esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancements and Challenges in CoAP--A Survey
## 4 Enhancements in CoAP
### Congestion Control Mechanisms in CoAP
#### 4.1.12 Genetic CoCoA++  
Another congestion control algorithm based on CoCoA++ proposed by [PERSON] et al. in [28], in addition to problem of large changes in RTO estimates in CoCoA+, also mentions that congestion control algorithms that save previous states are not affordable for constrained IoT devices due to memory limitations. Therefore, they propose that employs CDG and Genetic Algorithm (GA) for RTO calculation for a fixed interval of time. The method uses RTT(min) instead of RTT(max) for calculationto gain better results. To identify the congestion in the network, difference between current and previous RTT(min) is used. RTT(min) is tracked for 5 s and this observation period, minimum value of RTT is selected from all the observed ones. After the selection, crossover is performed which is done considering the previous and new value of minimum RTT (\(RTT_{min}\)) and then the PBF is calculated according to CDG obtained from difference of previous and new RTT(min). Finally, it calculates RTO and continues the same process. Although it solves some issues of congestion control of CoAP and CoCoA+, but the 5 s observation time might not be enough for the case of underwater and mobile communication. Moreover, the proposed scheme does not consider burst traffic.
## 6 Application of Algorithm to Various Scenarios for Fault Identification: Case Studies
### Effect of Fault Location
#### 6.1.2 Fault on Bus 634  
All types of faults were simulated on node 634. This node is considered because a transformer (XFM) is available between nodes 633 and 634. Node 634 is operated at a voltage of 0.48 kV and node 633 is operated at a voltage of 4.16 kV. The results for the phase-A to ground fault are detailed in Figure 10. The WD-index corresponding to phase-A has a high magnitude at the moment of fault incidence, as depicted in Figure 10b which shows that the magnitude of this index corresponding to phases-B & C is low. Figure 10c illustrates that the ALN-index has a high magnitude corresponding to all the phases due to fault incidence. Figure 10d illustrates that the fault index corresponding to faulty phase (phase-A) is high compared to the threshold, whereas this FI corresponding to the healthy phases (phase-B & C) has a low magnitude compared to the threshold. Hence, the algorithm effectively detects the AG fault incidence on node 634 and also identifies the nature of the phases. This also established that the algorithm is effective for the identification and classification of faults when transformers and lines are available between the fault location point and the point at which the protection scheme is established.
# Congestion Control and Traffic Differentiation for Heterogeneous 6 TiSCH Networks in IIoT
## 4 Multi-Queue Model and Priority-Based Transmission
### The Multi-Queue Transmission Model
The absolute deadline \(D_{i}\) of an arbitrary packet PK\({}_{i}\) equals to its arrival time \(t_{i}\) plus the relative deadline \(d_{i}\), i.e., \(D_{i}=t_{i}+d_{i}\). In this context, the relative deadline of a packet is assigned according to the considered application and the information carried in the corresponding packet, e.g., fire alarm, excessive pressure in a pipe, leakage of gas, etc. If Q\({}_{1}\) is empty, the scheduler selects a packet from Q\({}_{2}\) to be transmitted first according to the EDF approach. If both Q\({}_{1}\) and Q\({}_{2}\) are empty, the packets from Q\({}_{3}\) are transmitted according to the FIFO policy. Therefore, the packet delay and queuing time are mainly influenced by the number of higher priority packets in the system, as will be illustrated in the next section. The packet category can be embedded in the packet header as a 2 bit field, i.e., 00, 01, and 10 for T\({}_{1}\), T\({}_{2}\), and T\({}_{3}\), respectively, which can be extracted and decoded by the classifier to place the packet in its corresponding queue. This way, the proposed multi-queue model always ensures a prioritized transmission for the critical data over the non-critical compared to the conventional single-queue model. Another important advantage of the proposed multi-queue scheme is that higher priority data avoid the congestion problem. Traffic types T\({}_{1}\) and T\({}_{2}\) occur occasionally; hence, Q\({}_{1}\) and Q\({}_{2}\) are likely less prone to  
Figure 5: The proposed multi-queue model: (**a**) sub-tree of the Destination-Oriented Acyclic Graph (DODAG); (**b**) multi-queue model of node \(B\). congestion problems at parent nodes, i.e., queue overflow and packets loss. Further, this guarantees reliable and real-time communications of critical data even under the heavy traffic load of T\({}_{3}\).
