esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials  
GPS ground displacement data are taken from the Nevada Geodetic Laboratory website at: [[http://geodesy.unr.edu/gps_timeseries/tenv3/IGS14/](http://geodesy.unr.edu/gps_timeseries/tenv3/IGS14/)]([http://geodesy.unr.edu/gps_timeseries/tenv3/IGS14/](http://geodesy.unr.edu/gps_timeseries/tenv3/IGS14/)) (accessed on 1 May 2023).  
These data represent displacements of the Earth's surface in three orthogonal directions, which we will further denote as E, N, and U (Up) for displacements in the west-east, north-south, and vertical directions, with a time step of 1 day [22].  
In what follows, we will consider a regular grid of 100 \(\times\) 150 nodes, covering an area with latitude from 32.5\({}^{\circ}\) N to 51\({}^{\circ}\) N and longitude from 115\({}^{\circ}\) W to 128.5\({}^{\circ}\) W. Figure 1a shows the positions of GPS points for measuring displacements of the Earth's surface. Figure 1b shows the distribution density of GPS stations obtained using the Gaussian kernel estimate [23] at the nodes of this regular grid with a smoothing radius of 0.25\({}^{\circ}\) (about 28 km).  
We will consider estimates of the properties of GPS time series in a moving time window 365 days long with a shift of 7 days. For each time window, operable stations are determined from the condition that the number of gaps in the considered time window does not exceed 5% of the time window length, that is, that this number is not more than 18 values. Missing values are filled in with artificial data, which are found from the value to the left and right of the missing time interval of the same length as the length of the gap [19]. Figure 2 shows a graph of the number of working stations within a sliding time window of 365 days for 14 years of observations, 2009-2022.  
Figure 1: (**a**) Network of GPS stations (blue dots) in the US West, 2009–2022; (**b**) the density of GPS stations, estimated using a Gaussian kernel with a smoothing radius of 0.25 degrees on a regular grid of nodes with a size of 100 \(\times\) 150. Plot (**b**) presents every second grid line by coordinates, because all the grid lines form too dense a network.
## 3 Damage Detection and Localization Scheme
\begin{table}
\begin{tabular}{c c l} \hline \hline  & **Step No** & **Procedures** \\ \hline \multirow{8}{*}{**OCSVM**} & 1 & For each impact location \(k\) = 1:\(N\) \\  & 2 & Acquire the response set at DOF \(k\), \(k+1\) \\  & 3 & Calculate the transmissibility using \(\left(T_{k,k+1}^{k}\right)^{U}\) Equation (8) \\  & 4 & Select the frequency range of interest \(\omega_{1}\) to \(\omega_{2}\) \\  & 5 & Repeat (1–4) with all the available baseline state (\(U\)) data and calculate mean \((T_{k,k+1}^{k})^{U}\) \\  & 6 & Obtain the feature vector for training for \(k\) = 1:\(N-1\) with Equation (9): \\  & & \(\left(DSF_{train}\right)_{k}=\frac{\sum_{i=1}^{\omega_{1}}\left|(T_{k,k+1}^{k}) ^{U}-\text{mean}\left((T_{k,k+1}^{k})^{U}\right)\right|}{\sum_{i=1}^{\omega_{ 2}}\left|\text{mean}\left(T_{k,k+1}^{k}\right)^{U}\right|}\) \\ \hline \multirow{8}{*}{**OCSVM**} & 7 & Repeat Steps 1–3 and obtain new set of \(\left(T_{k,k+1}^{k}\right)^{D}\) for the monitoring state (\(D\)) \\  & 8 & Test the feature vector for \(k\) = 1:\(N-1\) using Equation (9): \\  & 9 & Detect state: Damaged or Undamaged using the selected one-class algorithms \\ \hline \multirow{8}{*}{**OCSVM**} & \multirow{8}{*}{10} & If damaged: Damage localization by fusing sensor information \\  & & At each frequency \(\omega\), identify the transmissibility difference that has the highest value \\  & & Looking across the entire frequency range, identify the entry with the maximum number of \\  & & \(\text{occurrences}\), \(n_{max}\) \\  & & Normalize this value by dividing it with the total number of spectral lines, \(n_{freq}\) \\  & & \(DI=n_{max}/n_{freq}\) \\  & & The sensor pair corresponding to this entry suggests that the potential damage location is between \\  & & the positions of the respective sensors \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Damage detection methodology.
