esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Comparison  
### Discussion  
In Exp. 1, the avoidance rate of maximum wormhole effect is verified. We find when MHA applied with constant boundaries, the strict boundaries performs better than the loose ones in avoiding wormhole attack and the simulation result is shown in the Figure 5(a). The curve of the strict boundary rises faster than the loose one with the growth of \(\mathrm{RREP_{lim}}\) value. Furthermore, we present the dynamic boundary, which changes with the received RREP number, in order to improve accuracy and efficiency. As a result, it can avoid 100% of wormhole attacks when \(\mathrm{RREPlim}\geq 2\). In Exp. 2, the avoidance rates for all \(\mathrm{RREP_{lim}}\) are over 96%.  
In Exp. 3, at \(\mathrm{CF}=1\), i.e., only one graylist broadcast is needed, 2.3 and 3.74 routes are found on average under the maximum and random wormhole effects, respectively. Compared with Exp. 1 and Exp. 2, it performs with 100% and 96% avoidance rates, respectively. As a result, we can conclude that only one graylist broadcast is required for avoiding wormhole attacks, and further minimize overhead. In addition, multi-wormhole cases that have not been discussed in other previous works can be avoided by increasing the \(\mathrm{RREP_{lim}}\) value in MHA. On the other hand, if there is no wormhole within a closed environment, the graylist broadcast can be cancelled by setting \(\mathrm{RREP_{lim}}=1\) to save energy and bandwidth.  
In Exp. 3, we also find in our assumed environment, most independent routes can be found when \(\mathrm{CF}<6\). In Exp. 4, the transmission distance of MHA is longer than the shortest route by about one hop, but below the average of all routes. This is because with MHA, the routes with higher hops are also filtered out. In addition, the shortest route can represent the route discovered by AODV in safe environments. It shows our judicious use of an extra hop (on average) to grant us a great amount of protection against wormhole attacks in comparison to AODV.  
Our method provides good performance for avoiding wormhole attacks, but there could be some attacks anticipating MHA. For example, attackers may add fake nodes to an intermediate list so the route has a longer distance. However, it is quite hard for attackers to correctly estimate the expected hops of a particular communication pair since they do not know their relative position. In addition, since we only select part of the searched routes for multi-path transmission, the probability that attacks can occupy the route are further reduced. In another scenario, attackers may maliciously modify other nodes instead of itself in the graylist. Thus the nodes that have been modified would be reported as modifiers and be blocked by the source node. To counter this, some ID-based cryptographic methods [19] such as digital signatures can be adopted to prevent this.
# Groundwater Storage Changes in China from Satellite Gravity: An Overview
## 2 Data and Methodology
### Data
#### 2.2.2. Hydrological Water Balance Models  
The WGHM model and the PCR-GLOBWB model were also used to quantify the spatio-temporal GWS changes in our study regions. The WGHM model simulates water fluxes and storages globally with a spatial resolution of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\)[67]. The WGHM model was developed to assess water availability and water uses of the globe, as well as to generate scenarios of changes in water resources. It is worth noticing that, different from most LSMs, the WGHM model considers sectoral water uses for irrigation, livestock, households, manufacturing, and cooling of thermal power plants [68]. Groundwater withdrawals and recharge are simulated in WGHM; thus, we can estimate GWS changes based on these data. In this study, the WGHM model developed by the University of Frankfurt with the version of 2.2b was used [68].  
In addition to the WGHM model, we used GWS outputs from the PCR-GLOBWB model, which is also a state-of-the-art grid-based global hydrology and water resources model and developed by the Utrecht University [69; 70]. PCR-GLOBWB simulates spatio-temporal water fluxes, e.g., evaporation, discharge, groundwater recharge, and human water use. GWS changes simulated by PCR-GLOBWB have been widely used to quantify the global groundwater depletion rate and its contribution to sea level change [71; 72; 73]. Both models are forced by the same climate forcing data, i.e., the WATCH Forcing Data methodology applied to ERA-Interim reanalysis data (WFDEI). Monthly gridded GWS changes over China from WGHM and PCR-GLOBWB at 0.5\({}^{\circ}\) resolution were used and compared with other estimates in this study.