## 4 Experimental Results and Analysis
### Experimental Setup  
In this part, we evaluate the performance of our CASD assisted PCSSR algorithm on all datasets, and its performance on group I datasets will be compared to other traditional graph-based classification methods stated in [17], including the original PCSSR graph method, the Gaussian kernel (GK) graph method, the nonnegative local linear reconstruction (LLR) graph method, the local linear embedding (LLE) graph method, the nonnegative low-rank and sparse (NNLRS) graph method, and the SR graph method. Our CASD assisted PCSSR approach is implemented under the same label propagation framework as other models, and the hyperparameters from other models stay the same as [17]. The process of hyper-parameter determination during our model development will be stated in Section 4.4.  
We separate every dataset into two parts, i.e., the training set and the testing set. In our case, the latter is much larger than the former. For each dataset, we randomly pick out 3/5/10/15/20 samples per class as the training set (the labeled samples), and the rest as the testing set (the unlabeled samples). An example of dividing IND PINE dataset is illustrated by Figure 5. To accord with [17], we run our algorithm 20 times for each dataset. The mean of overall accuracy (OA), average accuracy (AA), and the Kappa coefficient are utilized to evaluate the classification results.
###### Abstract  
A robust three-perspective digital image correlation (DIC) system based on a cross dichroic prism and single three charge-coupled device (3 CCD) color cameras is proposed in this study. Images from three different perspectives are captured by a 3 CCD camera using the cross dichroic prism and two planar mirrors. These images are then separated by different CCD channels to perform correlation calculation with an existing multi-camera DIC algorithm. The proposed system is considerably more compact than the conventional multi-camera DIC system. In addition, the proposed system has no loss of spatial resolution compared with the traditional single-camera DIC system. The principle and experimental setup of the proposed system is described in detail, and a series of tests is performed to validate the system. Experimental results show that the proposed system performs well in displacement, morphology, and strain measurement.  
Keywords:digital image correlation; multi-perspective; single camera; cross dichroic prism +
Footnote †: journal: Applied Physics Letters
## 2 Data and Methods
### Spatial Interpolation Methods  
The Kriging interpolation method is used for spatial analysis. The Kriging method is the basis for the creation of geostatistics and is an effective tool for investigating phenomena related to spatial correlation in nature. The weight of each sample is calculated as a weighted average of the attributes of the samples to be measured.  
There are many types of kriging interpolation methods, and ordinary kriging interpolation is one of the most common and commonly used methods, with the formula  
\[\hat{Z}_{(x_{0})}=\sum\limits_{i=1}^{n}\lambda_{i}Z_{(x_{i})} \tag{2}\]  
where \(\hat{Z}_{(x_{0})}\) is the predicted value. \(Z_{(x_{i})}\) is the sampled value, determined by the semi-variance function model. \(\lambda_{i}\) is the weight to be assigned to each unsampled location.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 2 V2X Technology  
According to the 3 GPP, the term V2X implies to communication between different entities based on Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Network (V2N), and Vehicle-to-Pedestrian (V2P) communications, etc. for both safety and non-safety applications [4]. V2X allows vehicular nodes to obtain a wide variety of traffic data in real time that greatly enhances driving security, traffic quality, and infotainment facilities. As the vehicles become more advanced and autonomous, a large number of sensors, and diverse communication devices and technologies will be used, which will generate a huge number of data, such as cooperative sensing data, communications, infotainment, sensing data, etc. The complexity of vehicular applications will further increase, as the density of the vehicles increase and existing V2X technology cannot meet the ever-growing requirements in the future. Efficient NR access technology, core network capabilities, and edge-cloud services are needed to meet V2X requirements. Kudos goes to the proven history, reliability, and development of cellular technology in telecommunications, cellular technology may resolve the current issues and requirements of V2X communications in vehicular networks.  
