esa-sceva/satcom-chunk-collection huggingface dataset:
# Signal to Noise Ratio of a Coded Slit Hyperspectral Sensor
## 3 Results and Data Analysis
### The SNR of the Visible-NIR Sensing  
This section concerns with the imaging performance of the VNIR sensor system but a representative detector array for the VNIR imaging system is not that obvious, due to the wide variety of detectors in this spectral range. In here, the Photonfocus MV1-D1312 IE-240-CL FPL which has been widely utilized for most of the mid-range commercial off-the-shelve hyperspectral systems, has been selected in this experiment. The efficiency of the spectral grating has been configured to approximately the same as that of the SWIR imager, but a different blazing wavelength at 600 nm has been utilized here. The objective lens has been configured a wavelength-independent transmission of 98% in this VNIR system.  
It is seen from Figure 10 that the multiplexed sensing performs better than that of the conventional one for the frame rates above \(\sim\)60 Hz when the signal level is low (i.e., the 10% albedo plot in Figure 10). When the signal level is high (i.e., the 50% albedo case) the conventional system still performs much better than the multiplexed one. It is noted that the SNRs of the VNIR are a lot worse than the two systems (i.e., the SWIR and the integrated systems) presented in the previous two sections.  
Figure 9: Shows the SNR behavior of the conventional spectrograph sensing vs. the CA multiplexed sensing for the SWIR sensing system, at an illumination of 60\({}^{\circ}\) zenith.
# A Multi-Service Adaptive Semi-Persistent LTE Uplink Scheduler for Low Power M2M Devices
## 3 Multi-Service Adaptive Semi-Persistent Scheduling Algorithm
### Multi-Service Adaptive SPS Algorithm
On the other hand, with the adaptive SPS algorithm, by issuing a semi-persistent adaptive allocation with an appropriate value of \(T_{SPS,S}\) in accordance with the corresponding \(T_{PDB,S}\), the scheduler ensures a high statistical probability of meeting the delay constraints even for packets with small delay budget. The pre-emptive function is based on these phenomenon. Some traffic classes with small and strict delay budget may have priority over other classes with higher and less strict delay budget values. Due to this prioritization, if there is not enough capacity left to admit a new adaptive SPS request with higher priority, the packet scheduler searches if there are any SPS resources designated for traffic class(es) with lower priority. If sufficient resources to support the new request are found and they are already in use by any lower priority UE(s), these allocations are pre-empted and higher priority traffic is admitted and their new SPS grant is sent accordingly. If the pre-emption attempt is unsuccessful due to resource insufficiency, the current allocations are unchanged and the new request is scheduled dynamically. Another approach might be modification of the control parameter \(f_{S}\) of the ongoing adaptive SPS allocations of lower priority traffic class(es), by assigning higher values of \(f_{S}\) such as \(0.5\leq f_{S}<1\), so that they are allocated a range of longer SPS periods with a tolerable range of QoS degradation level.
## 3 Magnetic Field Intensity Measurement
### Field Orientation Detection  
The measurement of the induced EMV in the coils of the 3D sensor was not enough to determine the EMF, because it only established the magnitude (modulus) of the field strength, and not the field sense. This could be determined from the phase shift--in the phase or antiphase--between the induced EMV in each coil of the 3D sensor and the WPT emitting coil voltage.  
The solution for determining the phase shift (\(0^{0}\) or \(180^{0}\)) was to measure, at the same time as the induced EMV (U1 ca), the sum of the induced EMV and the WPT voltage (also via an rms-to-dc converter), as shown in Figure 15.  
The phase detection procedure (phase/antiphase) was carried out according to the following algorithm:  
* Switch I opens; voltage U1 CA and U1 CC are near to 0 V (only a small value due to component noise).
* The amplification in A2 is adjusted so that the voltage U3 CC (resulting only from the signal at the WPT emitting coil) is approximately half-scale.
* The value of the U3 CC voltage is stored.
* Switch I is closed; the voltage U1 CC represents the induced EMV.
* A threshold level \"U\({}_{\text{P}}\)\" is calculated as the maximum value between the stored voltage value U3 CC and the voltage value U1 CC.
* The voltage value U3 CC is compared with the threshold; if U3 CC > U\({}_{\text{P}}\), then it is concluded that the signals are in phase and vice versa.  
The procedure described above had the advantage that it correctly determined the phase in the conditions under which the induced EMV swept the entire accepted dynamic range, and it did not generate errors even if the induced EMV was very small (or surrounded by noise).