# Local Attention Sequence Model for Video Object Detection
## 3 Kinematic Control of CDHRM
### Local Attention Sequence Model  
The distribution of spatial attention changes with time and has a certain continuity, and optical flow networks are usually used to establish motion information. However, this requires the introduction of additional deep convolutional networks such as FlowNet, leading to excessive model parameters and calculations, which is not conducive to model deployment. In addition, the optical flow network establishes local pixels corresponding to motion information, and it is difficult to model the continuity between high-level semantic features. Since the motion between adjacent moments occurs more in the local domain, we design the local attention sequence model, focusing on the small-range motion  
Figure 1: Pipeline of the proposed video object detection framework. Only three frames are shown for simplicity.  
Figure 2: Spatial attention module.  
information in the local domain, so as to establish the temporal consistency of the spatial attention module.  
Specifically, the local attention sequence model can be formulated as follows: The first step is to achieve the aligned distribution of spatial attention by aggregating the corresponding feature cells with correspondence weights. Given two adjacent frames \(\mathbf{F}_{t}\) and \(\mathbf{F}_{t-1}\), we first compute the affinity between two feature cells at various positions. Then, we compute the normalized correspondence weights in the local area. Finally, we compute the weighted sum of the corresponding feature cells in the local area as the aligned distribution as  
\[\begin{split} C_{x,y}(i,j)=\mathbf{F}_{t}(x,y)\cdot\mathbf{F}_{t- 1}(x+i,y+j)\\ \mathbf{T}_{x,y}(i,j)=\frac{\exp\left(C_{x,y}(i,j)\right)}{\sum \limits_{a\in\{\sum\{-k,-k\}}}\exp\left(C_{x,y}(a,b)\right)}\\ \mathbf{\hat{A}}_{t}(x,y)=\sum\limits_{i,j\in\{-k,-k\}}\mathbf{T}_ {x,y}(i,j)\cdot\mathbf{A}_{t-1}(x+i,y+j)\end{split} \tag{2}\]  
where \(C_{x,y}\) represents the affinity matrix. \(\mathbf{T}_{x,y}\) represents the correspondence weight matrix, and it is restricted in the sub-region with stride \(k\). \(\mathbf{\hat{A}}_{t}\) represents the aligned distribution of spatial attention.  
After achieving the aligned distribution of spatial attention, a small neural network, named the update network, is devised to fuse two distributions adaptively, with the goal of incorporating the temporal context of videos. As shown in Figure 3, the update network takes the concatenation of two distributions to obtain an adaptive weight through a single convolutional network. Then, we compute the weighted sum of the two distributions as the final distribution.
# Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data
## 3 Methods
### Methods for Efficient Computation  
One of the key properties required for data quality measures is that they should be computable in a reasonable amount of time and computation resources since the amount and the dimension of data are keep increasing as new advanced sensing technologies become available. In this section, we describe how we avoid a large amount of time and memory complexity to compute our suggested data quality measures.
## 2 Theoretical Background
### Acoustic Performance of EoLHM in the Literature
#### 2.3.1 Textile Waste  
Textile waste includes clothes, carpets, tablecloths and pieces from the textile sector. In literature, a very recurring classification is between woven (WF) and non-woven (NWF) fabrics: WF are obtained by threading fibers together perpendicularly, whereas NWF are bounded together by using heat, chemical, or mechanical treatment. Textile waste have been widely investigated from the acoustic point of view because they are largely available and their porous structure makes them suitable for acoustic absorption. The sound absorption of NWF waste was investigated in relation to the fiber content and the fiber diameter [34], and NWF shows higher sound reduction than WF [35]. A panel made of waste wool and polyamide fibers was designed in [36], and it presented a sound absorption coefficient equal to 0.91 and \(NRC\) equal to 0.56. The study pointed out that the sound absorption coefficient in the low-frequency range is affected by the thickness, while the volume density affects the absorption properties in the middle-frequency range. Blankets for building roofing and internal walls insulation were realized with polyester fabrics of different sizes and they showed an \(NRC\) ranging between 0.54 and 0.74 [37]. The study in [38] investigated the correlation between the humidity content and the transmitted wave through cotton fabric: for moisture content between 0 and 100%, the transmitted wave ranges between 31% and 7%.
