposed Architecture  
The proposed MSCNN is designed to integrate multi-scale feature extraction and attention mechanisms, enabling the model to capture both fine and coarse vegetation patterns effectively. By employing multiple convolutional layers with varying kernel sizes, the MSCNN can extract features at different scales, which is crucial for accurately identifying vegetation in diverse environments. The architecture of the proposed model is illustrated in Figure 2. This network structure incorporates a sophisticated feature extraction process comprising multiple stacked convolutional layers, batch normalization (BN) layers, activation functions utilizing the Rectified Linear Unit (ReLU) method, and an attention mechanism. The integration of these components enhances the network's ability to learn complex patterns and representations from the input data, improving overall performance in object classification tasks. The detailed layer structure is presented in Table 1, where each layer is designed to extract features while progressively maintaining spatial hierarchies. Below is a breakdown of the functionality and significance of each layer. Figure 2: Proposed MSCNN network architecture. * [20] Batch Normalization (BN)
* [21] Max Pooling
* [22] Max Pooling
* [23] Batch Normalization (BN)
* [24] Transposed Convolution
* [25] Max Pooling
* [26] Max Pooling
* [27] Max Pooling
* [28] Batch Normalization (BN)
* [29] Batch Normalization (BN)
* [30] Transposed Convolution
* [31] Attention Layer
* [32] Softmax
* [33] Pixel Classification  
This layer combines the feature representation by applying a higher number of filters, focusing on detailed aspects of the input data. This layer refines the features extracted in previous layers, allowing for a more focused representation. The final convolution layer in this sequence applies a large number of filters to consolidate the learned features into a comprehensive representation. This layer further processes the data with larger receptive fields, allowing the network to capture more contextual information. A smaller filter size is used here to refine the output of previous layers, focusing on specific details. This final convolution layer enhances the feature representation before attention mechanism and classification.
# MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform
## 4 Multi-Application Scheduling Algorithm (MASA) Based on DRL
### Framework of MASA
#### 4.1.1 NN-Scheduler
For each node \(n\) in the job DAG, its priority value \(q^{i}_{v}\) is calculated according to Equation (9), as indicated by the red box in Figure 3.  
\[q^{i}_{v}=q\Big{(}e^{i}_{v}\,y^{i}_{v}\,z\Big{)} \tag{9}\]  
It can be seen from the formula that the \(q^{i}_{v}\) of each node in each DAG is related to three elements, i.e., the task-level vector \(e^{i}_{v}\) containing the attribute information and dependencies of the node itself; the information \(y^{i}\) contained in the DAG where each node is located; and all the global vectors \(z\) representing the connections between job DAGs. Tasks \(n^{i}_{v}\) with high priority values \(q^{i}_{v}\) in the state space are transferred to the set of ready tasks \(\mathcal{A}_{t}\). Each episode updates \(\mathcal{A}_{t}\). Subsequently, the sofmax function is used to calculate the mapping probability \(P\left(n^{i}_{v}\in\mathcal{A}_{t}\right)\) for each ready task in \(\mathcal{A}_{t}\), as shown in Equation (10). \[P(n^{i}_{v}\in\mathcal{A}_{t})=\frac{\exp\big{(}q^{i}_{v}\big{)}}{\sum_{n^{i} _{v}\in\mathcal{A}_{t}}\exp(q^{i}_{v})} \tag{10}\]
## 3 Simulation Results  
### Isotropic Wire Antenna  
All of the simulation shown in this section were carried out in Numerical Electromagnetics Code (NEC). Figure 7 shows the input impedance and SWR values vs. frequency of proposed antenna. The reactive part of the input impedance \(81.5+399\) \(\Omega\) at the operating frequency is compensated by loading the antenna feed point serially with a capacitor (C = \(4\times 10^{-13}\) F). Figure 8 shows the input impedance and Voltage Standing Wave Ratio (VSWR) graphs after applying a compensation at the input. A purely resistive input impedance of 81 \(\Omega\) of the antenna can be seen and it matches well with a 75 \(\Omega\) coaxial cable resulting in a VSWR of 1.08. For the VSWR parameter to be \(\leq\)2, the antenna exhibits an impedance bandwidth of 40 MHz.  
Figure 6: Current variation in proposed antenna.  