###### Abstract  
The construction of intelligent logistics by intelligent wireless sensing is a modern trend. Hence, this study uses the multistate flow network (MFN) to explore the actual environment of logistics delivery and to consider the different types of transportation routes available for logistics trucks in today's practical environment, which have been neglected in previous studies. Two road types, namely highways and slow roads, with different speed limits are explored. The speed of the truck is fast on the highway, so the completion time of the single delivery is, of course, fast. However, it is also because of its high speed that it is subject to many other conditions. For example, if the turning angle of the truck is too large, there will be a risk of the truck overturning, which is a quite serious and important problem that must be included as a constraint. Moreover, highways limit the weight of trucks, so this limit is also included as a constraint. On the other hand, if the truck is driving on a slow road, where its speed is much slower than that of a highway, it is not limited by the turning angle. Nevertheless, regarding the weight capacity of trucks, although the same type of trucks running on slow roads can carry a weight capacity that is higher than the load weight limit of driving on the highway, slow roads also have a load weight limit. In addition to a truck's aforementioned turning angle and load weight capacity, in today's logistics delivery, time efficiency is extremely important, so the delivery completion time is also included as a constraint. Therefore, this study uses the improved d-MP method to study the reliability of logistics delivery in trucks driving on two types of roads under constraints to help enhance the construction of intelligent logistics with intelligent wireless sensing. An illustrative example in an actual environment is introduced.  
intelligent wireless sensing; intelligent logistics; reliability; multistate flow network (MFN); d-MP 2022
###### Abstract  
Internet of things (IoT)-based wireless communication technology has been applied for efficient work and safety in mines. However, underground mines are surrounded by walls and have numerous curves, which reduce communication stability. For smooth communication between devices, a line of sight (LOS) must be connected without obstacles. If optimal installation locations in a virtual space can be confirmed before installing the device in the field, trial and error can be avoided. In this study, a 3D model-based LOS analysis technology was developed using Python and a ray-casting algorithm. A place with numerous LOS connections has good communication with other places; consequently, it is a suitable location to install the device. To indicate the degree of communication smoothness, a smooth communication index was proposed. A preliminary experiment was conducted in an indoor space within the Samcheok Campus of the Kangwon National University, and a field experiment was conducted at the Samdo Mine in Dogye-eup, Samcheok-si, Gangwon-do. Based on these results, an effective wireless sensor network (WSN) was established by installing a ZigBee-based monitoring device. The results of this study can be further improved and used for constructing smooth WSNs in underground mines in the future.  
line of sight; underground mine; three-dimensional model; Internet of things; smooth communication index
# Performance Evaluation Method for Intelligent Computing Components for Space Applications
## 3 Evaluation Method
### Typical Application Benchmarks in Space
#### 3.4.1 Image Calibration Application Benchmark  
Image calibration has many important applications in the field of remote sensing and other space domains. Calibration can reduce noise, artifacts, and geometric distortions in images, and improve the color consistency and geometric accuracy of remote sensing images, thereby improving the quality and accuracy of remote sensing images and helping classification algorithms better accomplish their corresponding recognition and classification tasks. The thermal infrared band in remote sensing images can be used to estimate surface temperature. Image calibration can eliminate radiometric disturbances and non-linear sensor responses in images, thereby improving the accuracy of surface temperature estimation.  
In remote sensing applications with panchromatic sensors, image calibration is needed for images captured using imaging instruments on deep space exploration telescopes that require long exposures. Typically, to overcome the limitations of the sensor, multiple frames of images need to be acquired from the front end, and then they are overlaid and summed to form the final image. Before stacking the images, each frame of acquired images needs to undergo related preprocessing operations, namely image calibration testing. The specific steps are as shown Figure 3. When executing the benchmark experiments, three basic parameters of the image, \"IMAGE_FRAMES, IMAGE_WIDTH and IMAGE_HEIGHT\", need to be entered.
# Fault Feature Extraction of Parallel-Axis Gearbox Based on IDBO-VMD and t-SNE
## 6 Experimental Verification
### The Extraction of Fault Signal Features
The optimization facilitated by IDBO improves the robustness of VMD, effectively suppresses modal aliasing, and rapidly identifies the optimal values for parameters K and \(\alpha\). The fitness value used for testing is the minimum envelope entropy. The smaller the value, the less noise contained in the IMFs obtained from VMD, allowing for the extraction of more fault features. Therefore, IDBO enhances the robustness of VMD, effectively suppressing modal aliasing, and rapidly determining the optimal values for parameters K and \(\alpha\). This contributes to the improved ability to extract fault features by minimizing noise interference in the VMD decomposition process. Taking the surface crack fault signal as an example, the iteration curve of the VMD parameter optimization process is shown in Figure 6. After 10 iterations, the global optimal solution was found, resulting in the minimum envelope entropy of 4.2092. The optimal solution corresponds to parameters \(\alpha\) = 741 and K = 5. Subsequently, using VMD processing, the signals and spectra corresponding to the five intrinsic mode functions (IMFs) of the surface crack fault signal are shown in Figures 7 and 8. The optimal parameter combinations for VMD were determined for the five different states, as shown in Table 3.  