## 3 Results and Discussion
### Ranking Descriptive Analysis  
The average results of Irish and German assessors perception of butters are portrayed in Figure 1a,b and in Table 2. Corresponding to the results for yellow intensity (Table 1), both Irish and German assessors rated FS-TMR butterter significantly (\(p<0.05\)) lower for yellow color. Both German and Irish assessors rated FS-CLVR butter as having a more intense yellow color (Figure 1a,b), compared to FS-GRSS butter, although not significant, this may suggest that FS-CLVR butter contained higher amounts of \(\beta\)-carotene (\(\beta\)-carotene levels vary with intake of grass and clover outdoors). [PERSON] et al. 2016 [22] identified grass butters as having the highest levels of \(\beta\)-carotene. However, [PERSON], [PERSON] et al. (2019) [39] identified Massdam cheeses produced from cows supplemented with white clover as having higher amounts of \(\beta\)-carotene compared to cheese produced from only grass. As mentioned, \(\beta\)-carotene content in milk will vary due to the levels of grass and clover ingested by cows due to differences in availability within the pasture. German assessors perceived FS-TMR butter as significantly (\(p<0.05\)) darker than Irish assessors; however, this does not match results from the intensity scale portion of the consumer study (Table 1). This result may be due to the fact that in Ireland only butter derived from pasture is commercially available, while in Germany butter's from both pasture or concentrate are widely available, and therefore Irish consumers may have scored TMR butter lower for color intensity due to a lack of familiarity. Irish assessors perceived the aroma of FS-GRSS and FS-CLVR butters as significantly (\(p<0.05\)) more buttery compared to FS-TMR butter, similar to [PERSON] et al. 2016 [22], where consumers rated butter produced from grass significantly higher for diacetyl aroma compared to TMR butter. There was no significant difference (\(p<0.05\)) detected among the German assessors for buttery aroma. German assessors perceived FS-GRSS butters to be significantly more milky compared to Irish assessors. Grassy and rancid aroma were not identified as significantly (\(p>0.05\)) different by either German or Irish assessors. Both Irish and German assessors perceived significant (\(p<0.05\)) differences in saltines perception, with values for FS-GRSS and FS-CLVR butters significantly higher, compared to FS-TMR butter. This is likely linked to the texture--melt in the mouth attribute--where Irish assessors perceived FS-GRSS and FS-CLVR butters to melt much more rapidly (\(p<0.05\)) in the mouth. As previously mentioned, this is likely due to changes in fatty acid profile, which influence spreadability and melting properties of butter [40]. Similarly, Irish assessors perceived the flavor attribute creamy as significantly (\(p<0.05\)) higher in both FS-GRSS and FS-CLVR butters, than in FS-TMR butter, which is also likely directly related to the texture, and in turn to the unsaturated fatty acid profile of these butters. Irish assessors have previously perceived pasture butterters as significantly more creamy [22].
## 5 Performance Evaluation of Classification Algorithms
### How does Image Segmentation Influence the Classification Results?  
To test the performance of the classifiers with respect to variations in the image segmentation and to evaluate the influence of the segmentation stage on the succeeding object-based classifications, the following experiment was set up. The classifiers are trained and tested on the reference datasets with parameters being tuned according to the outcomes of the experiments described in Sections 5.2 and 5.3. The trained learning machines are applied to a total of 26 segmentations of the same image they were trained on, resulting in 260 classifications per image type when using a 10-fold cross-validation. Segmentation parameters were iteratively varied in order to produce segmentations with increasing average segment size.  
Figure 13: ROC curves for the classification of QB, TM and MSS, depicting the performance of different classifiers under varying segmentation inputs.  
