esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Target Detection Results of YOLOv4 Improved by Channel Pruning Technology  
The loss function of the model after sparse training in Section 3.1 is shown in Formula (6):  
\[L=\sum\
olimits_{(x,y)}l(f(s,W),y)+\lambda\sum\
olimits_{\gamma\in\Gamma}(g( \gamma)) \tag{6}\]  
\(\sum\
olimits_{(x,y)}l(f(s,W),y)\) is the training loss of the network, \(x\) and \(y\) are the input and output of model training, respectively, and \(W\) is the training parameter in the network. \(\lambda\sum\
olimits_{\gamma\in\Gamma}(g(\gamma))\) is the \(L1\) regular constraint term of the \(\gamma\) coefficient of the BN layer, and \(\lambda\) is the penalty factor. The distribution diagram of the \(\gamma\) coefficient of the BN layer during normal training is shown in Figure 12(a), and was mainly distributed around 1. The distribution diagram of the \(\gamma\) coefficients of the BN layer during the sparse training is shown in Figure 12(b). In the sparse training process, as the number of epochs increased, the \(\gamma\) coefficient of the BN layer gradually approached zero, which indicated that the \(\gamma\) coefficient gradually became sparse. By the 120 th epoch, the sparsity change gradually became weak, and the sparsity training was saturated.  
Figure 12: Experimental platform of the spatial location of binocular camera.  
Five commonly used indicators, average precision (_AP_), _Params_, Floating Point operations (_FLOPs_), model size and speed, were used to verify the performance of the improved model. The definition of average precision (_AP_) is shown in Formula (7), where \(P(r)\) represents a PR curve with precision (\(P\)) as the horizontal axis and recall (R) as the vertical axis:  
\[AP=\int_{0}^{1}P(r)d_{r} \tag{7}\]  
The _Params_ and Floating Point operations (_FLOPs_) are defined as shown in Formulas (8) and (9), where \(H\) and \(W\) represent the width and height of the input image, \(K\) is the convolution kernel size, and \(C_{in}\) and \(C_{out}\) represent the number of convolution kernels input and output.  
\[Params=C_{in}\times K^{2}\times C_{out} \tag{8}\]  
\[FLOPs=2\times H\times W\Big{(}C_{in}K^{2}+1\Big{)}C_{out} \tag{9}\]  
On the premise that the accuracy was not reduced greatly, 0.6 was selected as the pruning rate to shrink the YOLOv4 target detection model after sparse training. Finally, the _Params_, _FLOPs_ and model size were all decreased by about 89.1% after 19,331 channels were cut off, while the average precision (_AP_) decreased by only 0.1% (from 94.5% to 94.4%). To be specific, the _Params_ decreased from 63,937,686 to 6,973,211, the _FLOPs_ decreased from 29.88 G to 3.26 G, and the model size decreased from 244 M to 26.7 M, which indicated that the pruned model was much smaller than the original one and the improved algorithm was successful.
Sensors_**2015**, _15_, 8284-8301; doi:10.3390/s150408284  
Sensors  
ISSN 1424-8220  
www.mdpi.com/journal/sensors  
Article  
Vineyard Yield Estimation Based on the Analysis of High Resolution Images Obtained with Artificial Illumination at Night  
**[PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] ***  
Department of Computer Science and Industrial Engineering, Universitat de Lleida, Jaume II, 69, 25001 Lleida, Spain; E-Mails: [EMAIL_ADDRESS] (D.F.); [EMAIL_ADDRESS] (M.T.); [EMAIL_ADDRESS] (D.M.); [EMAIL_ADDRESS] (J.M.); [EMAIL_ADDRESS] (E.C.)  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-973-702-760; Fax: +34-973-702-702.  
Academic Editor: [PERSON]  
Received: 5 December 2014 / Accepted: 3 April 2015 / Published: 9 April 2015  
This paper presents a method for vineyard yield estimation based on the analysis of high-resolution images obtained with artificial illumination at night. First, this paper assesses different pixel-based segmentation methods in order to detect reddish grapes: threshold based, Mahalanobis distance, Bayesian classifier, linear color model segmentation and histogram segmentation, in order to obtain the best estimation of the area of the clusters of grapes in this illumination conditions. The color spaces tested were the original RGB and the Hue-Saturation-Value (HSV). The best segmentation method in the case of a non-occluded reddish table-grape variety was the threshold segmentation applied to the H layer, with an estimation error in the area of 13.55%, improved up to 10.01% by morphological filtering. Secondly, after segmentation, two procedures for yield estimation based on a previous calibration procedure have been proposed: (1) the number of pixels corresponding to a cluster of grapes is computed and converted directly into a yield estimate; and (2) the area of a cluster of grapes is converted into a volume by means of a solid of revolution, and this volume is converted into a yield estimate; the yield errors obtained were 16% and -17%, respectively.  
precision agriculture; yield estimation; segmentation techniques; color features
of volatile compounds from honey. Hydro-distillation (HD) [8; 9]; microsimultaneous steam distillation-solvent extraction (MSDE) [9]; liquid-liquid extraction (LLE) [10; 11]; dehydration homogeneous liquid-liquid extraction (DLLME) [11]; solid-phase extraction (SPE) [10; 12]; dynamic headspace extraction (DHS) [13; 14; 15]; solid-phase microextraction (SPME) [16; 17]; and ultrasound-assisted extraction (USE) [11; 18] are mentioned in the literature. Among the above techniques, SPME is a well-accepted methodology because it's simpler with no pre-treatment of samples and it is free of organic solvents. Different SPME fiber coatings are available, and the extraction is usually followed by gas chromatography-mass spectrometry (GC-MS), allowing the qualitative and quantitative determination of honey volatile fractions [19; 20].  