The 3 GPP released Cellular V2X (C-V2X) specification in Rel.14 and Rel.15. The 3 GPP introduced the 5G NR technology focusing on further enhancement of the cellular V2X networking infrastructure in Rel.16 [5]. The new 5G NR will be a game changer, because it can supplement or substitute 802.11p in V2X communication. Based on C-V2X, connectivity in vehicular networks can be obtained in the following communication modes:  
1. V2I communication mode: in V2I communications, vehicular nodes communicate with the RSUs within communication range. The RSU can be incorporated in either an eNodeB or a stand-alone traffic signal post.
2. V2N communication mode: in V2N mode, vehicular nodes communicate with cellular infrastructures (e.g., eNB), evolved packet switching, remote servers providing extended  
Figure 1: Structure of the survey paper.  
communication between the vehicles, and cloud-based services through cellular networks. V2N allows to broadcast and unicast communications between vehicular nodes and V2X management systems.
* V2V communication mode: the vehicular nodes communicate with each other at close proximity in an ad-hoc domain, without infrastructures, such as RSUs.
* V2P communication mode: in V2P mode, vehicles connect with pedestrians or bikers on the road to prevent accidents.
## 8 Discussion
### Motivations  
The future 6G network will not only concentrate on the pure communication area, but it will also be interoperable between diverse but related fields like electronics and materials, wireless communication, computer science and engineering, and computer science and technology [399]. Nanoelectronics for the Internet of Things, RF modules and packaging, high-frequency materials, radio transceivers, energy harvesting, THz imaging, and 2D/3D imaging will all be covered in the topic of electronics and materials. While in the field of computer science and engineering, the professionals can make contributions in a variety of areas such as image and signal analysis, mobile apps, security and privacy, big data analysis, smart sensor analytics, smart environments, and ubiquitous systems to name a few. In addition to their work in the field of wireless communication, experts in RF and antennas, 5G baseband technology, Internet of Things applications, future radio access, network optimization and management, spectrum regulations, and channel modelling are also actively involved in a variety of other fields (see Figure 8).
## 3 Materials and Methods
### Pollutants Removal
#### 3.2.3 Geosmin and MIB Degradation  
ZnO-based photocatalysts were also tested on aqueous solutions of geosmin and MIB (C = 5 \(\upmu\)g/L of each compound) following the same procedure described in the previous paragraph. A headspace solid phase micro extraction (HS-SPME) method combined with gas chromatography-mass spectrometry (GC-MS) was developed for the analysis. A medium polar SPME fiber Divinylbenzene/Carboxen/Polymethylsiloxane (DVB/CAR/PDMS) assembled on a SPME holder was used and extraction conditions were fine-tuned. The optimized method involved the use of 10 mL of sample inserted in a proper vial with 3 g of NaCl added to favor the salting out. The vial was then capped with a screw cap with a silicone/PTFE septum, stirred at 300 rpm and heated in a thermostatic bath at 40 \({}^{\circ}\)C for 10 min (equilibration time). Then, the SPME fiber was pierced through the septum and exposed in the headspace of the vial for 25 min.  
Fiber exposure time and temperature were optimized: fiber exposure times in the headspace of 10, 15, 20, 25 and 30 min were evaluated by observing the variation of the peak signal of a standard solution of geosmin and 2-MIB according to exposure time. The best signal was obtained after 25 min and it remained stable at longer times. The sample temperature was varied from 20 to 80 \({}^{\circ}\)C and the optimum value was found to be 40 \({}^{\circ}\)C.  
Finally, the fiber was inserted into the GC-MS injector and the analytes were thermally desorbed. The GC-MS system consisted of an Agilent 8860 GC System, an Agilent 5190-4048 Ultra Inert liner Straight injector, a SPME taper 0.75 mm, an Agilent J&W GC Columns 30 m \(\times\) 250 \(\upmu\)m \(\times\) 025 \(\upmu\)m and an Agilent 5977B GC/MSD GC Mass Detector. The analyses were conducted setting the He flow at 1.5 mL/min, the liner temperature at 270 \({}^{\circ}\)C, the MS transfer line temperature at 300 \({}^{\circ}\)C, the MS source temperature at 300 \({}^{\circ}\)C and the quadrupole temperature at 150 \({}^{\circ}\)C. The injection was performed through the pulse-splitless method, which injects the sample through a pressure pulse into the liner chamber with a set pressure of 30 psi, and a purge-flow split vent of 40 mL/min to clean the chamber after injection.  