Figure 13-16 show the results for the different classifiers tested on the QB, TM and MSS images. From Figure 13 a good performance of all classifiers can be observed over all segmentations. Only NB seems to be strongly affected by changes in the segmentation prior to classification, especially on the TM and MSS datasets. The highlighted operating points in Figures 14-16 identify the segmentation parameter combinations that were used to produce the segments for the reference datasets. These combinations were identified independently based on the segmentation quality assessments described in Section 3. For all the classifiers on all the image types the optimal segmentation parameters coincides with the operating point of best classification performance. It can also be seen that parameter combinations beyond the identified operating point, which produce segmentations with larger average  
Figure 14: Influence of the segmentation inputs on the classification accuracy for QB with increasing average segment size from left to right. Mean and range of accuracy measures from the ten-fold cross-validation are shown for each iteration.  
Figure 15: Influence of the segmentation inputs on the classification accuracy for TM with increasing average segment size from left to right. Mean and range of accuracy measures from the ten-fold cross-validation are shown for each iteration.  
segment size, negatively affect the classification performance. In addition, a smaller average segment size negatively affects the classification results. This effect becomes more prominent with NB than with SVM, RT and KNN, indicating a lower sensitivity of the latter to the segmentation stage. In addition, the stronger fluctuations of the NB accuracies, especially on the QB and MSS datasets, indicate this performance difference between the classifiers.
# Effects of Curcumin on the Renal Toxicity Induced by Ochratoxin A in Rats
## 2 Materials and Methods
### Experimental Design and Sample Collection  
During this study, 10-week-old (250-270 g), male Sprague Dawley rats, were randomly allocated into four experimental groups (6 rats for each group) experiencing 22 \({}^{\circ}\)C temperatures and 12 h day/night cycles. The animals received a standard diet ad libitum. Animals were treated daily for 14 days by gavage as follows: Control group: 2 mL/kg body weight (b.w.) of olive oil; OTA group: 2 mL/kg b.w. of olive oil containing 0.5 mg/kg b.w. of ochratoxin A [7]; CURC group: 2 mL/kg b.w. of olive oil containing 100 mg/kg b.w. of curcumin [20]; OTA (1 mL/kg b.w. of olive oil containing 0.5 mg/kg b.w. of ochratoxin A.) + CURC (1 mL/kg b.w. of olive oil containing 100 mg/kg b.w. of curcumin). The use of olive oil served to improve the stability of curcumin. The duration of the experiment (14 days) was based on our previous work [7; 21; 22]. Body weights were monitored at three time points: before the treatment (time 0), after 7 and 14 days of treatment. Once the experimental period ended, rats were anesthetized with 2% isoflurane (Isotec 4, Palermo Italy) and, after completely sedated, blood samples were collected from the aortas and immediately centrifuged at 3000 rpm for 15 min at 4 \({}^{\circ}\)C. Blood urea nitrogen (BUN) and creatinine (CREA) levels were measured after 14 days of treatment by an auto-chemistry analyzer (PKL PPC 125, Parametical srl, Salerno, Italy) using commercial diagnostic kits following the manufacturer's instructions. Following the end of the treatment, animals anesthetized with 2% isoflurane were prepared for the assessment of the clearance of inulin to measure the glomerular filtration rate (GFR) index expressed as ml/min 100 g body weight (b.w.), as previously described in our study [7]. Subsequent to the end of clearance experiments, rats underwent euthanasia by an overdose of 4% isoflurane (Isotec 4, Palermo Italy) and samples of kidney were taken on ice and stored at \(-80\)\({}^{\circ}\)C until analyzed for the activity of enzymes involved in oxidative stress and lipid peroxidation, and partially prepared for routine histopathology.
