esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Granularity Semantic Collaborative Reasoning Network for Visual Dialog
## 5 Experimental Results and Analysis  
### Comparison with State-of-the-Art Methods  
#### 5.1.1 Results on Test-Standard v1.0 Split  
Under the discriminative decoder setting using VisDial v1.0, we initially contrasted our proposed model with those of previously published results, such as LF [4], HRE [4], MN [4], co-reference neural module network (CorefNMN) [28], graph neural network (GNN) [29], factor graph attention (FGA) [30], dual visual attention network (DVAN) [31], RVA [16], dual encoding visual dialog (DualVD) [32], history-aware co-attention (HACAN) [33], synergistic network [34], DAN [5], textual-visual reference aware attention network (RAANet) [35], HESNet [15], CAG [19], LTMI-LG [36], VD-BERT [20], and MVAN [10].  
Our MGSCRN model outperformed various techniques according to the NDCG metric, as shown in Table 1. Specifically, when compared with VD-BERT, our model improved the NDCG from 59.96 to 60.98 (+1.02) and achieved results that were competitive with most non-NDCG metrics. Note that VD-BERT employs pretrained BERT language models and unified discriminative and generative training settings to improve performance. We also noticed that the MGSCRN model performed better than MVAN across various metrics (e.g., NDCG from 59.37 to 60.98 (+1.61) and MRR from 64.84 to 65.40 (+0.56)).  
To compare the MGSCRN to more sophisticated works, we provided our ensemble model's performance on the blind test-standard split of the VisDial v1.0 dataset in Table 2. For comparison, we chose the Synergistic [34], DAN [5], consensus dropout fusion (CDF) [37], and MVAN [10] models, because their ensemble performance results are available in the literature. For our ensemble model, we averaged the results (\(P_{t}^{Dis}\)) of the discriminative decoders after training six independent models with random initial seeds. Overall, our ensemble model outperformed all of the others (e.g., 62.25 NDCG compared to MVAN's 60.92, and 67.42 MRR compared to MVAN's 66.38). This further validates the effectiveness of our MGSCRN model as it focuses not only on predicting ground-truth answers (e.g., indicated by MRR) but also on selecting more plausible answers as correct ones (e.g., indicated by NDCG).  
To improve task performance even more, we added multitask learning that combined the discriminative and generative decoders during training. For a fair comparison, we conducted multitask learning experiments (see Table 3). Following the visual dialog task [4], the generative decoder generates the next token, depending on the current one from the ground-truth answer, and utilizes log-likelihood scores to determine the rank of the candidates. We averaged the ranking outputs of the discriminative and generative decoders for evaluation. As shown in Table 3, our proposed model was effective in a multitask setting, achieving the best results on all metrics. Note that ReDAN [6] and MVAN [10] adopt the same method as ours, but the LTMI [8] uses only the discriminative decoder output for evaluation.
## 2 LoRaWAN Network Downlink Communication  
### Downlink Communication Mechanism Based on the SDN Framework  
In this paper, a LoRaWAN downlink communication mechanism is proposed by combining the software defined network framework and the improved ARIMA model. The proposed downlink routing mechanism is shown in Figure 1. The routing mechanism is mainly composed of three parts, the host, the transport layer and the terminals. The link layer discovery protocol (LLDP) is developed for the host to enable the server to quickly and accurately set up the global topology of the downlink routing of the LoRaWAN network and the network status parameters. The SDN controller at the transport layer collects and manages the network status according to the traffic information, and then formulates the corresponding downlink communication routes. The link bandwidth occupancy rate prediction module sends out the received downlink bandwidth occupancy rate at time \(t\) into the time series ARIMA model for training, and the occupancy rate is predicted for time \(t+T\), where \(T\) represents the sampling point. The selectivity \(Q\) of different routes are calculated from the routing calculation module based on the triangle module of fuzzy mathematics. According to the flow entries, data is transmitted to the LoRaWAN gateway by the SDN switch. Finally, the downlink data packet is transmitted to the terminal device by the LoRaWAN gateway. Routing protocols are applied between network server to SDN switches. The ARIMA link bandwidth occupancy prediction model (LBOP-ARIMA) in the SDN control layer is the core part of the routing mechanism. This mechanism takes advantage of the OpenFlow protocol to implement the real-time information exchange between the control layer and the gateway and separates the processes of data control and data forwarding, and thereby reduces the transmission delay and packet loss rate.
