esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### ToF-SIMS Analyses of OTMS Molecules and OTMS SAMs
These experimental observations, as already discussed for the negative ion spectra, confirm the formation of OTMS SAMs on the SiO2 substrate. Figure 8 shows positive secondary positive ion mass spectra from \(m/z\) 150 to 400 for the three samples. Figure 8a shows the protonated molecular ion [M + H]+ at \(m/z\) 375 for the pure OTMS molecules, which was not detected in the OTMS SAMs (Figure 8c). As shown in Figure 8b,c, the spectra for the other two samples show very weak ions that are not identified. Therefore, the positive ion mass spectrum for the OTMS SAMs shows it is characterized by hydrocarbon ions, which is due to the alkyl groups. The information about the interface is the lack of OTMS headgroup-related ions. Therefore, the positive ion mass spectra for OTMS SAMs are less informative than their negative counterparts, in which, as discussed before, the -Si-O-Si- network was probed via higher-mass silicon oxide cluster ions. Figure 8: Positive secondary ion mass spectra (\(m/z\) 150–400) for (**a**) pure OTMS (placed on a polypropylene film); (**b**) a UV/ozone-treated SiO2 surface; (**c**) OTMS SAMs prepared on a UV/ozone-treated SiO2 substrate. Ions indicated by a star (\({}^{\star}\)) are associated with the OTMS headgroup, -Si(OCH3)3.
# Loop Closure Detection Based on Multi-Scale Deep Feature Fusion
## 3 Loop Closure Detection Algorithm Based on Multi-Scale Deep Feature Fusion
### Feature Fusion Layer  
An RGB image can obtain several feature maps describing different deep features from features extraction layer. For general image classification tasks, a fully connected layer is usually added to weight and sum all the feature maps to obtain the classification result. However, each feature map only corresponds to a small area in the original input image, and its receptive field is small. The deep feature is a local feature. For the loop closure detection problem, we prefer to have different scales of features because this helps to accurately determine whether two images belong to the same scene. In addition, AlexNet [27] requires the fixed-size of the input image to be 227 \(\times\) 227 \(\times\) 3. When the size of the input does not meet the requirements, cutting or compressing must happen, which causes the loss or distortion of some image information.  
To overcome this problem, we use spatial pyramid pooling (SPP) [1] to fuse multi-scale deep features. SPP divides the feature map into small patches of different sizes by different scales, and then gets each patch's feature. The receptive fields to the input images of these small patches are different, which means the patches correspond to different areas of the input image. Finally, the features extracted from the small patch of different sizes are combined to achieve the fusion of multi-scale features. In addition, the SPP layer can get a fixed-size output and eliminate the restriction that the input image size must be fixed.  
Taking a single branch as an example, the feature fusion layer is shown in Figure 3. Here, the input of feature fusion layer is the output from feature extraction layer. After the SPP layer, we can obtain the fixed-length representation of the features, which is finally sent to softmax.  
As seen in Figure 3, SPP divides each feature map by using three different scales 4 \(\times\) 4, 2 \(\times\) 2, 1 \(\times\) 1. Each scale corresponds to one layer in the SPP; three scales indicate that the SPP has three layers colored as blue, green and gray. For example, the 4\(\times\)4 scale divides a feature map into 4 \(\times\) 4  
Figure 3: Feature fusion layer.  
Figure 2: Structure of feature extraction layer.  