## 4 Fourier Ptychographic Transformer Backbone Network
#### Loss Function  
The loss function is composed of three parts: L1 loss, SSIM [38] loss between the output amplitude and phase of the network and the ground truth, as well as the amplitude loss between the reconstructed low-resolution amplitudes and the input amplitudes.  
\[\begin{split} loss=w_{1}\cdot L_{L1}(X_{A},Y_{A})+w_{1}\cdot L_{L1} (X_{p},Y_{p})+w_{2}\cdot L_{SSIM}(X_{A},Y_{A})+\\ w_{2}\cdot L_{SSIM}(X_{P},Y_{P})+w_{3}\cdot L_{amp}(A_{1},A_{2}) \end{split} \tag{11}\]  
\[L_{L1}(x,y)=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-f(x_{i})|, \tag{12}\]  
\[L_{amp}(A_{1},A_{2})=\frac{1}{n}\sum_{i=1}^{n}\frac{\sum_{i=1}^{m}\left|\frac{ x_{i}-D_{fpm}(f(x_{i}))}{\sum_{i=1}^{m}x_{i}},\right. \tag{13}\]  
\[L_{SSIM}(x,y)=1-\frac{(2\mu_{x}\mu_{y}+c_{1})(\sigma_{xy}+c_{2})}{(\mu_{x}^{2 }+\mu_{y}^{2}+c_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2})}, \tag{14}\]  
where \(n\) denotes the number of pixels in a amplitude, m denotes the number of LEDs, \(D_{fpm}\) denotes the forward downsampling process of FPM, \(\mu_{x}\mu_{y}\) denotes the mean of \(x,y\), \(\sigma_{x},\sigma_{y}\) represents the standard deviation of \(x,y\), \(\sigma_{xy}\) signifies the covariance of \(x,y\), \(c_{1},c_{2}\) are constant, and \(w_{1},w_{2},w_{3}\) are hyper-parameters. During Stage 1 of self-supervised learning, \(w_{1}=1\), while \(w_{2}\) and \(w_{3}=0\); during Stage 2 of supervised learning, \(w_{1}=0\), and both \(w_{2}\) and \(w_{3}=1\).
# A Study on the Design of Fog Computing Architecture Using Sensor Networks
###### Abstract  
It is expected that the number of devices connecting to the Internet-of-Things (IoT) will increase geometrically in the future, with improvement of their functions. Such devices may create a huge amount of data to be processed in a limited time. Under the IoT environment, data management should play the role of an intermediate level between objects and devices that generate data and applications that access to the data for analysis and the provision of services. IoT interactively connects all communication devices and allows global access to the data generated by a device. Fog computing manages data and computation at the edge of the network near an end user and provides new types of applications and services, with low latency, high frequency bandwidth and geographical distribution. In this paper, we propose a fog computing architecture for efficiently and reliably delivering IoT data to the corresponding IoT applications while ensuring time sensitivity. Based on fog computing, the proposed architecture provides efficient power management in IoT device communication between sensors and secure management of data to be decrypted based on user attributes. The functional effectiveness and the safe data management of the method proposed are compared through experiments.  
IoT; fog computing; sensing data; architecture +
Footnote †: journal: Sensors
# Study on Inertia Load Resistance Analysis Method of Light Truck Door Latch
## 2 Light Truck Door Latch and Its Inertia Load Resistance Analysis Process
### Light Truck Door Lock Performance Requirements  
According to the American SAE standard SAEJ839v002, the basic requirements for the mechanical properties of the car door latch are as shown below.  
1. Longitudinal Load--An automotive door latch and striker assembly, when tested as described under test procedures, must be able to withstand an ultimate longitudinal load of 11,000 N when in the fully latched position and 4450 N when in the secondary latched position.
2. Transverse Load--An automotive door latch and striker assembly, when tested as described under test procedures, must be able to withstand an ultimate transverse load of 8900 N when in the fully latched position and 4450 N when in the secondary latched position.
3. Inertia Load--An automotive door latch, when contained in the door latch system (including the door latch, striker assembly, outside handle, key cylinder, and any connecting mechanisms), and in the fully latched position, when evaluated by calculation, must remain in the fully latched position when subjected to an inertia load of 30g in any direction.  
Figure 4: The locking mechanism model. (1) Pawl connect arm, (2) locking connect arm, (3) key locking toggle arm, (4) motor, (5) worm, (6) locking gear, (7) inside locking toggle arm.  
In this study, when analyzing the inertia load resistance of the light truck door latch, the following requirements were carried out:  
When the door lock is in the fully latched position, it can withstand an inertia load of 60 g of acceleration and 30 ms of action time in any direction and can maintain the fully latched position.