## The Proposed Model
### X-Transformer  
In order to break through the traditional NMT model training strategy, we chose to train a model with a smaller parameter, called the X-Transformer. This NMT model was retrained according to different usage scenarios in order to prevent issues similar to those of the traditional pre-trained model with larger model parameters, such as BERT. Some of these issues are that, when it encounters a non-trained corpus, the performance struggles, and the inference time is quite long.  
The X-Transformer model was constructed using the aforementioned methods, as shown in Figure 5, and has the following advantages:  
* Sub-word tokenization: Sub-words can reduce the sparse words (such as terminology) of the model, improve the relevance of each vocabulary through sub-words, and reduce the dictionary size.
* Modifies the basic building block of the encoder: It can rearrange the self-attention mechanism and feed forward layer and modify the ratio number to improve the accuracy of the attention given to the input sequence.
* Reduces the number of encoder and decoder layers: It can reduce an excessive self-attention mechanism in both the encoder and decoder to ease the learning plateau in the model and also increase inference speed. In addition, reducing the decoder layers also increases the influence of the encoder output and helps to obtain a better output.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Modified Encoder** & **BLEU** \\ \hline sf-sf-sf-sf-sf-sf (Baseline) & 29.33 \\ s-sf-sf-s-sf-s-sf & 32.14 \\ s-sf-sf-sf (X-Transformer) & 46.63 \\ s-sf-sf-sf-sf & 46.72 \\ s-sf-s-sf & 46.62 \\ \hline \hline \end{tabular}
\end{table}
Table 2: BLEU scores on different arrangements of self-attention mechanisms and feed forward layers in the encoder.
# Wireless Sensor Networks for Smart Homes:
## 4 Performance Assessment
### Obtained Results
Considering the MRF24J40 MB at 2.4 GHz operating mode, the data rate is 250 kbps, while the initial _sleeping_time_ has been set to 5 s. The fuzzy controller, based on trapezoidal MFs, has been realized in the micro-controller PIC24F of the network controller, in C language, through the embedded code generator included in Matlab/Simulink. The network controller gets the battery level from each field device and, then, transmits a frame to them with the indication of the sleeping time. The retrieved results, summed in Table 3, are comparable to those collected through the Matlab simulations. It is useful to note that the data acquired in the testbed are lower because the power wasted by the devices in the idle mode has not been taken into account during the simulations, as stated previously. Nonetheless, using the solution introduced in this paper, the battery life extends considerably compared to both the case without FLC and the method of [PERSON] et al. even in the testbed scenario. These values prove the goodness of the proposed solution and that the purpose of this work is fulfilled. Additionally, the retrieved results reveal that the solution here proposed is flexible and efficient regarding the energy consumption control of battery-powered nodes in WSNs for smart homes that do not require expensive and complicated hardware and so can be implemented on low-cost Commercial Off-The-Shelf (COTS) devices.
# Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure
## 7 Algorithms and Protocols
### Inspection Path Planning  
Inspection path planning concerns the more detailed motion path plan determined from the waypoints guided by the inspection missions under the constraints of the environment such as obstacles e.g., vegetation. The inspection path planning process applies 3D models of the inspection target (or a part hereof). For example, an inspection task for a drone may use a bridge segment with a supporting bridge pillar and the model of the underneath side of the bridge deck to calculate the motion path.  
The method is designed as a sampling-based sequential optimization to calculate and optimize the inspection path [46].  
