esa-sceva/satcom-chunk-collection huggingface dataset:
# Overcoming Adverse Conditions in Rescue Scenarios: A Deep Learning and Image Processing Approach
## 2 Materials and Methods
### Pipeline Design  
The pipeline, as it is represented in Figure 1, comprises three main modules, each playing a specific role in improving the quality of the final image. We designed the first two, Exposure Correction (EC) and Exposure Segmentation (ES), to work in parallel. Simultaneously advancing the flow enabled us to boost all the steps required to reach the final module. EC aims to adjust the exposure of acquired images to an optimal level, using a DL method that automatically adapts to different lighting conditions. The isolation of Regions Of Interest (ROIs) based on their exposure levels is achieved by ES through the application of IP techniques such as global thresholding and edge detection. ES allows the system to process each ROI independently, ensuring that the final image contains only the information of interest. Finally, the last module, Image Fusion (IF), combines both EC and ES results to produce the final outcome. IF takes as input the information of all ROIs and corrections collected from the previous stages and fuses them to create a single image with an optimal exposure level.
## 3 Results  
### Platform Resources Usage  
Table 1 shows the resources occupied by the static partition and the reconfigurable modules in terms of LUT, BRAM and DSP, as well as the estimated power consumption for each of these partitions. Figure 8 gives the estimated power consumption for the platform in the different supported modes. Among the reconfigurable partitions, the most resource- and energy-consuming are the SPHINCS\({}^{+}\) IP and the AES IP. In Safe mode, the resource usage overhead compared to the High-Performance mode will be high, but these modules have the significant advantage of being implemented in hardware [18]. These numbers also show that, in this case, the impact of the dynamic and partial reconfiguration to reduce the overall power consumption of the platform would be minimal. Occupying the largest share of energy consumption is the static partition, especially the processing system which is responsible for more than 90% of the power consumption.
# A Heuristic Fuzzy Based 5G Network Orchestration Framework for Dynamic Virtual Network Embedding
## 3 System Model and Mathematical Background  
### Physical Infrastructure and Network Request Model  
The undirected graph is used to represent both the physical infrastructure and network request model in the proposed work. \(G^{PI}=(N^{PI},E^{PI})\) denotes a graph, where \(N^{PI}\) denotes the number of nodes and \(E^{PI}\) denotes the number of edges. Subsets for nodes and edges are also defined. The node subset includes CPU capacity and Security Level for each node, which are represented as \(CPU_{i}^{PI}\) and \(SL_{i}^{PI}\), respectively, whereas the edge subset includes bandwidth, which is represented as \(BW_{ij}^{PI}\). In the definitions, \(CPU_{i}^{PI}\) and \(SL_{i}^{PI}\) define the CPU capacity and the security level of the \(i\)-th node of the PI, and \(BW_{ij}^{PI}\) defines the bandwidth of the link connecting nodes '\(i\)' and '\(j\)' of PI.  
A graph for the Network Request Model is defined as a graph \(G^{NR}=(N^{NR},E^{NR})\), where \(N^{NR}\) denotes the number of requested nodes and \(E^{NR}\) denotes the number of requested edges. Similar to the PI, the nodes and edges of NR have subsets \(N^{NR}(CPU_{i}^{NR},SR_{i}^{NR})\) and \(E^{NR}(BW_{ij}^{NR})\), respectively. In the definitions, \(CPU_{i}^{NR}\) and \(SL_{i}^{NR}\) define the CPU capacity and the security requirements of the \(i\)-th node of the NR, and \(BW_{ij}^{NR}\) defines the bandwidth requirement of the link connecting nodes '\(i\)' and '\(j\)' of NR.