## 2 Study Area and Methods
### Methods
#### 2.2.4 Sandstorm Prevention and Wind Erosion Assessment  
The second ecosystem service estimated is sand storm prevention, which refers to the amount of sand retained in an ecosystem within a certain period (one year for this study). The Revised Wind Erosion Equation (RWEQ) model was used [33; 34] to estimate the sand storm prevention service and wind erosion.  
The RWEQ combines empirical and process-based modeling and has been extensively tested under broad field conditions. To simulate sand/soil loss at a regional scale over varying vegetation cover and patterns, the RWEQ in PC Raster [35] was modified. PC Raster is an environmental modeling language embedded in ArcGIS, providing spatial and temporal functions that can be used to construct regional models.  
The RWEQ model estimates sand/soil loss at a specific point (\(SL\); kg m\({}^{-2}\)) as a function of several factors: weather (\(WF\)); soil erodibility (\(EF\)); soil crust (\(SCF\)); surface roughness (\(K\)); and vegetation cover (\(C\)), which permit estimation of the maximum transport capacity by wind (\(Q_{\max}\)) as follows (Equations (5)-(7)):  
\[Q_{\max}=109.8\big{[}WF\times EF\times SCF\times K^{\prime}\times C\big{]} \tag{5}\]  
\[S=150.71\times\big{(}WF\times EF\times SCF\times K^{\prime}\times C\big{)}^{- 0.3711} \tag{6}\]  
\[S_{L}=\frac{2\times z}{S^{2}}Q_{\max}\times e^{-(z/s)^{2}} \tag{7}\]  
where \(Q_{\max}\) (kg m\({}^{-1}\)) is the maximum transport capacity, \(S\) (m) is the critical field length, \(z\) (m) is the distance from the upwind edge of the field, and \(SL\) (kg m\({}^{-2}\)) is the soil loss caused by wind erosion. The identification of above parameters was described in detail by [PERSON] et al. [26].
# Method and System for Heart Rate Estimation Using Linear Prediction Filtering +
## 3 Results
The result of the real-time processing of this signal by the LPC heart rate estimation algorithm is presented in Figure 10:1 The system was evaluated by generating ECG signals at these frequencies, and the heart rate was estimated using 100 cardiac cycles per batch; the performance was assessed by computing the accuracy compared to the true value. The generated signals were applied to the prototype for real-time accuracy tracking. In Table 1, the accuracy of the measured frequencies is presented, and the small standard deviation demonstrates that the instantaneous heart rate has been successfully estimated, as the waves have a fixed frequency.
## 4 Findings from the Literature Review
### Topic 3--General Power Generation and Transmission  
In this topic, we found several contributions about power generation and transmission, with the exclusion of photovoltaic applications (see Topic 4). The application of clustering on faults data led to a discriminant model in detecting the defects of power transmission lines using k-means, improving inspections rules and safety [50]. The latter is a non-negligible issue in this context. The topic includes contributions explicitly linked to prognosis analyses. The role of clustering is exploratory, to extract information that sets the probability of failure occurrence over a specific time frame [51; 52]. Interestingly, these two contributions share two important points: the first is the clustering algorithm used, i.e., AUto-adaptive and Dynamical Clustering, and the second is the concept of slow fault evolution. The slow evolution of a failure tends to be more problematic to analyze, making it necessary to structure the entire analysis on this specific characteristic. Research into the fault management of wind turbines is becoming increasingly important, and here too the concept of clustering and variability overtime is being investigated. Wind turbines present a high level of complexity due to the high variability of their operating conditions. Based on this, clustering, e.g., k-means, is proposed for identifying the operating conditions of wind turbines [53]. The topic of bearings is also addressed, with more contributions. Bearings are one of the most-investigated areas in the condition maintenance literature. In this topic, compared to, e.g., topic 1, it is addressed without focusing on impulsive signals that can be extracted from vibration measurements. In this sub-area, while still using vibration analysis, clustering takes on a more diagnostic role, used as a tool to identify the nature of the fault [54]. Similar analyses, using the same algorithm but with different parameters, specifically the energy ratios of the intrinsic mode functions of the bearings, have also been carried out in other research projects [55]. The same parameter is found, with the same diagnostic objectives, in other contributions to bearing analysis [56]. In the theme of slowly evolving faults, a contribution on temperature controllers shows the application of the AUto-adaptive and Dynamical Clustering technique to estimate the probability of occurrence of a fault. This gives insight about the algorithm performance [57]. The analysis on the techniques highlights the fuzzy c-means technique, mainly applied for the bearings monitoring [54; 55], and different contexts [58]. The same contributions and [56; 59] show a fruitful application of the decomposition of monitored signals into intrinsic mode functions, to use some extracted information as a discriminative feature within the analysis. Another important technique in this topic is the empirical mode decomposition, for the decomposition of monitored signals [54; 55; 56; 59; 60]. The temperature theme, highlighted among the keywords, relates to the topic of temperature controller monitoring [51; 57].