Based on the parameter combinations [K, \(\alpha\)] from Table 3, the VMD method was configured with the appropriate values of K and \(\alpha\), and this was used to decompose the samples into multiple intrinsic mode function (IMF) components. Table 4 presents the kurtosis values for each IMF corresponding to each state. Larger kurtosis values indicate greater impulsive characteristics in the components and, therefore, more distinctive features within the fault signal. The four IMFs with the highest kurtosis values were selected for each state, and 20 vibration features were extracted from each, resulting in a total of 80 features for each segment of the vibration signal, forming high-dimensional feature vectors.
# Region-Based Automated Localization of Colonoscopy and Wireless Capsule Endoscopy Polyps
## 2 Materials and Methods
### Data Augmentation  
Deep learning models such as CNN require voluminous data to train the model without overfitting. This is the biggest challenge in the biomedical images [38]. The data that is available is limited, and most of them are raw images without annotations. This problem is overcome by applying data augmentation to the input images and the corresponding ground truths. In colonoscopy and WCE videos, polyps show variations in size, shape, color, and location. With these variations, it is best to generate duplicate data from the available image data by flipping, rotating, changing the scale, shearing, and blurring the images. These sets of augmentation are applied on 50% of each mini-batch of the input data before training. The remaining 50% data are left undisturbed.  
Augmentation methods were chosen according to the appearance of the polyp images. Polyps change in shape and size, so image scaling and shear transform helped generate more data from the same image with different transformations. The images are rescaled from a range factor of 0.8 to 1.2 and shear transforms from a range between \((-4,A)\). Polyps also appear to be in different locations; to encounter these locations, the images were flipped and rotated through a range between angles of \((-180,180)\) degrees. Frames extracted from videos have the problem of motion blur, which lowers the quality of the image; to make the model generalize, Gaussian blur was applied on some of the images with a standard deviation varying from 0 to 1. Polyp images also have variations in brightening and darkening. To handle this problem, histogram equalization was applied on the images. We found from the experimental observations that histogram equalization degrades the performance of the model. This may be because the model finds it difficult to extract features from images that have enhanced contrast. These adjustments in the image intensities make the model differentiate the object pixels from the background. It is important to have a good quality image without losing the characteristics of the image while increasing the dataset through data augmentation.
# Transforming Customer Digital Footprints into Decision Enablers in Hospitality
## 3 Materials and Methods
### Emotion Modelling
#### 3.2.1 Hotel Aspect Extraction Using Hybrid Topic Modelling  
The customer's hotel experience is closely linked to various key aspects of the hotel, as outlined in the World Tourism Organization's (WTO) hotel classification report [39]. To identify these points of interest, we created an aspect model that encompasses essential facets such as \"Room\", \"Bathroom\", \"Front Desk\", \"Location/Access\", \"Public Areas\", \"Food & Beverage\", \"Exterior\" and \"Staff\". Contemporary research studies have used the aspect-based online review categorization to understand different topics using field-specific lexicon [40], frequency-based aspect topics [41] and syntactic features of the text [42]. In this study, we extract hotel mentions using a hybrid approach, combining semi-supervised topic modelling and keyword extraction through NLP techniques to enhance accuracy.  
To initiate the keyword extraction process, we developed a vocabulary specific to each aspect, consisting of various properties associated with them. By expanding this vocabulary using the trained Word2 Vec model, we generated a finely tuned aspect dictionary, including synonyms for each aspect. Additionally, to systematically widen the scope of aspect extraction, we incorporated a semi-supervised topic modelling approach, employing Guided Latent Dirichlet Allocation (GuidedLDA) [43]. GuidedLDA utilizes the initial seed  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Emotion** & **Examples of Emotional Expressions** \\ \hline Joy & [“happy”, “enjoyed”, “delighted”, \"beautiful”, “tasty”,...] \\ Anger & [“angry”, “dirty”, “disturbed”, \"irittated”, “noisy”,...] \\ Sad & [“sad”, “depressing”, “miserable”, “poor”, \"worried”,...] \\ Surprise & [“surprised”, “amazed”, “shocked”, “astonished”, “stunned”,...] \\ Fear & [“scared”, “dangerous”, “fear”, “afraid”, \"horrified\",...] \\ Trust & [“ensure”, “trust”, “make sure”, “belief”, “certain”,...] \\ Anticipation & [“look forward”, “anticipate”, “expect”, “predict”, “hope”,...] \\ Disgust & [“smelly”, “disgusting”, “rotten”, “smell”, “filthy”,...] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Excerpt from the emotion vocabulary developed for emotion extraction.  