Figure 8 presents the data processing chain for inspection path generation. The inspection target represented as a 3D point cloud is used as the input of the data processing chain (Figure 9). It extracts the features of the inspection surface by processing the point cloud for data cleaning and normal vector estimation. Next, it uniformly samples the point cloud based on a voxel down-sampling method. Then, it is segmented based on the bounding box facets, and a graph is generated for each point cluster. The completeness of the inspection is guaranteed by the proposed traversal path search algorithm, which allows the drone to visit and inspect each area of the target sequentially. Given the inspection sequence, a sequential convex optimization is formulated to generate and optimize flight waypoints with sensor orientations subject to sensor limitations, efficiency, and safety requirements. The proposed method has been validated with a data set of a real bridge and a power pylon [46]. The result shows that the proposed method generates a complete and efficient inspection path within a reasonable time and shows linear scalability concerning the size of the inspection target.  
Figure 7: Example of a solution for the path planning problem with four drones reaching four inspection targets. The solution paths are shown in red. Drones are shown as orange circles, inspection targets as blue (power towers) and purple (bridges), and selected inspection targets are shown as green circles.
# Enhancing IoT Network Security Using Feature Selection for Intrusion Detection Systems
## 1 Introduction
This model is expected to be used as a reference for the implementation and integration of machine learning methods in network management related to IoT domain threats and security. The contributions of this study can be summarized as follows:  
* A comprehensive feature selection framework involving various feature selection methods to detect consistent, non-redundant, and relevant variables that help identify vulnerabilities in IoT environments. * Optimized feature set generation investigates the different thresholds for feature importance, combining features that appear in multiple selection methods, and generates a reduced set of features to enhance model performance. * Improving IoT network security by demonstrating that the reduced feature set significantly enhances the accuracy and efficiency of the multi-layer perceptron (MLP) classifier compared to the full feature set. These contributions mark a significant advancement in enhancing the accuracy and efficiency of anomaly-based IDSs, thereby securing IoT ecosystems against increasingly sophisticated cyber threats. The remainder of this paper is structured as follows. Section 2 reviews related work on IoT security and intrusion detection. Section 3 details the methods used for feature selection and dimensionality reduction. Section 4 presents the experimental setup and results, and Section 5 discusses the implications of the findings. Finally, Section 6 concludes the paper and suggests directions for future research.
## 8 System Design
### Onboard Processing Design, Software and Operations
#### 8.5.5 Payload Software  
The payload software performs two primary functions. First, it is used to decompose a task into a series of jobs that can be executed by the operating software running on the flight computer. Second, it performs processing of the data collected.  
The planning task flow, which is depicted in Figure 8, begins with the receipt of a planning task from the operating software. This task request may require data collection only or data collection and  
Figure 7: Operating Software Flow.  
processing. A set of jobs (including, if applicable, jobs to send the payload software a processing task) is created for the planning task and sent back to the payload software. Imaging jobs include a specific time, attitude and other parameters that the operating software requires to capture the required images.  
The payload software processing function also begins with a call from operating software. The payload software processing function can be called when data collection for a task is completed. Alternately, it may be used to re-process (or perform alternate processing on) pre-existing imagery.  
When initiated by the operating software, a service request is included. This includes both the type or types of services to perform and specific service performance parameters. In this case, the most important parameter is what data to utilize when performing mosaicking. One key technical objective of this mission is to determine how valuable inertial measurement unit (IMU) and global positioning system (GPS) data is to optimizing mosaicking. Thus, mosaicking can be performed unaided, with IMU and GPS data, with IMU data only or with GPS data only. A validation mode, which performs all four types of mosaicking for comparison, is also included. Super resolution (image resolution enhancement) can be utilized in conjunction with mosaicking or on its own. Similarly, image compression can be utilized with mosaicking and super resolution, mosaicking, super resolution or on its own. The filenames for the resulting files are sent back to the operating software for retrieval and transmission to controllers, when completed. Figure 9 depicts the processing function's operations.  
Figure 8: Payload Software Planning Task Flow.  
Figure 9: Payload Software Processing Task.
