esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Related Work
These QoS attributes can be specified manually using a management service or measured automatically and integrated into VRESCO. For each service, a schedule can be defined that specifies when the monitor should trigger the measurement and publish their values. The average QoS values can be aggregated based on the information stored in the QoS events. This approach cannot support fluctuating or unavailable values. The challenge for this algorithm lies in capturing and computing dynamic attributes at query time. Several approaches in web service selection share their conceptual underpinning with the trust and reputation evaluation techniques by [PERSON] _et al_. [22] and [PERSON] _et al_. [23]. In these approaches, user feedback and provider reports regarding generic QoS attributes are the principal evaluation and selection criteria. Then, the QoS data are the trustworthiness of the reported quality of services from both users and providers. [PERSON] _et al_. measure the credibility of the user and the provider claims to improve the service ranking by a trusted third party. They also employed a time-series forecasting techniques to predict the future quality of the service based on these data. [PERSON] _et al_. introduce the Wisp framework to integrate the trust modelsinto service composition. Then, based on the users' experiences with services, the certainty is updated. They also measure the service honesty, competency and reliability. SDDS (static discovery and dynamic selection) by [PERSON] and [PERSON] is our initial framework for integrating static and dynamic attributes during web service discovery [24,25]. We examined context-sensitive QoS attributes [26] and their use in selecting services using aggregation and skyline-based algorithms [27]. Table 1 shows an example with SDDS-determined attributes, where the values are computed in a variety of ways; for example, visibility is obtained from weather services, such as AccuWeather [[http://www.accuweather.com/](http://www.accuweather.com/)]([http://www.accuweather.com/](http://www.accuweather.com/)). Other attributes that refer to context, such as environmental conditions or user preferences, can be obtained via sensors or directly from the user. In her PhD thesis, [PERSON] extended this work by providing a full formalization and description of our aggregation and skyline-based algorithms and then presented a thorough experimental evaluation [28].
## 5 Experimental Design
### Parameter Optimization  
The basic framework of the models we used included five convolutional layers, each with a 3 \(\times\) 3 kernel size, three max-pooling layers, and three dropout layers, as shown in Table 4. In our experiments, we designed five different CNN models to explore the effect of parameter tuning on model performance on an imbalanced dataset. We compared these with a traditional undersampling model for handling imbalanced data.  
Model 1: This is the baseline model using the default number of convolution kernels (32, 64, 96, 128, 256), a default learning rate of 0.001, kernel size of 3\(\times\)3, and dropout rate of 0.2.  
Model 2: Based on Model 1, the number of convolution kernels is adjusted to 103, 105, 107, 109, and 179 while the other settings are unchanged.  
Model 3: Similar to Model 2 but with a kernel size of 5 \(\times\)5, keeping the other parameters the same.  
Model 4: Based on the baseline model, this model adjusts both the number of convolution kernels and learning rate while the other parameters remain at their default values.  
Figure 10: Plot of interaction effect of four variables on evaluation metrics for the Fashion-MNIST dataset.  
Model 5: The adjustments made in Models 3 and 4 are combined by changing the convolution kernel number, size, and learning rate.  
The models were trained using the cross-entropy loss function, and evaluated based on five metrics: accuracy, recall, F1 score, G-mean, and P-mean. These metrics were employed to thoroughly assess the performance across each data category.  
During the training process, the number of epochs was not fixed. Instead, we set a stopping condition; if the difference in training accuracy between the current epoch and the previous epoch was less than \(1\times 10^{-2}\) (i.e., 0.01), the training stopped. This approach helped avoid overtraining the model once a certain accuracy level was reached.  
To optimize the CNN's performance on the minority class samples, we employed the two significant parameters mentioned above: LR and KS. By iteratively adjusting these parameters, we identified the optimal network configuration that performs best in terms of primary and interaction effects, enhancing the recognition capability of minority class samples.