Figure 7: Input impedance and the Voltage Standing Wave Ratio (VSWR) of the non-compensated antenna.  
The 3D patterns of arc antenna's vertical, horizontal and total gains are shown in Figures 9 and 10, respectively.  
Figure 9 demonstrates that the horizontal and vertical gains have maxima of 0.2 dB, whereas their nulls are \(-16\) dB and \(-120\) dB, respectively. The addition of these two radiation patterns results in a near spherical pattern. This is due to the fact that maximas of both horizontal and vertical gains complement each other, resulting in maximum gain variation of 0.5 dB over the entire sphere, as shown in Figure 10. The two-dimensional (2D)-elevation plots of total gain with a \(10^{0}\) step and at different \(\phi\) angles were analyzed and three representative plots are shown in Figure 11. These plots suggest that the total gain is nearly independent of \(\phi\), since, for the total gain, the maximum difference in any elevation plane is less than or equal to 0.5 dB.  
Figure 8: Input impedance and the VSWR of the antenna after compensation.  
Figure 9: 3D radiation patterns of Vertical and horizontal gains of the arc antenna.  
Similarly, the azimuth plots of total gain with a 10\({}^{0}\) step and at different elevation angles were analyzed, and three representative plots are shown in Figure 12. Although the variation in total gain is slightly different for different elevation angles, the maximum gain variation in elevation plane for any angle is again \(\leq\)0.5 dB.  
Figure 11: Total gain vs elevation angle (\(\theta\)) at different azimuth angles \(\phi\).  
Figure 10: Three-dimensional (3D) radiation pattern of the arc antenna’s total gain. Maximum variation is \(\mid\) 0.5 dB.
# An Efficient Probe-Based Routing for Content-Centric Networking
The _probe_ that is utilized to update FIB can be any content in the network. Hence, one key ingredient in the FIB updating algorithm is _probe_ selection. The selection of _probe_ can be considered from two aspects, depending on the different performance measures. One goal is to satisfy pending interests as much as feasible to directly reduce interest waiting time, so the _probe_ is selected from PIT, called _PIT probe_. The other is to improve routing accuracy to reduce interest retransmission, so the _probe_ is selected from FIB, called _FIB probe_. In this paper, _PIT probe_ selects the most popular entry from PIT as the _probe_. _FIB probe_ selects the entry with the highest cost or the least updated as the _probe_. Our approach is an effective optimization as compared to basic CNN, as evidenced by the findings. _PIT probe_ performs best in terms of waiting time, whereas _FIB probe_ performs better in all the other measurements. In summary, the contributions of this paper include the following:  
* This paper presents an efficient _probe_-based routing algorithm that hybrid the advantages of TCP/IP and CCN. * Compared to basic CCN, our approach improves the routing mechanism to find the best content provider and the shortest path to the nearest content by modifying FIB,interest packet, and data packet, as well as adding a path planning table. By real-time exploring cached content in routers and finding the shortest path to the nearest content, our approach reduces network congestion, reduces response time, and raises FIB accuracy. Compared to other content exploration methods that relied on extra designated packets that increase the network burden, our approach minimizes the cost of exploration. * Different _probe_ selections, such as from PIT or FIB, are compared and analyzed. Except for the waiting time of pending interests, _PIT probe_ performs better on average response time, while _FIB probe_ has a better performance in all other measurements, which shows that the best content provider optimization is effective. * Compared to RECIF+PIF and EEGPR, owing to speedup FIB updating by probes, our approach provides more reliable interest packet routing when the router failures. * A real network topology is simulated to discuss the feasibility of our approach. The results show that it is highly compatible with the existing cache replacement strategy and forwarding strategy. The rest of this paper includes the following sections: Section 2 introduces the traditional IP-based routing protocols, the basic CCN background, and references about content exploration in CCN. Section 3 introduces the routing process of basic CCN and then describes our approach, including data structure, _probe_-based routing algorithms, and routing complexity. Section 4 introduces the simulation platform setup and the results are discussed in detail. Our work is summarized in Section 5.