## 1 Introduction
Moreover, in the framework of the evaluation of rice quality, NIR spectroscopy has been used for the discrimination of rice [21; 29]; the classification of varieties and detection of transgenic rice [31]; the quantification of various physico-chemical properties (such as moisture content, sound whole kernel, whiteness, translucency, color, and amylogram characteristics) [32]; classification of cultivars [33], prediction of protein and amylase content [34; 35]; detection of wax rice [36]; and prediction of eating quality [37]. In this regard, the present paper aims at developing a non-destructive analytical methodology for detecting rice adulteration using a hand-held NIR spectrometer. Classification models were built using two different chemometric techniques, which often coupled with NIR spectroscopy, in particular for the analysis of plants and agro-food products [38; 39]: partial least squares discriminant analysis (PLS-DA) and soft independent modelling of class analogy (SIMCA).
# Land Cover Classification Using a KOMPSAT-3A Multi-Spectral Satellite Image
## 2 Materials and Methods  
In this study, first of all, a study area was selected. Obtained data was georeferenced for geometric correction and band ratios and indices were derived and then stacked into one. Training and validation pixels from the same high resolution natural color composite and panchromatic image were extracted based on field visit and expert knowledge. These pixels were used for classification and accuracy assessment of the results. Finally, four common classifiers, i.e., MahD, MinD, ML and SVM were applied on original and stacked dataset to compare the accuracy results. The overall process adopted by the study is shown in Figure 1.
###### Abstract  
On 19 September 2021, La Palma Cumbre Vieja Volcano started an eruption classified as Volcanic Explosive Index (VEI) 3. In this study, at least the six months prior to such an event have been investigated to search for possible lithosphere-atmosphere-ionosphere bottom-up interactions. The lithosphere has been analysed in terms of seismicity getting advantages from the high-density local seismic network. Possible atmospheric alterations related to the volcano emissions or release of gases due to the uplift of the magmatic chamber have been searched in SO\({}_{2}\), aerosol, dimethyl sulphide, and CO. The magnetic field on Earth's surface has been studied by ground geomagnetic observatories. The status of the ionosphere has been investigated with two satellite missions: China Seismo Electromagnetic Satellite (CSES) and European Space Agency _Swarm_ constellation, with Total Electron Content (TEC) retrieved from global maps. We identified a temporal migration of the seismicity from November 2020 at a depth of 40 km that seems associable to magma migration, firstly to a deep chamber at about 15 km depth and in the last 10 days in a shallow magma chamber at less than 5 km depth. The atmospheric composition, ground geomagnetic field, and ionosphere showed anomalies from more than three months before the eruption, suggesting a possible influence from the bottom geo-layers to the upper ones. CSES-01 detected an increase of electron density, confirmed by _TEC_ data, and alterations of vertical magnetic field on ground Guimar observatory that are temporal compatible with some volcanic low seismic activity (very likely due to the magma uplift), suggesting an eventual electromagnetic disturbance from the lithosphere to the ionosphere. A final increase of carbon monoxide 1.5 months before the eruption with unusually high values of TEC suggests the last uplifting of the magma before the eruption, confirmed by a very high shallow seismicity that preceded the eruption by ten days. This work underlines the importance of integrating several observation platforms from ground and overall space to understand geophysics better, and, in particular, the natural hazard affecting our planet.  