Figure 2: The high-level process of emotion extraction. The emotion model is from [38].  
word list, comprising the previously extracted keywords, to categorize each review into potential topics. Figure 3 illustrates the high-level process of aspect extraction applied in this study.
## 2 The Network Architecture  
### In-Vehicle Network Architecture  
Figure 1 shows the network topology in a carriage, and the in-vehicle network includes three types of nodes: service nodes, cluster head nodes, and sink nodes.  
1. Service nodes. Service nodes are responsible for bearing different services. Generally, urban rail transit services can be divided into security and non-security services. Security services are essential for the safe operation of the train, such as the operation control service, video monitoring service, emergency text service, and operating conditions monitoring service. Non-security services generally refer to passenger services with lower latency requirements, such as web browsing, video calls, and online games [18]. In this paper, we classify the services into three priorities according to the delay and reliability requirements: strict security service, sub-security service, and non-security service, as shown in Table 1.
2. Cluster head nodes. As mentioned above, cluster heads are responsible for forwarding the service data and managing the cluster members, usually selected by a selection algorithm. In this study, we elected cluster heads considering comprehensively the factors affecting the time delay and packet loss rate. In addition, we also set a low-delay queuing strategy for the cluster head nodes. Under this strategy, cluster heads can always forward the high-priority services first.
3. Sink nodes. Sink nodes are the superior nodes of cluster heads. They are deployed at the carriage's top center, responsible for receiving data from cluster heads and then forwarding information to the trackside system. In the ad hoc network, each node works according to the method of time-division multiple access (TDMA). The sink nodes are responsible for issuing the schedule to avoid resource collision and network congestion.
## 4 Advanced Persistent Threat Life Cycle Analysis
### Four Stage Attack  
The intrusion kill chain (IKC) is a four-stage model that identifies the behaviours and purposes of an APT attack [7]. The stages are described below:  
1. Information collection: In this initial stage, the recognition of the network is made using scanning or social engineering tools.
2. Intrusion: In this stage spear-phishing techniques, malicious email attachments or backdoors are used to obtain access privileges.
3. Latent expansion: The attacker attempts to maintain control in order to obtain data that will allow the attacker to continue with the expansion within the network.
4. Information theft: The attacker establishes a connection to a server, and the stolen data is transferred. Encryption techniques can be used to camouflage the extracted data.  
Another approach to the four-stages life cycle was detailed in [61]. In this approach, the stages are described as follows:  
1. Initial compromise: The techniques used are social engineering and spear-phishing.
2. C&C: A communication channel is established between a committed server and the target.
3. Lateral movement: Attackers seek to collect internal information and move between hosts with critical vulnerabilities.
4. Attack achievement: The attack is completed, and the theft of sensitive information begins.
## 3 Stability Preserving Criterion
### Migration of RAS and BA  
The effects of the proposed power reduction function on RASs and BAs of the system are depicted in Figures 10 and 11, respectively. As expected, both RAS and BA shift along the \(v\)-\(i\) plane, as \(e_{\%}\) and \(p\) diminish. Moreover, their area remains constant, due to the stability index invariance (\(\Delta\) = 0.516) guaranteed by applying the proposed power reduction function.
## 2 Materials and Methods  
### Study Area  
This study is performed across China, which covers a vast territory (Figure 1). Because of conspicuous spatial differences in temperature, precipitation, and topography, land cover types over China show evident spatial heterogeneity. China can be divided into four climate regions according to the aridity index (the ratio between evapotranspiration and precipitation): humid, semi-humid, semi-arid, and arid regions [33]. The humid regions (mainly located in the south of China) are dominated by forests and savannas; the semi-humid regions are primarily covered by croplands and grasslands; the semi-arid regions are dominated by grasslands; and the arid areas are mostly barren (Figure 1).  
To reduce the uncertainty of vegetation cover change, only the areas where the land cover type has not changed from 2001 to 2020 were selected for analysis. Specifically, the images of land cover in 2001 and 2020 were compared and subtracted. If the pixel value was equal to 0, it was assumed that the land cover type remained unchanged during the study period, and vice versa. In addition, the regions with non-vegetation cover (including water bodies, permanent wetlands, urban and built-up lands, permanent snow and ice, and barren) or shrublands cover (less than 100 pixels in the whole study area) were masked, i.e., excluded from this study. The final selected study area is shown in Figure 1. Within the selected study area, there are seven main vegetation types, namely evergreen forests, deciduous forests, mixed forests, woody savannas, savannas, grasslands, and croplands (Table 1). Among these, grasslands occupy the largest area, while mixed forests occupy the smallest area, accounting for 48.6% and 2.2%, respectively.  
Figure 1: Spatial distribution of vegetation types in the selected study area across China (the white area represents the pixels that were excluded for further analysis, including land cover type that has changed from 2001 to 2020, non-vegetation cover, and shrublands cover).