The chromatographic run consisted of a starting temperature of 333 K maintained for 1 min, then the temperature was increased by 10 \({}^{\circ}\)C/min until 523 K for a total GC runtime of 20 min. The retention times were 7.00 min for 2-MIB and 10.53 min for geosmin. The analysis was performed in Single Ion Monitoring (SIM) mode and target m/z ratios 112.0 and 95.1 were used for quantification of geosmin and 2-MIB, respectively. The ratios of 111.0, 125.0 and 182.0 m/z were used for analyte confirmation of geosmin while 108.1 and 135.1 were utilized as qualifier for 2-MIB. Quantification of peak areas was performed using the Agilent MassHunter Quantitative Analysis program.
## 2 Task Allocation and Dispatching
### Stable Marriage Problem
```
Input :Suitors, Reviewers Output :Assignment // Iterate suitors until all are matched \(\mathit{UnassignedSuitors}\gets\mathit{Suitors}\) for\(\mathit{thisSuitor}\leftarrow\mathrm{popfront}(\mathit{UnassignedSuitors})\)do // Check reviewers in order of decreasing preference for\(\mathit{reviewer}\leftarrow\mathrm{popfront}(\mathit{preferences}(\mathit{thisSuitor}))\)do \(\mathit{prevSuitor}\leftarrow\mathrm{Assignment}(\mathit{reviewer})\) if\(\exists\mathit{prevSuitor}\)then \(\mathit{thisPreference}\leftarrow\mathrm{preference}(\mathit{reviewer},\mathit{thisSuitor})\) \(\mathit{prevPreference}\leftarrow\mathrm{preference}(\mathit{reviewer}, \mathit{prevSuitor})\) if\(\mathit{prevPreference}\geq\mathit{thisPreference}\)then // Previous assignment is more preferable, // skip to next reviewer continue  end if // New assignment is more preferable, // re-enqueue previous suitor  pushback(\(\mathit{UnassignedSuitors},\mathit{prevSuitor}\))  end if // Assign match tentatively, // proceed with next suitor  Assignment(\(\mathit{reviewer}\)) \(\leftarrow\mathit{thisSuitor}\) break  end if  end for
```  
**Algorithm 1**Gale-Shapley Algorithm  
As [PERSON] and [PERSON] and [PERSON] (2018) describe, unassigned vehicles are sent to a designated parking point, where they will wait for new assignments. The number and location of these points will have an impact on the performance of the fleet. It makes sense to distribute the vehicles at a central location, or (if those are predictable) in the vicinity of the pick-up positions of incoming move requests.
# JANNet: A U-Shape Network with Jump Attention for Semantic Segmentation of Road Scenes
## 3 Results
### Summary of Segmentation Results  
Based on the semantic segmentation results and objective evaluation metrics in the previous section, we can find that the basic FCN network performs similarly in both datasets with good objective evaluation metrics, but the actual visual segmentation is poor. FCN can only recognize the target but cannot distinguish the outline. SegNet, on the other hand, can recognize the edges of objects to a certain extent, but there are often cases of mis-segmentation and burrs on the segmented edges. The edge segmentation of UNet is smoother, but mis-segmentation occurs in the background part. Finally, the JAUNet network structure designed in this paper shows that there are differences in the performance of the network in the two datasets. The performance in the CamVid dataset is better, as the object edges can be extracted well, there are few mis-segmentations in the background part, and the objective evaluation index of the test set is also the highest. However, its substantial  
Figure 14: Segmentation results of each network on the Cityscapes dataset.  
number of parameters and slower inference speed remain weaknesses in autonomous driving scenarios, which will be the direction of our future research. It can also be found in the segmentation results of each group that JAUNet is more sensitive to the recognition of small targets, which is in line with our expectations for JAM. It should be noted that the final upsampling of JAM compresses the number of channels to the number of categories (19 for Cityscapes and 11 for CamVid), resulting in a slight difference in the number of parameters between the Cityscapes and CamVid datasets.