Keywords:lithosphere; atmosphere; ionosphere; LAIC; volcano; La Palma; CSES; swarm +
Footnote †: journal: remote sensing
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 3 The Knowledge of Underwater Acoustic Sensor Networks
### The Characteristics of Underwater Acoustic Communication
#### 3.1.5 Highly dynamic topology  
In the terrestrial environment, once the sensor nodes are deployed, they cannot move around easily and frequently, so the topology changes only when old nodes die or new nodes join the network. However, in the underwater environment, due to the water currents, the sensor nodes are continuously moving with the water currents, therefore, the topology changes frequently, which has a great effect on the routing protocol performance.  
The differences between the underwater environment and the terrestrial environment are shown in Table 1. Due to the great differences, the standard wireless sensor network routing protocols cannot be applied to UASNs directly. We need to redesign or modify the WSN routing protocols according to the underwater environment conditions to meet the QoS requirements of UASNs.
## 3 Proposed Model
### Offloading Cost  
Workflow application tasks are closely linked to each other and follow by precedence constraints rules. According to the offloading system, rule partitions an application into two disjoint sets, for example, local disjoint set \(V_{loc}\) and remote disjoint set \(V_{cl}\). In the work flow application, the offloaded disjoint set in the cloud via wireless network incurred with communication cost. The total cost \(C_{total}\) of an application partitioning, is formulated in the following equation.  
\[C_{total}=\sum_{v\in V}Pr_{v}\cdot w^{local}(v)+\sum_{v\in V}(1-Pr_{v})\cdot w^ {local}(v)+\sum_{v(v_{i},v_{j})\in E}Pr_{e}\cdot w\Big{(}e\Big{(}v_{i},v_{j} \Big{)}\Big{)} \tag{1}\]  
However, partitioning \(P_{r}\) total cost is \(C_{total}\), \(\sum_{v\in V}Pr_{v}\cdot w^{local}(v)\) denotes the local execution cost and \(\sum_{v\in V}(1-Pr_{v})\cdot w^{local}(v)\) denotes server execution cost. In the end \(\sum_{v(v_{i},v_{j})\in E}Pr_{e}\cdot w(e\Big{(}v_{i},v_{j}\Big{)})\) is the communication cost between tasks \(v_{i}\) and \(v_{j}\).
## 5 Results
### Results with Multiple Waterdrops  
We conducted experiments to evaluate the performance of the proposed method in scenarios with more than three leakage positions. The results of scenarios with five and seven waterdrops are presented in Figure 8. In Figure 8, the detections and mean errors of the estimated leakage positions are indicated above the figures, and the yellow lines represent the estimation errors. Similarly to the result with _three separated waterdrops_, the errors were small if waterdrops were not generated near the walls, which indicates the number of waterdrops does not affect the accuracy of the leakage position estimation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(N_{\text{frame}}\) & **Detected Number (Max 30)** & **Detected Rate [\%]** & **Error (Mean \(\pm\) SD) [m]** \\ \hline
3 & 27 & 90.0 & 0.37 \(\pm\) 0.78 \\ \hline
6 & 27 & 90.0 & 0.36 \(\pm\) 0.84 \\ \hline
12 & 27 & 90.0 & 0.34 \(\pm\) 0.67 \\ \hline
18 & 26 & 86.7 & 0.22 \(\pm\) 0.24 \\ \hline
24 & 26 & 86.7 & 0.22 \(\pm\) 0.23 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Result of leakage position estimations with different \(N_{\text{frame}}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Noise** & **Detected Number (Max 24)** & **Detected Rate [\%]** & **Error (Mean \(\pm\) SD) [m]** \\ \hline L & 22 & 91.7 & 0.05 \(\pm\) 0.05 \\ \hline M & 22 & 91.7 & 0.05 \(\pm\) 0.06 \\ \hline H & 22 & 91.7 & 0.06 \(\pm\) 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of leakage position estimations with _three crowded waterdrops_.  
However, the detection rates were low if the number of waterdrops was large because the chances of waterdrops occluding each other increasing. If waterdrops were occluded in the stereo images, the proposed method failed to detect them as separate waterdrops. Furthermore, the proposed method failed to estimate the leakage positions outside of the fields of view of the stereo cameras, as can be seen in Figure 8, where the fields of view of the stereo cameras are illustrated as the cyan lines. It is worth noting that the use of a wider field of view camera would improve the detection rate of the proposed method.  
In conclusion, the proposed method can estimate multiple leakage positions if the waterdrops are captured by both cameras and not occluded in the stereo images.
