esa-sceva/satcom-chunk-collection huggingface dataset:
# Truck Platoon Control Considering Heterogeneous Vehicles
## 3 Multi-Vehicle Control
### Lateral Control  
This subsection describes how to control the lateral maneuver of each truck. Consider the case where \(p_{i}\) at time step \(k\) heads towards a waypoint whose coordinate is \((x_{d},y_{d})\). The reference _yaw_ angle \(\Psi_{k}^{i}\) is calculated as  
\[\Psi_{k}^{i}=\tan^{-1}\frac{y_{d}-y_{k}^{i}}{x_{d}-x_{k}^{i}}. \tag{17}\]  
At each time step \(k\), we control the yaw of \(p_{i}\) so that it converges to the reference yaw angle \(\Psi_{k}^{i}\). Recall \(\Psi_{k}^{i}\) is set to make the truck head towards the next waypoint. Let \(e_{\psi}=\Psi_{k}^{i}-\psi_{k}^{i}\) for convenience.  
One changes \(e_{\psi}\) so that it exists between \(-\pi\) and \(\pi\).  
\[e_{\psi}=\tan^{-1}(\sin(e_{\psi})/\cos(e_{\psi})). \tag{18}\]  
Recall that \(\dot{\psi}_{M}^{i}=\frac{s_{k}^{i}}{T+L^{i}}\tan(\delta_{M}^{i})\) denotes the maximum yaw rate of a truck \(p^{i}\). If \(\|e_{\psi}\|>\dot{\psi}_{M}^{i}*T\), then we set the steering angle as  
\[\delta_{k}^{i}=sign(e_{\psi})*\delta_{M}^{i}. \tag{19}\]  
The third equation of (1) is  
\[\psi_{k+1}^{i}=\psi_{k}^{i}+T*\frac{s_{k}^{i}}{L^{i}}\tan(\delta_{k}^{i}). \tag{20}\]  
(19) and (20) lead to  
\[\psi_{k+1}^{i}=\psi_{k}^{i}+sign(e_{\psi})*\psi_{M}^{i}*T. \tag{21}\]  
Here, \(sign(e_{\psi})\) denotes the sign of \(e_{\psi}\). This implies that we change the yaw of the truck with maximum yaw rate \(\dot{\psi}_{M}^{i}\).  
If \(\|e_{\psi}\|\leq\dot{\psi}_{M}^{i}*T\), then we set the steering angle as  
\[\delta_{k}^{i}=\tan^{-1}(\frac{e_{\psi}*L^{i}}{T*s_{k}^{i}}). \tag{22}\]  
(22) and (20) lead to  
\[\psi_{k+1}^{i}=\Psi_{k}^{i}. \tag{23}\]  
This implies that the yaw angle of the truck converges to the reference yaw angle.
# Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach
## 3 Results and Discussions
### Streaming Algorithm Approach for Personalized Thermal Sensation Monitoring  
In this paper, we introduce the main framework of streaming algorithm for personalized classification model to predict individual's thermal sensation based on streaming data obtained from wearable sensors. The main framework of the proposed streaming algorithm approach is depicted in Figure 8.  
The main components of the proposed algorithm (Figure 8) are explained in the following:  
_Streaming data_  
The availability of the real-time sensors data, from the wearable technologies, has given the possibility of streaming data, which processed via the proposed online streaming algorithm to adapt and personalize the classifier model. The streaming data includes:  
1. Wearable sensor data, which consists of the continuously measured variables, namely, individual's heart rate, skin heat flux, skin temperature, ambient temperature and aural temperature.
2. Data obtained from the interactive mobile App., which consists of personal data, namely, age and gender. Additionally, the individual's thermal sensation vote is to be obtained via mobile application-based questioner. The workflow procedures of streaming data acquisition and labelling are depicted by the flowchart shown in Figure 9.  
_Feature extraction_  
As shown earlier, the selected 12 features are extracted from the continuously measured variables, namely, \(H_{R}\), \(T_{\sigma}\), \(T_{sk}\), \(\Delta\overline{T}\), \(\overline{q}_{sk}\), the _rms_ of (\(H_{r}\), \(T_{c}\), \(T_{sk}\), \(\hat{q}\)), and \(\frac{\overline{d}_{jk}}{dt}\). Other personal futures, namely, age and gender are to be obtained via the interactive mobile App. from individual users.  
