esa-sceva/satcom-chunk-collection huggingface dataset:
# Routing Strategies for Isochronal-Evolution Random Matching Network
## 3 Path Planning and Routing Decision-Making in the IERMN
### Routing Decision-Making  
In the process of packet transmission, routing decision-making refers to the selection of routing actions by predecessor vertices. There are two routing actions taken by a predecessor vertex for the packets in its queue: send or hold. Holding a packet means that its sending is delayed, as mentioned in Section 3.2. There are two disciplines for each node:  
1. First-in-first-out (FIFO). The packet that is created or received by a vertex is placed at the end of its queue. The vertex always chooses to send the packet that is at the head of the queue.
2. Path iteration avoidance (PIA). Any edge or vertex cannot be visited more than twice by the same packet.  
A predecessor vertex makes a routing decision for a packet according to its SVF, DVF, SVVF, and FFF, as mentioned in Section 2.2. The SVVF is a set of all vertices that the packet has visited from its generation to the current slot. The FFF is the path used to transmit the packet in the future. It is assumed that a packet is generated in slot \(s\), and the initial value of its FFF is \(P_{SVF,\ DVF,\ s}\).  
The routing decision-making algorithm used for the predecessor vertices is shown in Algorithm 1. In Algorithm 1, \(Q_{i}\) denotes the queue of \(v_{i}\); \(|Q_{i}|\) is the length of \(Q_{i}\); \(Q_{i}[r]\) is the \(r\)-th packet in \(Q_{i}\); and \(Q_{i}[r]\). \(SV\), \(Q_{i}[r]\). \(DV\), \(Q_{i}[r]\). \(SVV\), and \(Q_{i}[r]\). \(FP\) denote the SVF, DVF, SVVF, and FFF of \(Q_{i}[r]\) respectively.  
The main steps of Algorithm 1 are presented Lines 3-6; this process is known as re-planning. A packet may not be sent in the slots predetermined by its FFF in the limits of FIFO, PIA, and the delivery capability of the vertex. If the current slot is later than the predetermined slot but the packet has not been sent, its FFF becomes invalid immediately. In this case, the vertex at which the packet is located must update the FFF of this packet by replanning. The new FFF is the path whose source vertex is the current vertex, the destination vertex is its DVF, and the planning slot is the current slot.  
Figure 1: Comparison of two paths. The blue arrows indicate the paths in which vertices are forbidden from delaying the sending of packets. The red arrows indicate the paths in which vertices are permitted to delay the sending of packets.  
```
Input: Queue of \(v_{i}\) and current slot \(k\) Output: The routing action taken by \(v_{i}\) in slot \(k\)
1.\(c=0\)
2.for\(r=1\)to\(|Q_{i}|\)do
3.if\(o(Q_{i}[r].\)\(FPF)<k\)then
4. Obtain \(P_{Q_{i}[r].SVF,\ Q_{i}[r].DVF,\ k}\) using a path planning algorithm
5.\(Q_{i}[r].FPF=P_{Q_{i}[r].SVF,\ Q_{i}[r].DVF,\ k}\)
6.endif
7.if\(c<C_{\rho}\)and \(v_{x_{k}}\
ot\in Q_{i}[r].\)\(SVVF\)and \((v_{i},e_{i,x_{k}}^{[k]},v_{x_{k}})\subset Q_{i}[r].\)\(FPF\)then
8.\(v_{i}\) sends \(Q_{i}[r]\) to \(v_{x_{k}}\)
9.\(c\gets c+1\)
10.endif
11.endfor
```  
**Algorithm 1**Routing decision-making algorithm for a vertex in an IERMN
# On the Alkalinity of Solid Catalysts for Transesterification of Dimethyl Carbonate and Ethanol
###### Abstract  
In this study, Mg-Al-Zn, MgO, Al\({}_{2}\)O\({}_{3}\), and ZnO were synthesized via the co-precipitation method and evaluated as catalysts for the transesterification reaction of dimethyl carbonate (DMC) and ethanol. The crystal structure, morphological characteristics, pore structure properties, and alkaline properties of the catalysts were analyzed by X-ray diffraction (XRD), scanning electron microscopy (SEM), Brunauer-Emmett-Teller (BET) surface area analysis, temperature-programmed desorption of CO\({}_{2}\) (CO\({}_{2}\)-TPD), and Fourier transform infrared spectroscopy (FTIR). The surface alkali strength and alkalinity of the solids were determined using the Hammett indicator method and non-aqueous titration. When Al\({}_{2}\)O\({}_{3}\) and ZnO are used as catalysts for this transesterification, the conversion rate of dimethyl carbonate is relatively low. When MgO and Mg-Al-Zn are used as catalysts, the conversion rate of dimethyl carbonate is higher. This indicates that the alkali strength of the catalyst for the transesterification reaction needs to be greater than 9.3. Additionally, the activity of the catalysts is also related to the amount of the alkaline sites on the solid surface. The alkali strength of MgO is greater than 11; its excessively high alkali strength will react with CO\({}_{2}\) and H\({}_{2}\)O during use, resulting in a reduction in the number of alkaline sites and thus showing unsatisfactory reactivity. The alkaline strength of the Mg-Al-Zn catalyst ranges from 9.3 to 11.0. When used for the first time, the number of alkaline sites decreases, and then the alkalinity remains at a certain value. Therefore, the alkaline strength of the solid catalyst for the transesterification reaction between DMC and ethanol needs to be between 9.3 and 11.0 so that the number of alkaline sites on the catalyst surface remains unchanged and the catalytic activity remains stable.  