## 2 Materials and Methods
### MTT Assay  
To evaluate the cytotoxic effects of both power densities of LLLT on GMSC viability, an MTT assay was conducted. Figure 3 shows that the groups had no significant difference after 24 h. After 72 h, it was observed that both parameters of LLLT resulted in a significant increase (\(p<0.05\)) in cell viability. This indicates that LLLT did not have any negative impact on the viability of GMSCs. On day 7, the GMSCs treated with 1.5 J/cm\({}^{2}\) LLLT had a significantly higher absorption value (\(p<0.05\)) than the GMSCs treated with 3.5 J/cm\({}^{2}\) LLLT (Figure 3).  
Figure 1: The surface genes of GMSCs were analyzed using flow cytometry. CD73, CD90, and CD105 were found to be positively expressed, while the hematopoietic markers CD34 and CD45 were not expressed.  
Figure 2: (**a**) Morphological characterization of GMSCs at 4 th passage. The multilineage differentiation potential of GMSCs to (**b**) osteocytes (confirmed with Alizarin Red S stain), (**c**) adipocytes (confirmed with Oil red O stain), and (**d**) chondrocytes (confirmed with Alican Blue stain).
## 3 Results and Discussions
### Energy Efficiency
During the hot season, the dynamic insulation efficiency represents the quota of the heat flux removed by the air flowing inside the DSF channel compared to the total heat flux that enters through the outer glass pane. The results are presented in Tables 10-12 for the three ventilation modes (SET 1-3) when the velocities inside the channel were measured. The measurements of air velocities were performed at representative values of solar intensity and exterior and cavity temperatures. For lower values of solar radiation intensity and ambient air temperature and higher ones for the air flow rate inside the channel, the dynamic insulation efficiency has the highest importance. An average dynamic insulation efficiency of 71.3% was recorded for the natural ventilation with both openings on the lateral outer glass and the vertical one in the top of the channel (Table 12), relative to the first scenario with natural ventilation of 67.5% (Table 10) and the second one with forced ventilation of 50.9% (Table 11). Thus, in order to prevent the overheating of the facade during the hot season, combining vertical and horizontal openings in natural ventilation gives the best efficiency of the double-skin facade functioning.
# Search of the Shortest Path in a Communication Network with Fuzzy Cost Functions
## 6 Experimentation and Results
### Description of the Experiment

Table 1: Characteristics of the traffic used in the experiments with NTT network. \begin{table}
\begin{tabular}{l l} \hline \hline
**Description of the Parameters** & **Value** \\ \hline Call rate & Poisson Distribution with mean value \\  & 0.14 calls/s \\ \hline Call duration & Exponential Distribution with mean value \(\mu=60\) s \\ \hline Type of traffic & ON/OFF \\ \hline Used Bandwidth over an ON period & 30 Mbit/s \\ \hline ON period & Exponential Distribution with mean value \(\mu=1\) s \\ \hline OFF period & Exponential Distribution with mean value \(\mu=1\) s \\ \hline Connection type & Symmetrical and bidirectional \\ \hline Simulation time & 10,000 s \\ \hline Interval of time to update routing tables & 300 s \\ \hline Number of replications with different seeds & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characteristics of the traffic used in the experiments with NFSNET network. _OFF period_: Period without transmission, that is, without consumption of resources. We use the same exponential function with a mean of 1 s. We force an off period to guarantee the competition of the burst. _Connection type_: Given a connection, both nodes can be source and destination simultaneously with the same consumption of resources. Thus, the connection is symmetrical and bidirectional. _Simulation time_: It is the simulated time assigned to each experiment (do not confuse with the duration of the simulation). _Internal of time to update routing tables_: Interval of time used to measure the average occupation of links and update the routing tables. Higher values in this parameter imply higher uncertainty in the measurements. In our simulation scenario, source and destination nodes are randomly selected (with equal probability) among all the nodes in the network. Thus, there are multiple active source-destination pairs simultaneously. In the networks, our magnitudes of interest are the total number of sent and received bytes by each node at each simulation interval. From the previous values, we calculate the following metrics:  
* _Mean Delivery Rate (MDR)_: Total number of received bits divided by the total number of sent bits during an experiment. This value indicates the probability that a sent data is finally received. We use this value to obtain the GMDR. * _Global Mean Delivery Rate (GMDR)_: Mean of the MDR in ten experiments. * _Confidence interval_ (Computed with a probability of 0.95).