_Labelled data_  
All training data must be labelled, either the old training data or the new personal data. Personal data is labelled manually via the questionnaire provided by the mobile App.  
Figure 8: Schematic representation of the proposed streaming algorithm for online personalized thermal sensation monitoring.  
Unlabelled data is the new data points to be labelled by the classifier, these unlabelled data points include the extracted features from the measured variables.  
_Localized Learning Algorithm_  
The localized learning algorithm (i.e., KNN-LS-SVM) is the classifier that receives the unlabelled data points and train a dedicated model with the \(K\) nearest training points in order to label the unlabelled ones. The output of this process is a predicted label of personal thermal sensation (\(\hat{T}_{S}\)).
# Energy Efficient Resource Allocation for M2M Devices in LTE/LTE-A
###### Abstract  
Machine-to-machine (M2M) communication consists of the communication between intelligent devices without human intervention. Long term evolution (LTE) and Long-term evolution-advanced (LTE-A) cellular networks technologies are excellent candidates to support M2M communication as they offer high data rates, low latencies, high capacities and more flexibility. However, M2M communication over LTE/LTE-A networks faces some challenges. One of these challenges is the management of resource radios especially on the uplink. LTE schedulers should be able to meet the needs of M2M devices, such as power management and the support of large number of devices, in addition to handling both human-to-human (H2H) and M2M communications. Motivated by the fundamental requirement of extending the battery lives of M2M devices and managing an LTE network system, including both M2M devices and H2H users, in this paper, two channel-aware scheduling algorithms on the uplink are proposed. Both of them consider the coexistence of H2H and M2M communications and aim to reduce energy consumption in M2M devices. The first algorithm, called FDPS-carrier-by-carrier modified (CBC-M), takes into account channel quality and power consumption while allocating radio resources. Our second algorithm, recursive maximum expansion modified (RME-M), offers a balance between delay requirement and energy consumption. Depending on the system requirements, RME-M considers both channel quality and system deadlines in an adjustable manner according to M2M devices needs. Simulation results show that the proposed schedulers outperform the round-robin scheduler in terms of energy efficiency and have better cell spectral efficiency.  
LTE/LTE-A; M2M; MTC; power saving; resource allocation; uplink scheduling Article
# Void Avoiding Opportunistic Routing Protocols for Underwater Wireless Sensor Networks: A Survey
###### Abstract  
One of the most challenging issues in the routing protocols for underwater wireless sensor networks (UWSNs) is the occurrence of void areas (communication void). That is, when void areas are present, the data packets could be trapped in a sensor node and cannot be sent further to reach the sink(s) due to the features of the UWSNs environment and/or the configuration of the network itself. Opportunistic routing (OR) is an innovative prototype in routing for UWSNs. In routing protocols employing the OR technique, the most suitable sensor node according to the criteria adopted by the protocol rules will be elected as a next-hop forwarder node to forward the data packets first. This routing method takes advantage of the broadcast nature of wireless sensor networks. OR has made a noticeable improvement in the sensor networks' performance in terms of efficiency, throughput, and reliability. Several routing protocols that utilize OR in UWSNs have been proposed to extend the lifetime of the network and maintain its connectivity by addressing void areas. In addition, a number of survey papers were presented in routing protocols with different points of approach. Our paper focuses on reviewing void avoiding OR protocols. In this paper, we briefly present the basic concept of OR and its building blocks. We also indicate the concept of the void area and list the reasons that could lead to its occurrence, as well as reviewing the state-of-the-art OR protocols proposed for this challenging area and presenting their strengths and weaknesses.  