alkinity; transesterification; ethanol; dimethyl carbonate; solid catalyst +
Footnote †: journal: Applied sciences
# An Observer-Based Current Sensor-Less Control Scheme for Grid-Following Converters
## 3 Conventional Control of Grid-Following Inverters  
The two main control purposes of a GFL-inverter are (i) delivery of the produced active power by the primary source to the grid and the exchange of the desired reactive power to the grid, according to the grid-codes, (ii) control of the DC-link voltage in the desired reference value. Both of the requirements should be met with a proper response time and stability margins.  
According to (7), it is general practice to control the \(P_{out}\) and \(Q_{out}\) with \(i_{d}\) and \(i_{q}\), respectively. In addition, according to (6), control of DC-link voltage at the predetermined value drives \(P_{out}\) to \(P_{dc}\); and, accordingly, the desired \(P_{out}\) is delivered to the grid. Therefore, the control goals would be control of the reactive power and DC-link voltage, which are done via control of \(i_{d}\) and \(i_{q}\). In this cascade control structure, the current control loops of \(i_{d}\) and \(i_{q}\) are inner loops, and the reactive power and DC-link voltage loops are outer control loops.
# Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance
## 3 Methodology  
The methodology starts with data collection. The system model for video anomaly detection using MAML from video surveillance is illustrated in Figure 1, where frames were extracted and then preprocessed.  
After preprocessing, feature extraction was done using swin transformer. The extracted features as feature maps were fed to the anomaly detector model, which used MAML to classify the dataset into normal and anomalous.
## 3 Results and Discussion
### Performance Comparison
#### 3.1.1 Comparison with [PERSON] et al.  
We compared our proposed study with [18], which employs deep learning involving back-propagation (network containing three layers and 30 neurons) and stacked autoencoder networks. The network structure was not clearly articulated in their study [18], i.e. without any pixel, resolution and dimension-specific information about the used image samples. Moreover, the visual evidence of detecting pores and fractures using this strategy [18] was not presented to corroborate their claim of accuracy, which certainly reduces its acceptability to the larger image-processing community. The induced strategy of [18] was to extract the shapes of pores and fractures from the image samples using 'Image Pro Plus' software and then use these extracted shapes as a training set to identify the same in the testing of high-resolution (with good chrominance) image samples containing a single  
Figure 5: (**a**). Regions encircled in yellow are identified as pores, whereas the regions encircled with white circles are recognized as fractures. (**b**). Enlargement of one pore and its detection policy. These samples have undergone Gaussian smoothing. and hence the difference is to the naked eye.  
large pore or a single large fracture. The used images have good chrominance values, as the pores and fractures are marked in blue in their study. Additionally, the pores and fractures in training and testing data used in [18] are visually large and clearly recognisable to the human eye, which makes the detection mechanism relatively simple. Hence, we suspect that the identification mechanism of [18] will fail in the presence of the few low-quality grayscale images containing micro-pores and fractures in our dataset. Several reasons for this failure are possible: (i) an inadequate number of training samples for learning the necessary features to understand the shapes of pores and fractures using deep learning and (ii) extracting shape-related information from micro-pores and fractures is a tough challenge for the strategy [18].  