small patches, and then extracts a feature from each small patch, so each feature map can extract 16 features. There are 256 feature maps as output from the feature extraction layer, and altogether there are 16 \(\times\) 256 features (blue parts in Figure 3). The features extracted by the different scales of the SPP are concatenated together to obtain a fixed-length feature vector. In order to normalize the results of the SPP, we add a SoftMax layer. Assuming that the feature vector output by the SPP layer is \(Z\in R^{n\times 1}\), the output of the SoftMax layer is:  
\[Y=\left[\frac{e^{Z_{1}}}{\sum_{j=1}^{n}e^{Z_{j}}},\cdots,\frac{e^{Z_{n}}}{ \sum_{j=1}^{n}e^{Z_{j}}}\right]^{T},Y\in R^{n\times 1}, \tag{2}\]  
The number of SPP
## 5 Data Preparation and Filtering  
Raw data collected from sensors tend to have errors and distortions. These data have to be pre-processed/filtered before they could be used for further analysis. The raw data has to be denoised and cleaned to transform them into useful information. Different types of errors may be accumulated throughout the process of data collection and this section aims to bring awareness to the most common errors retrieved from the data acquisition system and the data filtering methodology.
# Comparative Analysis of Current Approaches to Quality Estimation for Neural Machine Translation
## 5 Question 1: Which mPLM Is Best for QE Tasks?  
### Dataset Details  
In this study, we conducted experiments concerning sub-tasks 1 and 2 at the sentence-level of WMT20 based on various mPLMs. We experimented using the English-German language pair and used train, dev and test data provided by WMT20 ([[http://www.statmt.org/wmt20/quality-estimation-task.html](http://www.statmt.org/wmt20/quality-estimation-task.html)]([http://www.statmt.org/wmt20/quality-estimation-task.html](http://www.statmt.org/wmt20/quality-estimation-task.html)), accessed on 15 July 2021). Table 1 shows a summary of the data for each sub-task.  
In the case of sub-task 1, there is a total of 7k training data, and the numbers of source and MT output tokens are 98,127 and 97,453, respectively. The average of the mean z-standardized DA score is \(-\)0.008 and the median is 0.162. The development and test data consist of a total of 1K data, and there are approximately 14K source and MT output tokens. The development and test data provide average scores of \(-\)0.049 and 0.040, and the respective median scores are slightly higher at 0.211 and 0.319.  
In the case of sub-task 2 at the sentence-level, the number of sentences is 7K in the training data and 1K in each of the development and test data, as in sub-task 1. The average HTER score is distributed around 0.3, and the median value either does not significantly differ or is slightly lower than the average value. HTER is centered around values lower than the error rate of 0.5.
# Efficient Next-Hop Selection in Multi-Hop Routing for IoT Enabled Wireless Sensor Networks
## 5 Results and Analysis  
This section introduces simulation features, hypotheses, topology, implementation information, and different situations to achieve the results. Simulation is conducted by using network simulator NS-2.35, where TCL scripts are written to deploy the nodes as per configuration. Moreover, the energy model is also configured to extract the residual energy of each node as the simulation time passes gradually. Messages are also initiated as per the communication scenario from TCL files. These messages are then processed in C coding for further transmission to destination nodes by setting the configuration parameters such as source and destination IDs, ports, and IP addresses along with the packet's sequence number, type, and enumerated value. Similarly, the receive function is developed to further manage the functionality as per packet ID and then transmitted to other nodes until the destination nodes are not reached. We have used separate classes to configure \(CH\) and low-power sensor nodes. Objects of these classes are created in TCL files to create nodes as per configurations. Additionally, the battery storage of \(CH\) nodes is 20 times more than that of the ordinary sensors. The base schemes are LEACH [24], PEDAP [25], PEGASIS [27], DADCNS [28] and L-VH [32]. A list of simulation parameters is presented in Table 2.