# Proposal of an Adaptive Fault Tolerance Mechanism to Tolerate Intermittent Faults in RAM
## 4 Assessment of the Adaptive Fault Tolerance Mechanism
### Fault Injection Parameters
If an even number of fault activations occurred prior to memory access, they would cancel in pairs, reducing the effect of the intermittent fault. 6. Fault multiplicity. Transient faults may affect multiple locations due to technology scaling [5]. These multiple locations can be adjacent (i.e., neighbor cells in registers and memory, neighbor wires in a bus, etc.) or non-adjacent (i.e., random). Thus, we have injected both single and multiple faults in both random and adjacent bits (in the same memory word). In addition, multiple transient faults can occur when a single transient fault coincides with a previously uncorrected error [41], no matter what its origin is. We have also injected this type of multiple faults, particularly an intermittent fault followed by single and multiple transient faults. 7. Measurements. The data measured are: * The percentage of failures, P\({}_{\text{F}}\): \[\text{P}_{\text{F}}=\frac{\text{N}_{\text{F}}}{\text{N}_{\text{inj}}}\times 1 00,\] (2) where N\({}_{\text{F}}\) is the number of failures, and N\({}_{\text{inj}}\) is the number of injections (i.e., simulations). * The percentage of latent errors, P\({}_{\text{L}}\): \[\text{P}_{\text{L}}=\frac{\text{N}_{\text{L}}}{\text{N}_{\text{inj}}}\times 1 00,\] (3) where N\({}_{\text{L}}\) is the number of latent errors. * The detection coverage, \(\rm C_{D}\): \[\rm C_{D}=\frac{N_{D}}{N_{inj}}\times 100,\] (4) where \(\rm N_{D}\) is the number of errors detected. * The recovery coverage, \(\rm C_{R}\): \[\rm C_{R}=\frac{N_{R}}{N_{inj}}\times 100,\] (5) where \(\rm N_{R}\) is the number of errors corrected. * The percentage of intermittent errors detected, \(\rm P_{I}\): \[\rm P_{I}=\frac{N_{I}}{N_{inj}}\times 100,\] (6) where \(\rm N_{I}\) is the number of intermittent errors detected. To distinguish between failures and latent errors, we have classified the memory addresses into two categories. First, we have denoted the memory addresses where the workload results are stored (being part of the data segment) as _system outputs_ so that a value different from the simulation without faults will be considered as a failure. Second, we have denoted the rest of the memory addresses (including the code segment, the remaining of the data segment, and other unused addresses) as _internal elements_; in these addresses, a value different from the simulation without faults will be considered as a latent error.
## 2 R-SpaceFibre Design: Features and Trade-Offs
### SpaceFibre Data Link Layer  
The Data link layer of a fully compliant version of the protocol is described in this paragraph, with reference to a simplified architectural block diagram shown in Figure 2. It is composed by a Medium Access Controller (_MAC_), a transmission block (_Tx Block_) and a receiving block (_Rx Block_).  
The data interface with the host is realised trough VCs, both on the RX side (_Out VCBs_) and on the TX side (_In VCBs_). They allow handling independent flows of information across a single physical link. There are up to 32 different VCs available. A special interface is dedicated to broadcast (BC) messages, with one Rx buffer, Out Broadcast Channel Buffer (_Out BCB_), and one TX buffer, Input Broadcast Channel Buffer (_In BCB_).  
The _MAC_ is the core of the Data link layer: it manages data framing and it is responsible for synchronising the operations between transmitting and receiving side. Moreover, the _MAC_ implements the _QoS_: it continuously schedules which VC shall send data based on different parameters such as priority and reserved bandwidth.  
The Data link layer also implements retry buffers to resend data in the case the far-end of the link detects a communication error and asks for retransmission. The Retry Buffers block contains three buffers: the Data Retry Buffer, the Flow Control Token (FCT) Retry Buffer and the Broadcast Retry Buffer.  
Data or BC are read out from the selected VC or from the corresponding Retry Buffer and framed appropriately. Then, data frame could be scrambled to reduce electromagnetic emissions and eventually the Cyclic Redundancy Check (CRC) [20] block computes a numeric field to be added at the end of each data frame, broadcast message and control words that require it. The far-end will re-compute independently this value and will compare it with the received one, in order to detect accidental errors occurred during the communication process and promptly ask for retransmission of corrupted frames or control words.  