In contrast, our proposed pixel-wise \(k\)-IN does not require a large image set for accurate identification. The strategy presented in [18] is mostly not applicable in the current dataset (see Section 2.3) because of its adopted learning style that requires a single pore or a fracture for training and a single pore or a fracture for testing.
# Type Hierarchy Enhanced Event Detection without Triggers
## 4 Experiments and Evaluation  
### Dataset and Baselines  
In this paper, we conduct extensive experiments on the ACE2005 dataset [43], which contains 599 English documents and defines 33 event types. We split the dataset following the previous event extraction work [1,8,22]. To evaluate our model, we adopt precision (P), recall (R) and F1 scores, among which F1 is the most comprehensive measurement.  
\[\text{P}=\frac{\text{TP}}{\text{TP}+\text{FP}}\times 100\% \tag{20}\]  
\[\text{R}=\frac{\text{TP}}{\text{TP}+\text{FN}}\times 100\% \tag{21}\]  
\[\text{F1}=\frac{2\times\text{P}\times\text{R}}{\text{P}+\text{R}}\times 100\% \tag{22}\]  
where TP, FP and FN denote true positives, false positives and false negatives, respectively.  
It is important to emphasize that our framework finishes event detection without triggers. Therefore, we remove trigger annotations from the corpus. Finally, each sentence is assigned only a set of labels based on event type annotations. If a sentence does not contain a defined event, we will assign the label NA. Table 3 shows examples of annotated sentences. It is noted that if a sentence contains the same event, our model still treats it as multiple events.  
In this paper, to evaluate the performance of our proposed THEED framework, we compared our model with other advanced methods in the ACE2005 dataset.  
* **DMCNN**[8], extracting word-level and sentence-level features from text automatically using dynamic multipooling layers.
* **JRNN**[23], a joint framework of bidirectional recurrent neural networks, is used for event extraction.
* **TBNNAM**[10], which encodes the representation of sentences according to the target event type without triggers.
* **Text2 Event**[32] is a sequence-to-structure model that directly learns from text annotation and requires no trigger offsets.
* **DEEB-RNN3**[25], which uses chapter-level embedding representation as a reinforcement for sentence-level event detection.
* **OntoED**[29], a novel framework for event detection with the help of ontology embedding, describing event detection as the process of ontology filling.
* **Query and Extract**[9], which refines event extraction as a query-and-extract paradigm and leverages rich semantics of event types.
# Novel Extensions to Enhance Scalability and Reliability of the IEEE 802.15.4-DSME Protocol
## 6 Implementation of D-DSME on Real Devices  
An implementation of the proposed D-DSME extensions was developed on boards equipped with the PIC24 FJ256 GB108 microcontroller and the MRF24J40 MA transceiver, both produced by Microchip Tech. The communication between the transceiver and the microcontroller takes place via the Serial Peripheral Interface (SPI).  
The implementation does not require any hardware modifications. The D-DSME extensions were implemented from scratch and the classic IEEE 802.15.4-2006 MAC layer, implemented in the transceiver, was disabled. The considered scenario is a network with a tree topology that includes one _PAN-Coordinator_, one coordinator, and three end nodes. All the end nodes generate two flows, \(f_{1}\) and \(f_{2}\), with period equal to \(2T_{SF}\). The messages (with a 8-bytes payload) are transmitted to the coordinator that sends them to the _PAN-Coordinator_. The end-to-end delays are acquired using the GPIOs integrated in the MCUs of the source and destination nodes. The configuration parameters are the same as the ones in Table 2. Table 4 shows that the measured end-to-end delays are similar to the ones obtained in simulation. The time differences, which are always between 1409 \(\upmu\)s and 1949 \(\upmu\)s, are due to the processing delays that were not considered in the simulation. These times are acceptable if compared with the obtained delays. In the considered scenario, no packet was lost.  