## 2 Materials and Methods
### Training and Validation  
Training data was from datasets Platesmania, UFPR-ALPR, and AOLP. Validation/testing data was from AOLP, UFPR-ALPR, CaltechCars, OpenALPR, and EnglishLP databases. Data distribution between training and validation was based on the distribution given in paper [5]. The data used for training and testing had marked license plates, and all plates had their four corners marked to test rotation and the second part of detection. All data were stored in the TFRecord format, with the image stored as an integer within the range of [0, 255]. It was stored with compression that did not affect the quality of the image. Additionally, a 32 \(\times\) 32 \(\times\) 5 matrix with ground truth data was stored, which was generated from the marked plates. For the precise license plate detection step, images were stored in the same format, except that they were already cropped, containing only candidate license plates and an additional vector of nine elements. The elements consisted of 4 \(\times\) 2 + 1 X and Y coordinates of the license plate corners and a confidence score that indicates whether it is a license plate or not. Rotation, distortion, magnification, reduction, center modification, merging, and color processing augmentation was performed on all the training data. To simulate smaller license plates in traffic scenarios, multiple image combination augmentation was applied, and validation data was left unmodified.  
\"Area\" resizing interpolation was used both for training and validation data. To maintain the same conditions for all experiments, the same training strategy was applied: the first 50 epochs use \"Adam\" optimizer with learning rate 1 \(\times\) 10\({}^{-4}\), \(\beta_{1}\) = 0.9, \(\beta_{2}\) = 0.999, \(\varepsilon\) = 1\(\times\)10\({}^{-5}\), and amsgrad = true. Then, SGD optimizer with cyclic learning rate and base = 1 \(\times\) 10\({}^{-3}\) is used. The model training process stops when the test loss does not improve for 10 consecutive epochs. After that, the learning coefficient is reduced ten times and the training process is repeated twice. Accuracy of the license plate number detection algorithm is evaluated using two metrics:  
* Object detection check. IoU (Intersection Over Union) is used with a threshold of 0.4. It evaluates predicted object coordinates with real coordinates.
* Object coordinates error check. Euclid distance was calculated between every original point and predicted point (1). \[err=\frac{\sqrt{\left(x_{p}-x\right)^{2}+\left(y_{p}-y\right)^{2}}}{\sqrt{ \left(x_{max}-x_{min}\right)^{2}+\left(y_{max}-y_{min}\right)^{2}}}\] (1)  
The system's speed was calculated using TensorFlow 1.15 with an NVIDIA GPU 1080 Ti. To simulate real-life scenarios, the neural network's speed was tested with a group size of 16, allowing for the simultaneous processing of 16 images. This process was repeated 100 times and the average single processing speed was calculated. Finally, the single image processing time from all 16 images was computed. The speed results are presented in frames per second or milliseconds.
# Novel Instrument for Clinical Evaluations of Active Extraocular Muscle Tension
###### Abstract  
Strabismus can be caused by abnormal tension of the extracellular muscles (EOMs) attached to the eyeball in superior, inferior, lateral, medial, superior oblique, and inferior oblique positions. Evaluating the tension in each EOM is crucial for surgical planning in strabismus, which is conducted by adjusting the tension on the EOM. The purpose of this study was to develop a compact measuring device to non-invasively evaluate the active EOM tension. The proposed device employed a cotton-tipped medical swab to transfer the EOM tension connected to the force sensor as a non-invasive medium. The tilting angle of the swab and the force of active EOM tension were wirelessly transferred to a laptop computer for recording and real-time displaying of the measured values. The active EOM tensions for the four recti muscles were 101.7 \(\pm\) 15.0 g (mean \(\pm\) SD) for the lateral rectus; 88.0 \(\pm\) 15.4 g for the medial rectus; 61.3 \(\pm\) 6.8 g for the inferior rectus; and 121.3 \(\pm\) 38.5 g for the superior rectus. These values were higher than the reported values of 45-60 g measured in previous studies. In the previous studies, however, the EOM was detached from the globe and attached to a strain gauge, and, thus, there were no passive elastic forces from ocular connective tissue, resulting in lower values compared with the current study. The previous methods were also complex and not suitable for clinical measurement. Thus, the proposed method, which is non-invasive and mimics the conventional force generation test with a cotton-tipped swab, could facilitate the evaluation of active EOM tension, both clinically in strabismus management and in research into understanding its pathophysiology.  