# Improved YOLOv7 for Small Object Detection Algorithm Based on Attention and Dynamic Convolution
## 4 Experiments
### Experimental Comparison of YOLOv7 Network Model and Improved Network Model  
Figure 10 compares the P-R curves for the upgraded network model's identification of tiny objects floating on the water's surface. The AP value of the water bottle is shown by the size of the region that the P-R curve and coordinate axis enclose. After numerous learners learn the data, if P-R Curve A of one of the learners completely envelops the P-R curve of another learner, B, it can be asserted that the performance of A is better than that of B. From the figure, it can be clearly seen that the P-R curve of the improved YOLOv7 network model in the detection of the small target dataset has completely enveloped the P-R curve of the baseline model. Therefore, the performance of the new model proposed in this paper was higher than the benchmark model in small target detection.  
The effectiveness of the YOLOv7-CA dynamic network model was comprehensively evaluated across three distinct scenarios representing real-world images. These scenarios encompassed target-dense images, small target images with intricate backgrounds, and images featuring ultra-small targets. The detection outcomes of both the fundamental YOLOv7 network model and the YOLOv7-CA dynamic network model are showcased through Figures 11-13.  
Figure 10: P–R curve before and after improvement.  
Figure 9: Thermal image comparison of different attention mechanisms.  
Figure 11: Dense target image detection effect comparison.  
Figure 12: Complex background image detection effect comparison.  
In Figure 11, which pertains to target-intensive images, the original image hosts a total of 13 targets. The initial network model successfully detected 11 out of these 13 targets, resulting in 2 missed detections. In contrast, the improved network model attained a perfect detection rate, capturing all 13 targets.  
Moving to Figure 12, depicting small target images set against complex backgrounds, both the original network model and its enhanced counterpart flatered in accurately detecting small targets. Additionally, the original model erroneously generated false detections due to the background interference. However, the enhanced network model surmounted these challenges and effectively identified small targets, even in the presence of intricate background interference.  
The ultra-small target image illustrated in Figure 13, characterized by a minuscule target box size of 0.05 \(\times\) 0.04, posed a substantial challenge. In this context, the original network model experienced missed detections. Notably, the improved network model continued to excel, successfully detecting all the ultra-small targets despite their small proportions.
# The EGNOS Augmentation in Maritime Navigation +
## 3 Experimental Setup
### Static Test  
Static data for 21 h, with a sample rate of 30 s, have been downloaded by EGNOS RIMS located in Rome for the day 6 November 2021. The EGNOS RIMSs can be considered as static reference receivers, which are placed at fixed and known surveyed locations whose coordinates can be used as reference for the positioning error analysis. The position of the considered station is shown in Figure 2.  
The static test has been considered to carry out a preliminary evaluation of EGNOS performance in an open-sky scenario characterized by good satellite visibility and geometry as confirmed by Figure 3 that plots the number of GPS satellites as function of time, while Figure 4 shows the behavior of Position Dilution of Precision (PDOP), Vertical DOP (VDOP) and Horizontal DOP (HDOP) during the whole test.  
Figure 3: Number of visible GPS satellites during the static test.  
Figure 2: EGNOS RIMS located in the airport of Rome “Ciampino”.  
The Figures 3 and 4 confirm the good satellite visibility and geometry of the operational scenario. The number of GPS satellites varies from a minimum of six satellites to a maximum of ten, with an average of eight visible GPS satellites also guaranteeing a solution availability and continuity equal to 100%. In Figure 4, the same behavior can be noted for the PDOP, VDOP and HDOP curves, with lower values for HDOP: the DOP values do not exceed the threshold of 3 (with the exception of some epochs at the beginning and end of the data collection) indicating a good satellite geometry for the entire duration of the test.
# Study on Solar Radiation and the Extreme Thermal Effect on Concrete Box Girder Bridges
## 2 Calculation Method of Solar Radiation and Field Temperature Observing  
### Determination of the Solar Radiation Parameters  
On a clear day, the primary external heat source of the concrete box girder is solar radiation. Solar radiation consists of direct radiation, scattering radiation, and ground reflection. The intensity of solar radiation at the outer edge of the atmosphere is related to the solar constant, which changes slightly in a year, and it is calculated [26,27] as:  
\[I_{0}=1368\times\left[1+0.033 cos\bigg{(}\frac{360N}{365}\bigg{)}\right] \tag{1}\]  
where \(N\) is the number of days (starts from 1 January).  
Therefore, it changes slightly in summer (from July to August), about 1.2%.  