## 1 Introduction
(2022) [43] proposed an auto machine-learning-based approach to predict excavation-induced tunnel displacements. [PERSON] et al. (2022) [44] developed a machine-learning method which employed an advanced machine-learning model of extreme gradient boosting, to predict the maximum wall deflections of deep braced excavations in sand. [PERSON] and [PERSON] (2022) [45] proposed a data-driven decision support framework based on the integration of a deep neural network and gradient descent technique to predict the risk in shield tunnel excavation under uncertainty. In most of the above studies, site conditions, excavation methods, excavation dimensions, and the distance between the object and excavation are generally used as the input variables, but the use of real-time monitoring data is still insufficient. Since the monitoring data are a direct reflection of excavation, it is of great engineering significance to construct a practical data-driven method which makes full use of on-site monitoring data for fully understanding the responses of existing buildings and utilities induced by adjacent excavation and ensures the safety in excavation. In view of the great difficulty in directly monitoring some existing structures (such as subway tunnels, utility tunnels, and buried pipelines) in the adjacent excavations, and the problem of monitoring interruption caused by a variety of factors, the motivation of the article is to construct an indirect method for predicting the responses of structures induced by adjacent excavation by making full use of on-site monitoring data. Here, the settlement of existing buildings induced by adjacent excavation is taken as the case study, seven necessary monitoring items required in China's technical code for the monitoring of building excavation engineering [46] are taken as the basic input variables, and an indirect approach for predicting the settlement of buildings caused by excavation is established by using machine-learning technology. The content of the article is arranged as follows: Firstly, the research status in the domain of adjacent excavation is reviewed, and the issue studied in this article is laid out. Then, the solution scheme for the issue is put forward. Finally, based on the MATLAB software platform, the scheme is implemented by using the machine-learning algorithms of SVM and BP neural networks, and a data-driven model for predicting the settlement of buildings induced by adjacent excavation is established.
###### Abstract
[13] applied the particle swarm optimization (PSO) algorithm to achieve full-space inversion, enabling the segregation of the inversion outcomes for the apparent resistivity anomalies of the mine roof and floor. [PERSON] et al. [14] utilized PSO for Group Information Sharing to invert transient electromagnetic data from conical source mines, improving the interpretation of mine-wide spatial inversion. [PERSON] et al. [15] proposed transient EM inversion based on a joint quantum particle swarm optimization (QPSO) algorithm that was combined with the smooth constrained least squares (CLS) algorithm. [PERSON] et al. [16] applied the firefly algorithm (FA) for transient electromagnetic inversion. [PERSON] et al. [17] utilized the differential evolution (DE) algorithm for the two-dimensional inversion of magnetic anomaly signals. The results indicated that the DE algorithm is not only suitable for amplitude inversion in the analysis of two-dimensional magnetic profile anomalies with induction or residual magnetization effects but also for low-dimensional data inversion in geophysics. These algorithms possess their respective strengths and weaknesses, primarily differing in their evolution mode. The PSO algorithm's optimization mode is simple and exhibits good optimization performance, but it suffers from a serious problem of premature convergence. Consequently, researchers have introduced many PSO variants with improved performance [18; 19]. The DE algorithm is a highly effective heuristic algorithm widely used in engineering, but its performance depends on its mutation strategy and control parameters. Different problems require different parameter adjustments, which can be time-consuming. Scholars have proposed strategies to address this, such as fuzzy adaptive DE [20; 21]. SSA [22] places emphasis on more late-stage searchability by classifying and updating the sparrows in the population; however, it primarily relies on the location information of the best and worst individuals in the sparrow's population for location updates, with the low utilization of other individuals. There is still significant room for improvement in optimization performance for high-dimensional complex optimization problems. Based on the above analysis, this paper proposes a segmented regularized inversion method based on the improved sparrow search algorithm (ISSA) to address issues of high dependence on a priori information, the unstable performance of optimization algorithms, and the slow convergence speed of existing local optimization methods. Firstly, the segmental regularized inversion method is introduced to enhance the efficiency and stability of the inversion. Then, a differential variation operator is incorporated into the original sparrow search algorithm to improve the utilization of individual information. Finally, a comparative test is conducted between synthesized data from the standard model and measured data to assess the convergence stability, initial model dependence, and electrical interface resolution capability of the ISSA inversion method.