## 5 Discussion
### Limitations and Future Work  
This study reveals that residents and tourists do not have significant differences in their perceived behaviors regarding the same urban landscape. However, there are significant differences in people's perceptions of different landscape elements, and the aesthetic perceptions of different landscape elements have a significant effect on place identity, providing a new perspective for enhancing the sense of identity of the city. That said, there are some limitations. To enhance the universality and representativeness of these findings, it is essential to significantly expand the number of subjects. This should involve recruiting participants from diverse cultural backgrounds, various social and economic classes, and different geographical regions [35].  
Future work will need to explore the differences in participants' affective attitudes related to their own identity features in the environment in more depth. Previous research has found that men and women differ in their overall and localized focus on the environment, with women being more concerned with the overall image and men being more interested in specific localized areas [36]. In addition to eye-tracking, physiological sensing devices, GPS location devices, and human behavior recognition are all useful methods for capturing behavioral data. These behavioral metrics data can be processed by artificial neural networks, and visual quality models can be built to support decision-making. Therefore, future research will require deep intersections with other disciplines to provide data to inform landscape architecture theory development and guide urban overlook planning and management.
# Workforce Learning Curves for Human-Based Assembly Operations: A State-of-the-Art Review
## 4 Results
### Domain 2: Data Acquisition and Monitoring Technologies for Human-Based Assembly Operations  
The presented journal articles and conference papers are related to _data collection methods and monitoring tools applicable to manual operations_. These can be applied to cycle time monitoring, but are not limited to it, since other technologies to monitor additional factors could be adapted to monitor time. The main focus of this subsection is to understand what are the latest advancements in general data acquisition and monitoring technologies. 2.1 Human Factor Analyzer for Work Measurements of Manual Manufacturing and Assembly Processes by [PERSON] et al. (2019)  
The purpose of this journal paper by [PERSON] et al. (2019) is to present and demonstrate the function of a \"human factor analyzer\", a combination of software and hardware solution components consisting of a set of motion tracking cameras similar to the ones used in motion tracking for animated films, and a software program that digitizes the tracked operator movements to monitor his/her manual manufacturing and assembly process-tasks and gathered data related his/her movements' time, speed, and reach. [PERSON] et al. have proposed this _work measurement model_ since traditional measuring methods are often expensive, both in time and resources, and the reliability of the data depends on the skill and repeatability of the person responsible for the measurements. [PERSON] et al. first tested their proposed _human factor analyzer_ in a lab environment, and later in an industrial environment, an Italian welding factory containing a process mainly dependent on human operators (i.e., welders), both of which demonstrate the easiness and simplification of high-quality data collection. 2.2 Continuous Measurement of Muscle Fatigue Using Wearable Sensors during Light Manual Operations by [PERSON] et al. (2019)  
[PERSON] et al. (2019), in their conference paper, address the muscle fatigue measure and management in real-time by using surface Electromyography (sEMG) sensors. The first topic they cover is the selection of the type of sensors, given they found common sEMG sensors to be heavy and stationary, they chose wavebands with wireless capabilities to monitor the fatigue. Another interesting approach from the authors is that, as mentioned by them, previous studies measured the signal during work breaks, and not in real-time during the operation during the production day. To validate if any correlation existed between perceived fatigue of the operator with real fatigue measured with the sEMG, the authors used _Borg Rating of Perceived Exertion_[33] where twelve operators rated their fatigue on different parts of the arm as well as using the sensors to objectively measure their fatigue. The results showed no correlation between the two variables, which the authors attribute to different possible psychological reasons that are not assessed in the sensor measurement.