Conversely, on the Rx side, received data frames, BC messages and control words are handled. First, CRC Check block controls the correctness of the CRC value embedded in received data. In the case of errors, a retry request is sent to the far-end. If no errors are detected, data frame may be de-scrambled and stored in the appropriate _In VCB_, whereas broadcast messages are stored in the _In BCB_. Control  
Figure 2: SpaceFibre data link layer block diagram.  
words are consumed for managing the communication process. For detailed information, please refer to the SpaceFibre standard [2].
## 4 Conclusions  
In conclusion, the relevance of visible light communication (VLC) in the 6G era is underlined by its application versatility in various scenarios. From facilitating communication between vehicles in smart cities to enabling connectivity in outdoor spaces, VLC proves instrumental in addressing the evolving communication landscape. This paper aims to contribute to the understanding and optimization of VLC systems through the introduction of open-source tools. These tools assess the optical power distribution, allowing researchers to configure scenarios by specifying parameters like space size, wall presence, and reflective properties. The methodology incorporates realistic LED radiation patterns, enhancing scenario precision, visualization of 3D-curves, and evaluating power loss. The second part delves into multi carrier modulation schemes, OFDM and UFMC. The adaptations for UFMC in VLC are explored, revealing a superior performance compared to OFDM. The computed relative spectral efficiency gain demonstrates a 16% improvement with UFMC to be balanced by the increased computational complexity, particularly the filtering stage, that still has to be studied. On a practical note, the paper reflects the relevance of VLC in the 6G communication technologies landscape, as well as the integration of UFMC.  
**Author Contributions:** Conceptualization, V.G., M.D. and V.M.; data curation, V.G. and A.-C.H.; formal analysis, V.G. and A.-C.H.; funding acquisition, V.M.; investigation, V.G. and A.-C.H.; methodology, V.G., A.-C.H. and V.M.; project administration, M.D. and V.M.; resources, V.G.; software, V.G. and A.-C.H.; supervision, M.D. and V.M.; validation, V.G.; visualization, V.G. and A.-C.H.; writing--original draft, V.G.; writing--review and editing, V.G., M.D. and V.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the project Wal-e-Cities, a research project supported by the European Regional Development Fund (ERDF) of the European Union and also BEL6 GICA ([[https://www.6G.be](https://www.6G.be)]([https://www.6G.be](https://www.6G.be)) accessed on 19 March 2024) is supported by FOD \"Economie, K.M.O., Middenstand en Energie' of Belgium and \"The APC was funded by BEL6 GICA\".  
**Data Availability Statement:** All the codes can be found at: [[https://github.com/veroniquegeorlette/VLC_channel_modeling_python](https://github.com/veroniquegeorlette/VLC_channel_modeling_python)]([https://github.com/veroniquegeorlette/VLC_channel_modeling_python](https://github.com/veroniquegeorlette/VLC_channel_modeling_python)) (accessed on 19 March 2024).  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# A Resource Oriented Route Selection Framework Using Contextual Information Based on Fuzzy Logic
## IVv6 Routing Protocol for Low Power and Lossy Networks (RPL)  
RPL, standardized by IETF, is a distance vector routing protocol for LLNs that is compliant with the 6 LoWPAN protocol that makes use of IPv6. It operates at the network layer and supports several link-layer technologies, such as ZigBee, Bluetooth low energy (BLE), and IEEE 802.15.4. In this section, we present brief RPL mechanisms and features.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 10 Advancing Medical Diagnostic Videos: Limitations and Future Work
### Data Augmentation  
Data augmentation is a widely used technique for imbalanced or small datasets. It involves flipping, rotating, and cropping already available video frames to increase the size of the training dataset. Several researchers have used this technique [52; 70] to generate endoscopy and ultrasound frames. [PERSON] et al. [70] augmented video frames by a rotation of 20 degrees and flipping both ultrasound and gaze map frames horizontally, while [PERSON] et al. [52] applied plain rotation and random translation to augment the data. GANs are another approach successfully used for generating artificial data, as previously explained in the paper. Scientists are now using GANs to predict the next few frames in a video, and this technique can also be helpful for medical videos. This approach can help predict the subsequent frames if the medical video is captured by an undertrained medical staff and ROI is not fully captured. It is recommended that precaution should be taken while applying GANs to medical videos, because GANs do not learn the distribution of original videos; instead, they only copy them. Hence, the original videos are different from GAN-generated synthetic ones.