# Numerical Analysis of the Settlement Behavior of Soft Soil Improved with Stone Columns
## 2 Numerical Analysis of Field Trials at Bothkennar Site
### Soil Profile  
The Bothkennar test site is located on the south side of the Firth of Forth in Scotland and is very well characterized [39; 40]. The site has been used extensively for research, including in-situ testing and undisturbed sampling techniques [40], full-scale testing of piles and pad footings on untreated soft clay [41], and widespread load on stone columns [42]. The soil profile comprises a firm-to-stiff silty clay crust about 1.2 m thick, underlain by a deep deposit of soft clay (Carse clay) about 17 m thick, which rests on a layer of dense sand, gravel, and cobbles about 3 m thick. Below about 14 m, the soft clay becomes more laminated and firmer. In-situ vane tests showed that the undrained shear strength of the Carse clay increases linearly from about 18 kPa just below the crust to 55 kPa at depth. The overlying crust layer has a variable undrained shear strength based on hand vane tests from about 120 kPa at 0.2 m below the ground surface to 40 kPa at the base of the crust. The groundwater table is normally close to the ground surface. The soil stratigraphy and key properties for the Bothkennar soft clay site are shown in Figure 1.
# A C-Based Framework for Low-Cost Real-Time Embedded Systems
## 4 Core Framework Components
### Implementation Description  
The timer task consists entirely of RtFw code. It makes use of a suitable RTOS timing primitive, vTaskDelayUntil in the case of FreeRTOS, to schedule its execution every control cycle period. This periodic schedule is then used to generate a stream of events that drives cyclic execution. The stream is conveyed to an _event system_ through data flow $. The event system is responsible for collecting cyclic, as well as sporadic events, and delivering them to the control task by activating it whenever an event occurs. As explained previously, real-time networks, UI, and proxy tasks can notify the event system about the occurrence of a sporadic event as well, by means of data flows 1 and 2, respectively.  
The event system consists of a FreeRTOS message queue Q of \(N\) elements and a counting semaphore R. The main goal of Q is to buffer up to one cyclic event and \(N-1\) sporadic events. The actual values stored in the queue are integer event identifiers, useful to distinguish cyclic from sporadic events and, at a finer granularity, one class of events from another.  
Considering that cyclic events must be given higher priority than sporadic events to bound control cycle jitter, cyclic events are always inserted at the front of Q. Instead, sporadic events are appended to its back, to be processed in a first-in, first-out (FIFO) fashion. This is consistent with the message queue primitives xQueueSendToFront and xQueueSendToBack that FreeRTOS provides, and is well within the capabilities of any other RTOS.  
By itself, prioritizing the way events are enqueued is insufficient to grant cyclic events expedited handling, unless there is a way to guarantee that Q is never completely full when a cyclic event occurs. Otherwise, any enqueue operation--no matter if it is directed to the front or the back of the queue--would block the caller. RtFw addresses this potential issue by ensuring that Q never contains more than \(N-1\) sporadic events, under the assumption that no cyclic events are ever generated while another cyclic event is still in the queue.  
The counting semaphore R is used exactly for this purpose. More specifically, it keeps a record of free elements in Q that are available for sporadic events at the moment, and blocks sporadic event sources as needed, to exert back pressure on them. Accordingly, sporadic event insertions are preceded by a potentially blocking xSemaphoreTake operation on R, and sporadic event extractions are followed by a matching xSemaphoreGive. The assumption mentioned previously is reasonable because violating it would imply there was a cycle overflow, which is considered a severe error condition in a cyclic executive [49]. As a simple form of recovery, RtFw skips a control cycle whenever it detects a cycle overflow, keeping track of the occurrence in its status and diagnostic information.