## 2 Basic Theory
### Analytic Hierarchy Process  
(a) Construct a judgment matrix  
According to the expert scoring system, the importance degree is expressed by 1-9 [14], as shown in Table 1. The importance of each index to the related factors can be determined. By comparing the importance of each element of the same level to a criterion in the previous level, the judgment matrix \(A\big{(}a_{ij}\big{)}\) is constructed, as shown in Formula (10).  
\[A=\begin{bmatrix}a_{11}&a_{12}&a_{13}&a_{14}\\ a_{21}&a_{22}&a_{23}&a_{24}\\ a_{31}&a_{32}&a_{33}&a_{34}\\ a_{41}&a_{42}&a_{34}&a_{44}\end{bmatrix} \tag{10}\]  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Scale** & **Implication** \\ \hline
1 & Indicates that two factors are equally important. \\ \hline
3 & Indicates that one factor is slightly more important than another. \\ \hline
5 & Compared with the two factors, one factor is more important than the other. \\ \hline
7 & Compared with the two factors, one factor is more important than the other. \\ \hline
9 & Compared with the two factors, one factor is more important than the other. \\ \hline
2, 4, 6, 8 & Median of the above two adjacent judgments \\ \hline Reciprocal & A vs. B—If the scale is 3, then B vs. A is \(1/3\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Scaling of the judgment matrix.  
In the formula, \(a_{ij}\) is the importance scale of index \(i\) compared with factor, where \(a_{ii}=1\), \(a_{ij}=1/a_{ij}\).
2. Calculating the normalized relative weight of each index relative to the upper index According to the geometric mean method, the relative weight is calculated as follows (11): \[W_{i}=\frac{\left(\prod\limits_{j=1}^{n}a_{ij}\right)^{\frac{1}{n}}}{\sum \limits_{k=1}^{n}\left(\prod\limits_{j=1}^{n}a_{ij}\right)^{\frac{1}{k}}}\] (11) 3. Consistency check A consistency test is performed by calculating the ratio of \(C.I.\) to \(C.R\). The calculation of the consistency index \(C.I.\) is as follows (12): \[C.I.=\frac{\lambda_{max}-n}{n-1}\] (12) Of which: \(\lambda_{max}\approx\frac{1}{n}\sum\limits_{i=1}^{n}\frac{(AW)_{i}}{W_{i}}= \frac{1}{n}\sum\limits_{i=1}^{n}\frac{\sum\limits_{j=1}^{n}a_{ij}W_{j}}{W_{i}}\).  
Then, the corresponding average random consistency index \(R\). \(I.\) is found in Table 2 according to the value of \(n\).  
The consistency ratio \(C.R.\) can be calculated by the consistency index \(C.I.\) and the average random consistency index \(R.I.\) The calculation Formula (13) is as follows:  
\[C.R.=\frac{C.I.}{R.I.} \tag{13}\]  
If it can be considered that the consistency of the judgment matrix is acceptable, the weight vector calculated by Equation (11) can be used as the weight coefficient of the corresponding index. Otherwise, the judgment matrix needs to be revised.
## 3 System Hardware and Software Structure
### Hardware and Software for Vehicle Perception System
#### 3.2.1 High-Precision Map Creation  
The high-precision map is capable of storing and retrieving comprehensive data pertaining to the overall road structure, road regulations, static signage, and real-time traffic signal information for the designated examination route. The specific implementation process is outlined as follows:  
We employ a combination of LiDAR and GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System) as data sources to collect point cloud information for the testing area. Subsequently, utilizing the SC-LeGO-LOAM [19], we construct a point cloud map, which serves as the foundational representation of the road environment. Then, the point cloud map is imported into a Vector Map Builder, where it is further refined through the addition of vectorized elements. This vector map encapsulates intrinsic road features, such as lanes, parking lines, traffic lights, and intersections. This vector map is fine-tuned through on-site calibration and testing to enhance its accuracy and fidelity. Finally, we integrate real-time traffic signal information into the map through roadside RSUs components, ensuring that the map reflects the real-time state of traffic lights accurately. All the road environmental data and detection results mentioned above are utilized within the RPAM model to ascertain whether the vehicle's behavior conforms to road traffic regulations. This process, in conjunction with the traffic responsibility determination by traffic management authorities, ensures traffic safety and stability during the driving examination process.