void avoiding; opportunistic routing (OR); underwater wireless sensor networks (UWSNs); void area; routing +
Footnote †: journal: _Sensors_  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Void Avoiding Opportunistic Routing Protocols for Underwater Wireless Sensor Networks: A Survey. Sensors **2022**, 22, 9525. [[https://doi.org/10.3390/s22239525](https://doi.org/10.3390/s22239525)]([https://doi.org/10.3390/s22239525](https://doi.org/10.3390/s22239525))
## 3 Results
### PEMS Differences  
As a second approach for each test, the absolute and relative differences of the Golden and Lab PEMS to the bags were calculated. The means and the standard deviations of the differences for the pollutants are presented in Figure 2. The mean differences of PEMS to the bags were \(\pm\)1.5 g/km (or \(\pm\)1%) for CO\({}_{2}\), <16 mg/km (or <5%) for CO, <4 mg/km (or <11%) for NO\({}_{\rm x}\) and 1 \(\times\) 10\({}^{11}\) particles/km (40%) for PN. These differences indicate negligible, if any, bias for the gaseous pollutants. The scatter of the differences lies within the permissible limits for the gaseous pollutants, with CO and NO\({}_{\rm x}\) reaching the relative permissible limits in some cases. However, for PN, it seems that the PEMS measurements at tailpipe are significantly higher than the measurements at the dilution tunnel. The PN scatter (error bar) is acceptable (30%).  
Figure 1: Mean emissions from all laboratories with different equipment: Bags (eight laboratories), Golden Portable Emissions Measurement Systems (PEMS) (seven laboratories, one instrument), Lab PEMS (five laboratories, five instruments). Upper panel shows gaseous pollutants. The lower panel shows particle number and mean exhaust flow rate (EFR). Error bars show one standard deviation.
## 4 Interference Detection Based on Joint TF Analysis by Using RSPWVD  
Although the WVD has many good properties and provides nearly the best resolution among all of the TF analysis techniques, due to its intrinsic quadratic nature, it suffers from cross-term interference when it is applied to multi-component signals. This drawback severely hinders the usefulness of the WVD for detecting RFI characteristics in the TF plane. In Section 3, the PWVD has been used to suppress the cross-terms for multi-component signals; however, many desirable properties of the WVD, such as marginals and instantaneous frequency, are annihilated, and the TF concentration property is also attenuated. In order to eliminate the cross-terms present in the quadratic TF distribution and to preserve  
Figure 3: Pseudo Wigner–Ville distribution of four Gaussian components’ signal.  
good time and frequency resolution at the same time, a reassigned SPWVD method has been proposed in interference detection for GNSS receivers.
## 8 Discussion and Future Directions
Zilliga sharding technology performs the division of network nodes through POW algorithm. It selects a Boss sharding and multiple work sharding, each sharding contains no fewer than 600 nodes. Each transaction is mapped to a work sharding according to the address, and the sharding verifies the transaction through the PBFT consensus algorithm and forms a sub-block in the current work shard. Then, the work sharding sends the sub-blocks to the boss sharding. After the boss sharding's verification, the final blocks are merged and generated. * Change the structure of the single chain. For example, the Huobi Chain [151] adopts double chain structure and dynamic collaboration. The trading chain is responsible for transaction clearing, pursuing transaction speed and reducing fees. The contract chain supports complex applications such as financial contracts. The IOTA mentioned above also uses a Tangle structure instead of a single chain. * Privacy protection and regulatory mechanism are two contradictory problems. In the future, a system framework can be developed to balance the privacy protection and the regulation. Perhaps Idemix, zero-knowledge proof, attribute encryption and other methods can help solve this problem. At present, some studies have begun to focus on the regulatory system of blockchain. Analyzing the regulatory mechanisms of the European Union and the United States indicates that it is possible to have greater value added with the minimum regulatory limit [152]. * A unified safety evaluation system can be established for different safety defects. This is not only conducive to the development of blockchain products, but also conducive to security audit. For example, [PERSON] and [PERSON] [153] developed several evaluation metrics based on the security of work certificates. * Energy certification and verification. Measures should be implemented to prevent cheating producers whose energy is actually generated from fossil fuels but claim it is renewable energy. Sunchain [45], for example, has added such checks to its program.
## 4 Effects of Weather Conditions in the Gulf of Guinea on the IoT Quality of Service
### Analysis during the \"Calm Sea\" and Calm Weather on the HFSWR Sites  
Such atmospheric conditions are characterized by clear and sunny weather with low intensity wind. The effects of such weather on the operation of HFSWRs are practically negligible. The main reason is the fact that the light wind, which characterizes such weather, has very little effect on the surface of the sea by producing waves of low altitude. Under these conditions, the sea state does not exceed 3 according to the Douglas scale [31], and according to [32], the propagation of EM waves across the sea surface is the best possible. As far as the vessel detection is concerned, it is possible even beyond the nominal HFSWR range. One such example is presented in Figure 8.  