When passing through the atmosphere, solar radiation will be scatted, and the direct solar radiation \(I_{Dn}\) reaching the earth's surface is determined as Formula (2). The scattering radiation \(I_{s}\) on the structure's surface is calculated as Formula (3):  
\[I_{Dn}=I_{0}\cdot P^{m} \tag{2}\]  
\[I_{s}=(0.271I_{0}-0.294I_{Dn})\cdot sin\ h\times(1+cos\ \beta)/2 \tag{3}\]  
where \(P\) is coefficient of atmospheric transmission, valued as 0.807 in this paper, \(m=\csc h\), \(h\) is the solar altitude angle, \(\beta\) is the angle between the normal direction of the structure's outside surface and the ground.  
Since the earth's surface can reflect direct radiation and scattering radiation, the reflection \(I_{f}\) will affect the web and the bottom slab of the girder, and it is calculated as:  
\[I_{f}=[I_{Dn}sin\ h+(0.271I_{0}-0.294I_{Dn})\cdot sin\ h]\times r_{e^{\prime}} (1-cos\ \beta)/2 \tag{4}\]  
where \(r_{e}\) is the ground shortwave radiation rate, which is valued as 0.2.  
The total solar radiation on the surface of the girder is expressed as:  
\[I=I_{Dn}+I_{s}+I_{f}. \tag{5}\]  
To study solar radiation, 16 July 2018 was selected, which was a clear day, and the number of days N is 197. According to solar physics, the solar angle \(\theta_{N}\) at the bridge site of the day is 114.72\({}^{\circ}\), and the solar declination \(\delta\) is 21.35\({}^{\circ}\). The time difference \(t_{d}\) between the local true solar time and the standard time \(t_{s}\) (UTC/GMT + 08:00) was calculated as Formula (6), and the local true solar time \(t_{t}\) is calculated as Formula (7):  
\[t_{d}=0.165 sin\ 2\theta_{N}-0.025 sin\ 2\theta_{N}-0.126 sin\ 2\theta_{N} \tag{6}\]  
\[t_{t}=t_{s}-\frac{120^{\circ}-\gamma}{15^{\circ}}+t_{d}. \tag{7}\]  
The solar altitude \(h\) and solar azimuth \(\psi\) were calculated as:  
\[sin\ h=\ sin\ w\ sin\ \delta+\ cos\ w\ cos\ \delta\ cos\ \alpha \tag{8}\]  
\[sin\ \psi=\frac{cos\ \delta sin\ \alpha}{cos\ h} \tag{9}\]  
where \(w\) is latitude, \(\gamma\) is longitude, and \(\alpha\) is solar hour angle, which is calculated as  
\[\alpha=(12-t_{t})\times 15^{\circ} \tag{10}\]  
where \(t_{t}\) is the local true solar time.
## 4 Discussion  
### Examples  
Resonant column test data (shear modulus and damping ratio) sourced from the literature are compared with a model for sand and clay with different confinement stress levels. The parameters of model are selected by least-square fitting of the model to the damping ratio as measured from a resonant column test. The linear damping ratio \(\zeta^{L}\) is experimentally constrained with a constant value below elastic threshold regime. It was found that the model order as an indicator of degree of nonlinearity is less sensitive then the nonlinear parameter. Moreover, the values only range from 2 to 3 (41 cases are tested). Previous studies revealed that the exponent of the hysteresis is \(n=2\) for many polycrystalline metals (copper, zinc, and lead) and particulate materials (rock, sandstone, and sand), and that these materials require quadratic correction by a constitutive stress-strain equation in order to describe the symmetrical hysteresis response [24; 33]. Therefore, this study uses the second model order. The data points and fitted models are plotted on the semi-log scale (Figure 3). Higher confinement stress extends the elastic threshold region with lower \(\alpha\) values. Clay is expected to be less flexible or less nonlinear than sand and the application of confinement stress would reduce the nonlinearity. These outcomes are reflected in the trends of the \(\alpha\) values. These examples demonstrate that the \(\alpha\) parameter contains physical, not ad-hoc, fitting parameters.