Keywords:extracular muscle; force generation test; strabismus; tension measurement +
Footnote †: journal: Applied Sciences
# Computationally Efficient Channel Estimation in 5G Massive Multiple-Input Multiple-output Systems
## 4 Proposed Algorithm
### Complexity Analysis  
According to the number of real multiplications required in the algorithm, the computational complexity of the SDGS detection algorithm proposed in this paper was analyzed. Since all linear MMSE detection algorithms and the proposed algorithm must calculate the filter matrix, \(\mathbf{W}=\mathbf{G}+\mathbf{I_{2k}}\), and the matched filter signal, \(\hat{\mathbf{y}}=\mathbf{H^{H}y}\), then only the other parts were analyzed for complexity, mainly using the following three parts of the composition.
## 4 Discussion
### Comparison of the Land Cover Results Obtained in This Study with Existing Land Cover Products  
In this research, specific land cover classifications were performed, and then compared with the existing global high-spatial-resolution products in the QLM. The land cover results obtained in this study had higher spatial resolutions and classification accuracies. Moreover, the identification and classification of the land cover types in a relatively small area and the boundaries between the land cover types were more accurate. Nevertheless, the classification results obtained in this study were different from the existing global high-spatial-resolution land cover products in terms of consistency. The data sources, classification algorithms, and feature variables involved in the classification were different, which were the main reasons for the differences in the classification results. Optical remote sensing imagery has the advantages of long-term coverage and wide coverage, and it is the main data source for land use classification research. Different data sources have different spatial and temporal resolutions, which leads to uncertainties in the classification results and inconsistencies between the different land cover products. In the existing high-spatial-resolution land cover products and land cover studies, most products used Landsat images with a resolution of 30 m as the data source, which was different from the Sentinel-2 images used in this study. For example, the GlobeLand30 developed by [PERSON] et al. used Landsat TM/ETM+ data from 2000 and 2010, supplemented by HJ-1 satellite data [16]. For the FROM-GLC30 developed by [PERSON] et al., 8929 Landsat TM/ETM+ scenes of the green season from 1984 to 2011 were collected from various sources [17]. Based on the Landsat 8 surface reflectance imagery from 2014 to 2016, [PERSON] et al. developed global land cover products, including GLC_FCS30-2015 [19]. Second, in addition to the supervised classification methods that are commonly used, some new methods have also been proposed and used in land cover classification. For example, [PERSON] et al. [61, 62] proposed a new time series land cover mapping method based on machine learning and applied it to the development of land cover products in the Qilian Mountains. The Chinese land cover products developed by [PERSON] et al. were based on a new SPECLib-based operational approach proposed in their research [27]. The pixel-based, object-oriented, and knowledge fusion (POK)-based classification methods were used with the GlobeLand30 dataset [16]. Therefore, different classification algorithms will also result in differences in the land cover results.
# Time Modulated Arrays: From their Origin to Their Utilization in Wireless Communication Systems
## 2 State of the Art
### TMA Design under an (Exclusive) Antenna Perspective  
Most of the research works involving TMA have been developed under an antenna designer point of view. More specifically, they focus on the synthesis of an appropriate shape of the radiation pattern. We therefore classify such works depending on the patterns that are synthesized, i.e., fundamental or harmonics.
## 3 Experimental Setup
### Feature Extraction  
Nine time-domain features (i.e., RMS, variance, skewness, kurtosis, shape factor, crest factor, entropy, histogram upper, and histogram lower) are extracted from four sets of vibration data collected in 139 days. The example plot of nine features from accelerometer #1 (radial) is presented in Figure 4. It can be seen from Figure 4 that not all features represent the degradation condition of the slew bearing. Kurtosis, variance, and histogram lower features are more sensitive to the bearing condition than the other six features. Focusing on the RMS, variance, kurtosis, and histogram upper and histogram lower features, they show a sudden peak on day 90. This is due to the fact that coal dust has been inserted into the bearing (on day 58) and the roller and raceway have an incipient defect.