In Figure 8, the vessel detection and tracking at a distance of 270 km is shown, which is well beyond the nominal HFSWR range. Furthermore, the clear weather does not introduce any difficulties in the operation of the satellite links, and there are no problems with communication between the HFSWRs and the command centers. This means that data will arrive in a timely manner and without significant delays. In clear weather there are no data delays, so it can be said that the IoT OTH maritime surveillance service operates in optimal conditions. Figure 9 shows the satellite link operation in one day by hour during a period in a day in the conditions of the \"calm sea\" (at the top of the Figure, we have shown the weather conditions for that day in parallel).  
Figure 8: Surveillance during favorable weather conditions.  
Figure 9: Transfer rate during favorable weather conditions.  
Analysis When Strong Winds Are Present in the HFSWRs Surveillance Area, but Weather on the HFSWR Site Is Calm  
In Figure 10 a satellite image of the bad weather over one part of the IoT OTH maritime surveillance area is presented.  
As it can be seen from Figure 10, the storm does not cover the coast itself, but \"only\" a large part of the HFSWR coverage area. Under these weather conditions, the range of HFSWR decreases due to the harsh surface wave propagation conditions [32]. This directly causes decline in the HFSWR vessel detection capabilities. In Figure 11 the detection capabilities of one HFSWR under the conditions described above are shown.  
As it can be seen in Figure 11, the range of HFSWR is practically limited to barely 150 km. On the other hand, since the weather conditions at the HFSWR sites are favorable for satellite communications, there are no significant differences in data delays in comparison to the Case A. In other words, from the satellite communication perspective this case is very similar if not identical to the previous one. Henceforth, the analysis done in the Case A is valid here as well.  
Figure 11: HFSWR detection capabilities during bad weather conditions over part of the HFSWR surveillance.  
Figure 10: Bad weather conditions over part of the HFSWR surveillance area.
## 4 Experimental Setup
### Metrics Used for Evaluation  
Prior to applying a number of different machine learning algorithms, the raw network flows were handled to obtain the sketchy feature vectors, according to the procedure described in the previous sections. The classification quality metrics were calculated according to the following procedure:  
1. communication flows were aggregated into time windows (in this case, 3-min time windows were used).
2. for the given time windows, sketchy feature vectors were calculated.
3. within the ground-truth communication flows, labels were examined against those predicted; subsequently the TP, TN, FP and FN errors (true and false positives and negatives) were measured.
4. lastly, recall, precision, and F1-score metrics were calculated and reported.
## 3 Opportunistic Transmission Scheme for Integrated UAV-ICV Networks Enabled by Node Cooperation
### UAV-Assisted Information Transmission
In practical integrated UAV-ICV networks, the cache size of nodes is limited. Once the cache is full, the node is unable to store newly received information and mst drop these message copies, which reduces the delivery ratio of integrated UAV-ICV networks. Therefore, the selected UAV nodes must not only have high centrality and sufficient energy but also consider their remaining cache size. It is assumed that the initial cache space of UAV and ICV is \(B_{\text{init\_UAV}}\) and \(B_{\text{init\_veh}}\), respectively. In addition, the size of each message copy is \(\alpha\). The number of message copies currently cached by the UAV and ICV is \(\beta_{\text{UAV}}\) and \(\beta_{\text{veh}}\), respectively. According to the above-mentioned analysis, for the \(l\)-th transmission, the remaining cache size of the UAV and ICV can be calculated by (17) and (18), respectively. \[B_{\text{rest\_UAV}}(l)=B_{\text{rest\_UAV}}(l-1)-\alpha\times\beta_{\text{UAV} }(l), \tag{17}\]  
and  
\[B_{\text{rest\_veh}}(l)=B_{\text{rest\_veh}}(l-1)-\alpha\times\beta_{\text{veh }}(l), \tag{18}\]  
where \(B_{\text{rest\_UAV}}\) and \(B_{\text{rest\_veh}}\) are the remaining cache sizes of UAVs and ICVs, respectively. We have \(B_{\text{rest\_UAV}}(0)=B_{\text{init\_UAV}}\) and \(B_{\text{rest\_veh}}(0)=B_{\text{init\_veh}}\). Therefore, the selected UAV nodes for transmission should consider the node centrality, remaining energy, and remaining cache. The selection criterion is  
\[\Phi =\text{lg}\Big{(}a\times D_{\text{Centrality}}+10\Big{)}\times \text{lg}(b\times E_{\text{rest\_UAV}}(l)+10) \tag{19}\] \[\times\text{lg}(c\times B_{\text{rest\_UAV}}(l)+10),\]  
where \(a\), \(b\), and \(c\) are the tuned parameters. In (19), logarithmic operations are performed to eliminate heteroscedasticity of tuned parameters. Additionally, the higher the value of \(\Phi\), the better the forwarding performance to that node. A diagram of UAV-assisted information transmission is shown in Figure 3.