# A Machine Learning Approach to Determine Oyster Vessel Behavior
## 3 Oyster Fishing Dataset
### Statistics of Oyster Fishing Dataset  
The new oyster fishing dataset (used in this study) consists of ping points produced by VMS modules recorded from July 2013 to August 2014. The 612,700 total points are classified into four behaviors with explicit rule-based categorization of docked (4.38%), poling (6.62%), fishing (44.33%) and traveling (37.31%).
# Lightweight Path Recovery in IPv6 Internet-of-Things Systems
## 3 System Model and Problem Statement
### Problem Statement
Figure 4: An example of mirrored point-to-point traffic. (**a**) Up-path and downpath. (**b**) Inverse order paths. * _Gain Loss Ratio_. The meaning of this metric is how many bytes of gain we can exchange for one byte of overhead, which is defined as: \[Gain\ Loss\ Ratio=\frac{Gain}{Loss},\] (2) where \[Gain=\sum_{i=1}^{Num_{generated}}C_{ID}\times Len_{i}\times R_{i},\] (3) and \[Loss=Overhead\times Num_{generate}.\] (4) The \(C_{ID}\) is the number of bytes occupied by a node ID, i.e., 2 bytes in our network model, and \(Len_{i}\) is the path length of the packet i. The \(R_{i}\in\{0,1\}\) is an indicator variable of the packet i where \(R_{i}=1\) means the packet path was correctly recovered, while \(R_{i}=0\) means the packet path does not exist or has not been recovered correctly. The _Overhead_ is the extra overhead added to each packet for path recovery in this algorithm. As seen in the previous section, the overhead is 6 bytes in our network model, including 2 bytes for the parent node ID, 2 bytes for the grandparent node ID, and 2 bytes for the hash value. \(Num_{generate}\) represents the packets generated by all active nodes, although some source nodes could not be successfully communicated to the destination node due to a failure of the original route node, which is limited by the transmission distance.
# Research on the Scope of Spectral Width Parameter of Frequency Domain Methods in Random Fatigue
###### Abstract  
In the current fatigue life calculation theory, the most commonly used method is the frequency domain method. However, most of the frequency domain fatigue life prediction models do not indicate the scope of the application of the spectral width parameter. Different frequency domain methods have strict applicability to the spectral width parameter, and improper model selection will lead significant error. Therefore, it is particularly important to determine the scope of application of the spectral width parameter for different frequency-domain methods. This paper firstly introduces the current frequency domain methods, then simulates the analogue spectrum and selects three materials for comparison in the different frequency-domain methods. By analyzing and comparing the results of random fatigue life and relative error results, the application of different frequency-domain methods is obtained, and random vibration simulation verification is carried out with the practical engineering example, which can provide a reference for the selection of life prediction models.  
Keywords:random fatigue; frequency-domain method; spectral width parameter; stationary Gaussian random process; life estimation; finite element analysis +
Footnote †: journal: Applied Sciences
# Effect of Bentonite Addition on the Properties of Fly Ash as a Material for Landfill Sealing Layers
###### Abstract  
Landfill sealing layers protect groundwater and soil against contamination from waste leachate. Fly ash, which is a coal combustion by-product, might be used as a substitute material for natural soils to build sealing layers. To improve its properties, different additives can be used. In this study, bentonite in the amounts of 5%, 10% and 15% of the dry mass of fly ash was used as an addition. Compacted fly ash and fly ash-bentonite mixtures were tested in order to verify how bentonite and the amount of addition affect the properties of the materials. Therefore, hydraulic conductivity, as the most important parameter, which determines the suitability of a material for construction landfill sealing layers, were tested. Results indicate that bentonite changes the properties of fly ash and allows the achievement of smaller hydraulic conductivity values. To provide a comparison to the natural soil, sandy silty clay that meets the criteria for natural soils for sealing layers was tested and the differences are presented. Fly ash with 15% of bentonite addition reached a value of hydraulic conductivity for the material suitable to build compacted landfill sealing layers.  
fly ash; bentonite; hydraulic conductivity; landfill sealing layers +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Compressor Performance
On the other hand, in a specific flow rate range, the compressor performance gradually decreases with the increase of the clearance. The same flow point of each configuration is selected for further analysis. For configurations 1 to 3, only the size of c1 changes, and the compressor performance of configuration 1 is taken as the benchmark. For configurations 3-6, only the size of c2 changes, and configuration 3 is taken as the benchmark. The variation trend of compressor efficiency attenuation with the tip clearance is obtained, as shown in Figure 11. It can be seen from this figure that for cavity 1, when the tip-to-span ratio (the ratio of tip clearance to stator blade height) increases by 1%, the compressor efficiency decreases by 0.4%; for cavity 2, the efficiency decreases by 0.6%, and as the gap continues to increase, the extent of efficiency decrease has an increasing trend. It is in the same order of magnitude as 1% efficiency decay in reference [15].