## 4 Experimental Analysis  
### Experiment Description  
In order to verify the effectiveness and performance of the proposed \(E^{2}ISA\), which aims to solve the aviation equipment EMC problem, a group of environmental interference suppression experiments was conducted in an open area, operating at a VHF (approximately 100 MHz), as shown in Figure 5.  
Two Agilent 9020A signal analyzers [24] were used to acquire the near and far EMC signals by connecting to two omnidirectional antennas with a frequency scope of 20 MHz to 500 MHz whose gains are the same at different directions. The test frequency covered the FM broadcast frequency band to generate external far-site interference signals. Near-site interference signals were generated by a Agilent 8267D PSG Vector Signal Generator [25], which generated near-site interference signals of different frequencies. Before testing, it was necessary to perform the calibration by placing the near-testing and far-testing antennas together to eliminate the offset of the different equipment. Similar to the literature [10], all antennas in our experiments remained stationary during each test.  
The one-meter and three-meter methods were both used in the tests. In the one-meter test, the near-site antenna was placed one meter away from the radiating antenna, and the far-site antenna was located nine meters away from the radiating antenna. In the three-meter test, the near-site antenna was placed three meters away from the radiating antenna, and the far-site antenna was placed nine meters away from the radiating antenna.  
After data acquisition, we selected 129 waterfall images at random as the training set, which was labeled by the \"labelme\" tool, as shown in Figure 6. Horizontal and vertical reflections were carried out on the training set for data augmentation.
# QuietPlace: An Ultrasound-Based Proof of Location Protocol with Strong Identities
## 1 Introduction
### Definitions and Participating Entities  
In the following we provide an overview of the terminology, definitions, and types of participating entities and attacks as they will be used throughout the paper.  
* A 'proof of location' (PoL) is a digital certificate that validates a device's location at a specific time. A proof of location may consist one or more location proof segments.
* A 'location proof segment' (LPS) is a piece of information that is produced by a device and validates or cancels the location claim of another device.
* The 'participants' are the devices that participate in the location proof system. Some participants (i.e., smartphones) can have many roles, while other (i.e., a certificate authority) can have only one. The participants are referred to via their role in the system. In our protocol, the roles of the participants are: prover, witness, certificate authority, and verifier. The 'users' are the human owners of the devices. The 'prover' is the device that wants to obtain a PoL certifying its existence at a particular place on a particular time. The 'witness' is a device near the prover that certifiesthat the prover is indeed in its proximity, when it receives a request from the prover. Witnesses may belong to users, something that makes them untrusted. They can also be owned by a verifier or a certificate authority (CA) and thus be trusted. The'verifier' is the entity that the PoL created between the prover and the witnesses is addressed to. Usually, it is a location-based service. The 'certificate authority' (CA) is responsible for keeping a list of the participants in the system and validating their identities. It can also implement a trust schema by ranking the users according to their behavior.
* With the term 'identity', we refer to the unique identifier of each participant. This is expressed with the use of unique key pairs, related to a username by the CA. If a participating device belongs to a user (i.e., a prover or witness), the identity is linked to the user, who is registered with credentials (username/password) to the CA and the location-based service (verifier).  
Proof of location protocols may utilize additional entities that are required for their operations. The proof of location server is used in some protocols such as [5]. It acts as an intermediate that receives proofs from provers and is responsible for transfering them to the verifiers. We do not consider a server in our protocol. In a typical PoL protocol, the prover collects LPSs from nearby witnesses that can be either decentralized nodes [4] (i.e., other users) or part of the infrastructure [14]. The witnesses may also provide the prover with a location [3]. The prover then creates the PoL and can present it either directly to the verifier or after the PoL is checked by another entity such as a CA or a server [5]. Other protocols such as [15] assume that the verifier is near the prover, acting as a witness that just verifies the prover's location.