## 2 Overview of SDN Architecture
### SDN Southbound Interface  
OpenFlow is so far the most popular standard for SDN southbound (SB) interface. Earlier implementations of SB interface were forwarding and control elements (ForCES) and protocol-oblivious forwarding (POF) [2]. ForCES uses logical function blocks (LFB) in the data forwarding elements to provide networking functionalities such as IP routing [3]. However, these standards rely essentially on modifying forwarding devices to support flow tables [2], in a way that can be dynamically configured by remote entities through operations such as, adding, removing, or updating flow rules in the flow table. Therefore, a recent initiative such as OpenFlow emerges,  
Figure 1: Software defined networking architecture.  
which does not require modifications of the switches to support flowtable. This attracts not only the research community but also the networking industry [2]. OpenFlow became the most popular and powerful implementation of the SB interface [22], standardized by Open Network Foundation [23] to control the behavior of the SDN devices. [24] developed OpenState as a superset extension of Openflow aimed at offloading some of the control logic to switches thereby shifting the pragmatic approach from stateless to stateful operation. This means the OpenFlow switches can also be directly programmable, thus they can handle forwarding rules locally without the need to completely depend on the remote controller. OpenState is yet to be implemented as the future extended version of OpenFlow [25]. In OpenState, the legacy flow table in OpenFlow is preceded by a state table to processed flow states, by using flow-states and global states. A programmer can define flow entries that apply to different scenarios, using state transition, a programmer can control the evolvement of different scenarios. This survey focuses on OpenFlow as the standard southbound interface between switch to SDN Controller.
# A Systematic Overview of Recent Methods for Non-Contact Chronic Wound Analysis
## 6 3D Wound Reconstruction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Paper** & **Preprocessing/Segmentation** & **Classification** & **Image Database** & \begin{tabular}{c} **Performance** \\ **Metrics** \\ \end{tabular} & \begin{tabular}{c} **Time (s)** \\ **Avg/Min/Max** \\ \end{tabular} \\ \hline [PERSON]. [PERSON] et al., [55] & KPN color mode(Mean shift & K-means algorithm & 32 images & MCC value 0.68 & 6/- \\ \hline [PERSON]. [PERSON] et al., [34] & RGB compression 4 times, noise removal(Mean shift, largest connected component & K-means & 64 images & MMC 0.736 & 15/- \\ \hline [PERSON] et al., [88] & Color correction, median filter, color homogenization, YRDD—Db and Dr channel(K-means, Fuzzy C-means & 50 images & \begin{tabular}{c} Accuracy Fuzzy \\ C-means 90.25\%, \\ K-means 93.45\%, \\ Bayesian classifier \\ \end{tabular} & 120/- \\ \hline [PERSON] et al. [40] 2015. & \begin{tabular}{c} Median filter, color space transformations/K-means \\ \end{tabular} & SVM, NN, RF & 113 images & \begin{tabular}{c} Accuracy SVM \\ 88.08\%, NN 81.87\%, \\ RF 87.35\% \\ \end{tabular} & 69.5/- \\ \hline [PERSON] et al., [3] & Noise removal, H\(\overline{\text{S}}\)–H \\ 2014. & \
\begin{tabular}{c} Channel(Fuzzy divergence thresholding \\ \end{tabular} & SVM, Bayesian classifier & 74 images &
\begin{tabular}{c} Accuracy Bayes classifier \\ (Medetec) \\ \end{tabular} & N/A \\ \hline [PERSON] et al., [60] 2013. & Median filter/K-means & MLP neural network & 113 images & Accuracy 86\% & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cont. then the surface of each type of tissue can be computed by summing the triangles belonging to the same class. Final classification results were better when merging two or more classification images from different points of view.