# Reputation-Based Trust Approaches in Named Data Networking
## 5 Discussion
### Future Directions in NDN
Aiming at mitigating BOAs in NDN, reputation values can be exploited as condition for caching in order to accomplish oscillation avoidance and hence, weaken the attacker. **Secure Routing:** Reputation can also be useful for routers, enabling them to make appropriate forwarding decisions. Routers can assign ratings to their adjacent routers and choose to which they should forward Interests based on their neighbors' trustworthiness. In a decentralized framework, reputation values should be synchronized. This requires developing a related exchange protocol which introduces undesirable communication overhead. In order to minimize this overhead, reputation values can be piggybacked in routing messages [12]. This direction has only been substantially explored in wireless networks but appears to be a promising feature for NDN routing protocols [41], as well. **Content/Service quality evaluation:** Besides complementing NDN's credential-based methods to assuring that invalid content is discarded from the network, reputation can provide a second layer of trust in NDN. Similar to ratings in P2P networks, reputation values can allow consumers to reason about content quality, instead of simply certifying its authenticity and integrity. Activating this functionality can possibly be useful to address another challenge, as well, which is creating client profiles based on end-users' preferences and making those profiles available to content providers (e.g., Netflix) [42; 43]. NDN enables bringing reputation directly on top of the network layer, possibly utilizing cross-layer information, a direction which has not yet been explored. An extension of this direction could be further explored for Service-Centric Networking approaches [44; 45; 46], the reputation being based on service quality, availability and other metrics.
## 3 Results and Discussion
### Physiological-Biochemical Profile of the Selected Isolates  
After characterizing the isolates by applying morphological and molecular-genetic methods, 13 isolates were selected and subjected to biochemical profiling with the API 50 CHL system (BioMerieux(r) SA, France) for the identification of LAB based on the ability of each isolate to consume the 49 carbon sources included in the kit.  
Figure 1: Cell morphology of the isolates grown on MRS-agar (capital letters) vs MRS-broth (small letters). Isolates Ech1 (**A,a**), Ech3 (**B,b**), Ph1 (**C,c**), and Ph3 (**D,d**). Methylene blue staining, 1000 \(\times\) magnification.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Genotype (RAPD)** & \multicolumn{6}{c}{**Flour Type/Isolate**} \\  & **Khorasan** & **Buckwheat** & **Spelled** & **Barley** & **Wheat** & **Spelt** & **Corn** \\ \hline  & & & Cn1 * & Ech1 * & & L1 * & \\  & & & Cn2 & Ech2 & & & \\
1 & & & Cn3 & Ech4 & & & \\  & & & Cn4 & Ech6 & & & \\  & & & Cn5 & Ech7 & & & \\  & & & Cn6 & & & \\ \hline
2 & X4 * & & & Ech3 * & & & \\ \hline
3 & X2 * & & & & & \\ \hline
4 & & & & & & L2 * & \\ \hline
5 & & & & Ph1 * & Car * \\ \hline
6 & & El1 * & & & & \\ \hline
7 & & & & Ph2 * & & \\  & & & & Ph4 & & \\ \hline
8 & & & Ph3 * & & & \\ \hline
9 & & & & Ph5 * & & \\ \hline \end{tabular}  
* Isolates selected for further work on the species identification (isolates were selected so that each genotype was presented and each type of flour had at least one representative culture).  
\end{table}
Table 2: Distribution of the obtained rod-shaped isolates by genotype (RAPD) and origin (flour type).  
According to the set of digestible carbohydrates, the LAB isolates were divided into two groups: the group of _Lactiplantiobacillus plantarum_ 1 and that of _Levilactobacillus brevis_ 3 (Table 3). The isolates in the _Lactiplantiobacillus plantarum_ 1 group showed a much wider range of digestible carbohydrates. This somewhat corresponded to the more diverse biochemical pathways for the carbohydrate assimilation characteristic of facultatively heterofermentative LAB bacteria (such as the _Lactiplantiobacillus plantarum_ species) compared to obligate heterofermentative LAB bacteria (such as the _Levilactobacillus brevis_ species) or homofermentative lactobacillus. A distinctive feature of all isolates identified by the system as _Levilactobacillus brevis_ 3 was the utilization of D-xylose. The most discriminative carbohydrate sources and the biochemical profile of the studied cultures are presented in the Supplementary file (Table S2).