# WRTU-16T: Write-Enhanced Low-Power Radiation-Tolerant SRAM for Space Applications
## 2 Proposed WRTU-16T SRAM Architecture
### SEU Recovery Analysis
#### 2.5.3 SEMNU at Node Pair Q-S0  
A SEMNU at nodes Q and S0 causes a permanent flip from logic '1' to logic '0'. In this case, all sensitive nodes are affected at once, and the recovery mechanism fails to restore the original data. To mitigate such events, a layout-level solution is required. Specifically, increasing the physical distance between nodes Q and S0 during the layout design can reduce the possibility of a simultaneous strike.
## 4 Analysis of Test Results
### Interface Force Mechanism Analysis  
Figure 8 shows the strain-load relationship for the post-installed rebar in specimen C5 and specimen C6.  
Analysis showed that:  
In the early stage of loading, the strains of the C5 specimen and the C6 specimen post-installed rebar were small. However, the strain increase rate was very low. It indicated that the shear stresses on the joint surface at the early stage of loading were mainly carried by the bonding force of the new-to-old concrete itself.  
The post-installed rebar in specimen C5 started to grow suddenly and rapidly when it was loaded to 900 kN. In contrast, the post-installed rebar in the C6 specimen with the addition of the interface agent did not start to grow more significantly until it was loaded to 1200 kN. This indicated that the epoxy resin interface agent started to work by enhancing the new-to-old concrete bond when loaded from 900 to 1200 kN. When loaded near the ultimate load, the post-installed rebar tensile strain growth rates all started to rise considerably. In summary, it was clear from the above analysis that in new-to-old concrete with post-installed rebar + interface agent, the epoxy resin interface agent played the main role in the pre- and mid-loading period by increasing the bond between the new-to-old concrete. The post-installed rebar started to function only in the mid-and late-loading period.
# Adaptive Allocation Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks
## 5 Simulation Module Implementation
### Modified NS-2 Simulator for IEEE802.11n  
Network Simulator 2 (NS-2), is an open-source network simulator. We implement modifications to the physical and MAC layers for NS-2 to achieve a simulator for IEEE 802.11n through the framework NS-2.34 Version [27]. The modified model is meant to be proper with the original NS-2.29 for the 802.11 model in [28].  
The physical layer of NS-2.34 802.11 is an easy model to understand in which the improved version is embedded in [28]. Through that module, we update the signal-to-noise-ratio to bit-error-ratio (SNR-BER) table to measure the overall network performance and also the error recovery efficiency with various power conditions and multi-input multi-output (MIMO) system schemes. According to that, the new physical wireless files are embedded to enable MIMO to be used as the form of the network interface.  
In the MAC layer, we embed the IEEE 802.11n MAC protocol files as a new MAC protocol type for the NS-2.34. We implement only the aggregation MAC protocol data unit aggregation (A-MPUA) into the MAC layer. Normally, A-MPUA is less efficient than the aggregation MAC Service Data Unit (A-MSDU) aggregation. However, A-MPUA can be more efficient in environments with high error rates due to the use of the block acknowledgment mechanism. The acknowledgment of every aggregated data frame is done separately, and to be re-transmitted if suffering from error. In the interface queue type, we implement the new aggregation module. Figure 1 shows our implementations of IEEE 802.11n in NS-2.34.  
The simulation is done by assuming that each host has full constant bit rate (CBR) traffic coordinating to the related AP. The length of each data packet handed to the MAC layer is 1000 bytes.  
Figure 1: IEEE 802.11n implementation in Network Simulator (NS)-2.34.  
We suppose that the frame size of the MAC layer is a maximum of 8 Kbytes as an aggregated payload. The remainder of the NS-2 simulation assumptions are shown in Table 2.