Figure 5: Packet Loss Ratio results.
## 8 Disease Management Options within the Context of a Disturbed Gastrointestinal Barrier
### Pharmacological Intervention in CKD
Thus, they all have the potential to increase serum phosphorous levels through upregulation of NaPi-2b and consequent intestinal Pi absorption and should be applied after careful consideration [92]. A double-blind, randomized, placebo-controlled study in patients with CKD and type 2 diabetes proposed the effectiveness of bardoxolone methyl regarding increased GFR compared with a placebo. Bardoxolone methyl is considered to have the potential to activate Nrf2 and, thus, modulate antioxidant and anti-inflammatory responses [93]. However, these findings need to be interpreted with caution since the phase 3 trial (BEACON) with the primary efficacy endpoints \"ESRD\" or \"death from heart failure\", had to be terminated due to a significant rate of unforeseen adverse cardiovascular events in CKD patients treated with bardoxolone methyl [94]. A series of post hoc analyses were conducted to identify risk factors for heart failure in the BEACON population. Bardoxolone methyl increased cardiovascular events in the proportion of patients with baseline brain natriuretic peptide (BNP) > 200 pg/mL and previous hospitalization for heart failure [95]. However, these clinical trials did not comprise the effects of a disturbed GI barrier on the study outcome. Alternatively, as discussed below, non-pharmaceutical interventions, such as dietary protein restriction, appear to have beneficial effects on Nrf2 activity. Recent experimental evidence taken from an adenine-induced mouse model of CKD has highlighted SGLT inhibitors as potential modulators of GI health in the context of renal disease. Application of SGL5213, an inhibitor of GI SGLT1, reduced circulating levels of gut-derived uremic toxins, creatinine, and urea [96]. Treatment reduced renal and GI inflammation and restored the _Firmicutes/ Bacteroidetes_ ratio, which is currently discussed as a possible biomarker for gut dysbiosis [96]. This is not limited to CKD but is also associated with other conditions, such as obesity and aging [97; 98]. It remains to be seen whether these effects can be transferred to human patients, as no data is available yet. The effect of renal SGLT2 inhibitors was recently meta-analyzed and revealed a lower risk of decreasing kidney function, ESRD or renal death by 30% in patients with GFR < 60 mL/min/1.73 m\({}^{2}\)[99]. However, this effect depended on other comorbidities, as patients with CKD and heart failure did not profit from the medication [99]. GI health was not considered in this analysis.
## 4 Review of a V2I Authentication Scheme
### System Initialization  
Firstly, each RSU inputs its unique real identity \(ID_{r}\) to TA. TA randomly chooses a fresh number \(\alpha_{i}\) where \(i\in[1\ldots m]\), \(\alpha_{1}\
eq\alpha_{2}\
eq\cdots\
eq\alpha_{m}\). Then, TA calculates a group of pseudo identities \(RID_{i}=E_{K}[ID_{r}\|ID_{TA}\|\alpha_{i}]\) and assigns \(\{ID_{TA},(RID_{i},\alpha_{i})\}\) to RSU. Then,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Meaning** \\ \hline \(ID_{TA}\), \(ID_{r}\) and \(ID_{v}\) & Identity of TA, RSU and \(VUL\), respectively \\ \(BIO_{v}\) & The biometric information of \(VUL\) \\ \(PW_{v}\) & The password of \(VUL\) \\ \(BSK_{v}\) & The biometric key of \(VUL\) \\ \(\varepsilon_{v}\) & The reproduction parameter of \(VUL\) \\ \(K\) & The private key of TA \\ \(x_{r}\) & The private key of RSU \\ \(P_{pub}\) & The public key of RSU \\ \(A\) & The adversary or attacker \\ \(h(.)\) & The collision resistance one hash function \\ \(E_{\epsilon/d}(.)\) & The symmetric encryption/decryption \\ \(\oplus\) & The XOR operator \\ \(+\) & The normal addition operator \\ \(\|\) & The concatenation operator \\ \(N_{i}\), \(i\in[0\ldots 7]\) & The freshly generated random numbers \\ \(\alpha_{i}\), \(i\in[1\ldots m]\) & The nonzero positive random number \\ \(\beta_{i}\), \(i\in[1\ldots n]\) & The nonzero positive random number \\ \(ctr\) & The randomized counter \\ t\({}_{0}\), t\({}_{1}\), t\({}_{2}\), t\({}_{3}\), t\({}_{4}\) & Timestamps \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of notations.  