## 3 Results and Discussion  
### Vcc  
Table 1 shows the changes in VCCs in yellowtail muscle during storage at different temperatures. The counts of general viable bacteria, Enterobacteriaceae, _Aeromonas_ spp., heterotrophic marine bacteria, and _Pseudomonas_ spp. in OM and DM stored under all storage conditions increased significantly and reached 7 Log CFU/g, which could be prolonged as the storage temperature decreased. Although _B. thermospnacta_ and H\({}_{2}\)S-producing bacteria in refrigerated or ice-stored OM and DM propagated significantly, their counts did not exceed the initial spoilage levels (7 Log CFU/g). However, there was no significant increase in the abundance of these bacteria at either muscle site during the SC storage. In the present study, microbial growth was assumed to follow first-order reaction. Therefore, at each temperature, the linear growth rate was calculated using the portion of storage days and the logarithm of VBC in a linear relationship, as shown in Table 2. Lactic acid bacteria that showed no obvious growth during storage were excluded. The growth rate (Log CFU/g/day) of each bacterium ranged from 0.42 to 0.77 in refrigerated storage, 0.19 to 0.35 in ice storage, and 0.02 to 0.16 in SC storage. This indicates a significant temperature-dependent inhibition of the growth of each bacterium during storage. Therefore, storage at lower temperatures, especially SC, can slow the propagation of mesophilic bacteria and various spoilage-causing bacteria in the yellowtail muscles. In greater amberjack fillets stored at \(-3\)\({}^{\circ}\)C to 20 \({}^{\circ}\)C, bacterial growth was suppressed by storage at lower temperatures [32]. Additionally, SC storage effectively slows bacterial growth and extends the shelf life of fishery products [33]. These results are consistent with our findings.
## 3 Results
### Experiment 1: Analgesic Effect of BO
**(A)** Representative images of immunofluorescence staining in each group (red, pERK1/2; blue, nucleus). Upper images; magnification: \(\times\)200, Scale bar = 150 \(\upmu\)m. Lower images; magnification: \(\times\)600, Scale bar = 50 \(\upmu\)m. White arrow (\(\
earrow\square\)) indicates a pERK1/2-positive cell. **(B)** The numbers of pERK1/2-positive cells. Each \(n\) = 5, \(\upmu\)p \(<\) 0.01 vs. control, * \(p\) \(<\) 0.05, **\(p\) \(<\) 0.01 vs. DMM. **(C)** Immunoblot images of pERK1/2 and total ERK1/2 in the right dorsal horn of the spinal cord. **(D)** The quantified data of the immunoblot for pERK1/2 normalized to total ERK1/2. Each \(n\) = 5, \(\upmu\)t \(p\) \(<\) 0.01 vs. Control, * \(p\) \(<\) 0.05, **\(p\) \(<\) 0.01 vs. DMM. All data are presented as mean \(\pm\) standard deviation. pERK, phosphorylated extracellular signal-regulated kinase; DMM, destabilization of the medial meniscus; BO, bioogito. Experiment 2: Contribution of ERK1/2 Phosphorylation to Pain-Related Locomotive Dysfunction with DMM Surgery  
The results of Western blotting revealed significant blockade of ERK1/2 phosphorylation in the DMM + U0126 group (\(p<0.05\)) (Figure 6A,B). In the rotarod test, the latencies to fall off the rotarod apparatus were not different between the DMM + vehicle group and the DMM + U0126 group before the surgical intervention. After 24 h, rats in the DMM + U0126 group were observed to exhibit significantly longer walking time on the rotarod apparatus than rats in the DMM + vehicle group (\(p<0.05\)) (Figure 6C).
## 4 Results and Discussion
### Features Classification Results
The AlexNet model performs better than alternative models, exhibiting the lowest loss values during training and validation stages for all feature classifications. This indicates that the AlexNet model learns more efficiently and generalizes unseen data better, making it the most effective model for this particular task. These results underscore the robustness and reliability of the AlexNet model in accurately identifying features within pedestrian crash diagrams. For instance, a study on horse-and-buggy crashes in rural Michigan utilized AlexNet to extract crash-related features from diagrams, facilitating the development of an automated safety data extraction system [8; 9]. Similarly, research analyzing micro-mobility injuries in Michigan employed AlexNet to classify micro-mobility crash locations from crash diagrams, demonstrating its effectiveness in diverse traffic safety contexts [8; 60].