## 5 Evolution of Opportunistic Routing in Asynchronous WSN
### Evolution of Opportunistic Routing Protocols
#### 5.2.5 Morr  
In 2018, [PERSON] et al. improved the ORR [13] and proposed Modified Opportunistic Routing with Residual energy (MORR) [15]. They focused on the issues of network lifetime and load-balancing in WSNs. MORR makes use of the same methodology as ORR, but is more refined than ORR, in such a way that their proposed protocol considers channel interference and controls the number of forwarders by  
Figure 16: Sender- and receiver-initiated fast channel hopping scheme in MOR [14].  
selecting nodes that have a minimum number of neighbors, while considering the maximum residual energy of forwarders, and controlling the number of transmitters as well. The contributions of this paper were the following. First, their focus was on channel interference for collision avoidance by selecting nodes with minimum neighbors and minimizing the duplicate packets. Second, they controlled the number of transmitters. Finally, they selected nodes with the maximum residual energy, in order to balance the consumption of energy and enhance the lifetime of the network. ORR and MORR have one major difference: MORR considers residual energy and minimizes the number of neighbors, while also considering the shortest distance to avoid collision and minimize duplicate packet transmissions. The forwarder set calculation is similar to the ORR, except that the forwarder with the minimum amount of neighbors is selected.  
However, MORR does not consider duty cycle duration. To reduce the sender waiting time, the authors proposed a longer duty cycle, which is in opposition to reduced energy consumption. On the other hand, they select nodes with the minimum amount of neighbor nodes; in this case, there is a higher possibility that these nodes will drain energy earlier than the other nodes, because the same nodes will be selected multiple times.
# Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications
## 2 Imaging Modalities of Electrical Impedance Tomography (EIT)
### Absolute Imaging  
The simplest method to produce an image from EIT measurements is that within the measured object plot the measured voltages directly to a conductivity distribution. In absolute imaging at any point in time, images can be produced without the need for reference calculations.
# Secure Cooperative Routing in Wireless Sensor Networks
## 2 Materials and Methods
### Detection of Sinkhole and Wormhole Attack Using MIDS Algorithm  
In the deployed LEACH-based network, CHs receive data from the sensor nodes which, for final processing, are transferred to the base station (BS). BS has an IDS agent which analyzes the information received by the BS. A simple mathematical model for the verification of the effectiveness of the algorithm is presented. The network comprises uniformly distributed four anchor nodes, strategically positioned at the perimeter of the simulation area and randomly deployed 196 non-anchor nodes (N1, N2). Clusters formed from fuzzy c-mean clustering are denoted by Ci. The Hi is the CH of a certain cluster Ci. The packets received Ri and packets transmitted Ti values of a CH(Hi) are processed by the IDS agent in the BS. The IDS agent has a ratio gauge which calculates the intrusion ratio (IR). From the value received from the IR, the IDS agent decides the malicious activity. IR can take any of the two values: infinity or any numeric value n:  
\[\text{IRi}=\text{\{infinity: Hi is malicious ; n: Hi is a normal node} \tag{7}\]The underlying AOMDV protocol keeps a record of the routing table entries used by the sender node. It checks in the routing table whether a route is available or not for communication. If the route is found, it gives the information; otherwise, it broadcasts the RREQ parcel to its neighbors which then checks whether a route is available to the necessary goal or not. When the sender node transmits the RREQ, it will note the time t\({}_{1}\). At whatever point the receiver gets the RREQ bundle, it sends the RREP parcel to the source along a similar way through which the RREQ parcel has shown up. Time t\({}_{2}\)_i is noted for each route reply by the sender node.  
Then, the sender node takes the RTT (t\({}_{3}\)_i = t\({}_{2}\)_i - t\({}_{1}\)) value from each path and divides this value with the hop count of each respective route. The obtained value is termed as ts_i. The average RTT is calculated with the help of this value ts_i. Now, the calculated value is the threshold (tht) RTT. From the comparison of tht and ts_i, it is found that if ts_i is very minimal, also the hop count of the certain path is 2, then the route is detected as a wormhole link and is jammed for further communication. The sender node detects the first neighbor as malicious and sends a dummy request to it and the receiver receives this dummy request through its first neighbor and detects this node as malicious. In this way, both nodes are eliminated, and routing table entries are updated throughout the network.