TA stores \(h(ID_{r}\|K)\) into its database but does not store them in plaintexts. At the beginning of every session, RSU reselects the \((RID_{i},\alpha_{i})\) pair and updates the set of \((RID_{i},\alpha_{i})\) pairs periodically to keep the \(RSU\) anonymous.
# Narrowband Internet of Things (NB-IoT):
## 1 Introduction
In [31], the authors presented a survey on the NB-IoT downlink scheduling issues by highlighting the associated scheduling process in terms of offset index selection. In [33], the authors surveyed the uplink and downlink performance evaluation of NB-IoT systems by analyzing the main causes of latency, trade-off between throughput and free resources, channel occupancy etc. with respect to Release 13 and Release 14 updates. In contrast to the above surveys, this paper presents:  
* A comprehensive survey of NB-IoT, from Release 13 to the ongoing Release 16 prospects. * An all-inclusive overview of the state of the art of PHY and MAC layers by addressing the key improvement concerns in terms of challenges and the corresponding potential solutions. * The possible NB-IoT deployment strategies for synchronous and asynchronous network structures in HetNet scenarios to foster the NB-IoT coexistence with legacy technologies as well as with the fifth generation (5G) networks. * Discussion on the open research challenges to motivate future research directions. To the best of the authors' knowledge, this is the first survey that covers broadly these above-mentioned contributions and hence will facilitate the reader's knowledge related to NB-IoT from standardization, ongoing research, and its practical implementation. The rest of this paper is organized as follows: Section 2 discusses NB-IoT standards by elaborating the key design changes and the related ongoing enhancements. Section 3 presents the state of the art of NB-IoT protocol stack by detailing the PHY layer and MAC layer features. Section 4 discusses the open research questions and their potential solutions, and the conclusion is drawn in Section 5.
## 7 Differentiation between Switching and Faulty Transients
### Capacitive Switching  
A capacitor connected on the node 675 was switched off at the fourth cycle and switched on again at the sixth cycle. The current signal recorded on node 650 was processed using the algorithm and FI was evaluated, as illustrated in Figure 14. It was established that FI has lower values than the threshold during the transients due to the switching off and on of the capacitive load. Hence, the proposed algorithm effectively discriminates the switching transients due to the capacitor switching from the faulty transients.  
Figure 13: Effect of feeder tripping and re-closing on the performance of algorithm (**a**) Current waveform (**b**) WD-index (**c**) ALN-index (**d**) Fault index.
# Vision-Based Distance Measurement in Advanced Driving Assistance Systems
## 6 Experiments and Results
### Performance Comparison of Depth Estimation
#### 6.2.2 Ablation Study of Depth Map Estimation  
Compared with unsupervised networks, semi-supervised networks can improve the performance of depth map estimation by introducing the sparse and local depth labels. In this subsection, we present our experiment to demonstrate the function of these sparse labels. Figure 6 shows the comparative results of the unsupervised and semi-supervised methods. The first row contains three forward-looking images captured by on-board vision system and containing vehicles and pedestrians. Three maps of the 3-D point cloud are the sparse and local depth labels corresponding to the three images in the first row. From the depth labels in the second row, we can observe that the ground truth of the depth value is mainly concentrated in the middle area of each image, and there is no ground truth in the upper and lower edges. The third row contains the depth map estimation results obtained by the unsupervised method, which only uses left-right consistency. The last row contains the depth maps estimated by the proposed semi-supervised method. By comparing the areas in white rectangles in the third and fourth rows, and considering the areas in red rectangles in the first row, we can see that the results of the semi-supervised method can estimate depth values more accurately. For example, the depth prediction of strip objects such as pedestrians and traffic signs is more accurate.