It is well known that several factors including temperature, equilibration time, extraction time, sample volume, water-honey ratio, and sodium chloride addition, affect volatiles' isolation. However, optimum magnetic stirring velocities have not been studied yet. In the last decade, only a few studies [21; 22; 23; 14] focused on optimizing the conditions for isolation of honey volatiles. In most studies, the procedure is followed by a one-factor-at-a-time technique or grouped factors to reduce the experimental design runs. To overcome this problem, the optimization can be carried out by using multivariate statistic techniques [25]. Symmetrical experimental designs (Box-Behnken, Doehlert designs, three-level factorial, and central composite) could be used to generate optimization models.  
In this context, the aim of the present work was (a) to develop an optimized methodology of the SPME technique for the isolation of dominant volatile compounds of thyme honey using response surface methodology (RSM) (b) to evaluate the robustness of the model using response data validation from eighty unifloral Greek thyme honeys and (c) to optimize the magnetic stirrer velocity.
## 2 The GMCS-Involved \((t,n)\) Scheme for CVQSS
The dealer obtains \(\{x_{12_{1}},p_{12_{1}}\}\) with \(x_{12_{1}}=x_{h}-\sum_{j=1,j\
eq 2}^{n}\sqrt{T_{j}}x_{j}\) and \(p_{12_{1}}=p_{h}-\sum_{j=1,j\
eq 2}^{n}\sqrt{T_{j}}p_{j}\). 3. Repeat the step 2 for \(t\) times. Each of subset \(\{x_{l_{l}},p_{l_{l}}\}\) of the dealer is the same as \(\{x_{j},p_{j}\}\) of \(B_{j}\), for \(l\in\{1,2,\cdots,t\}\) and \(j\in\{1,2,\cdots,n\}\). Figure 1: The state preparation of participants and the dealer. L is laser, M represents modulator, BS is beam splitter, DHD expresses double homodyne detector, and \(\{x_{j},p_{j}\}\) are independent Gaussian random numbers retained by the \(j^{th}\) participant \(B_{j}\) with \(j\in\{1,2,\cdots,n\}\). 4. The dealer emerges the secure key \(S\) from raw data. Distribution of the secure key \(S\) can be described as follows \[ \begin{array}{c}Z_{1}=S\mod x_{1j_{1}},\\ Z_{2}=S\mod x_{2j_{2}},\\ \cdots\\ Z_{t-1}=S\mod x_{t-1j_{t-1}},\\ Z_{t}=S\mod x_{tj_{t}}.\end{array} \] (1)
5. The polynomial prepared by the dealer can be expressed as \[f(x)=S+Z_{1}x+Z_{2}x^{2}+\cdots+Z_{t-2}x^{t-2}+Z_{t-1}x^{t-1}.\] (2) The dealer calculates \(f(x)\) and obtains \(\{f(x_{1j_{1}}),f(x_{2j_{2}}),\cdots,f(x_{tj_{t}})\}\). 6. The dealer recodes the \(f(x_{lj_{i}})\) by Pauli operation. Simultaneously, it prepares some decoy particles and randomly inserts them to the coding sequence. The dealer remembers the initial state and position of each decoy particles. The dealer selects the secure key rate \(R\) of the proposed protocol as the minimum of \(\{R_{1j_{1}},R_{2j_{2}},\cdots,R_{tj_{t}}\}\), \(R_{lj_{l}}=R_{j}\)[18] and sends them to all the participants according to the sequence of \(C_{lj}\). After affirming \(C_{lj}\) has received the coding sequence, the dealer announces the initially inserted state and position of each decoy particles to \(C_{lj}\). Then \(\{x_{j},f(x_{lj_{i}})\}\) becomes the private key of \(C_{lj}\). 7. Legitimate participants restore the secret \(S\) by the Lagrange interpolation. \[ \begin{array}{c}f(x)=f(x_{1j_{1}})\psi_{1}+f(x_{2j_{2}})\psi_{2}+\cdots+f(x_ {tj_{t}})\psi_{t}\\ =S+Z_{1}x+Z_{2}x^{2}+\cdots+Z_{t-1}x^{t-1},\end{array} \] (3) where \[\psi_{k}=\prod_{l=1,l\
eq k}^{t}\frac{x-x_{lj_{i}}}{x_{kj_{k}}-x_{lj_{l}}},\] (4) with \(x_{lj_{l}}\) equal to \(x_{j}\) for \(k,l\in\{1,2,\cdots,t\}\) and \(j\in\{1,2,\cdots,n\}\). Here, procedures of the GMCS-involved CVQSS is introduced in detail. Legitimate participants can share message from the dealer based on it. Furthermore, the security is a critical factor for a QSS scheme. The security analysis of QSS is typically more involved than that of QKD. The general security proof against eavesdroppers in the channels and dishonest participants who have only appeared recently. However, dishonest participants have more superiorities to undermine the secret sharing than eavesdroppers from outside. Consequently, the following security analysis primarily focuses on both eavesdroppers and dishonest participants.
## 4 Results and Discussion
### Single Circular Cylinder
And, finally, Point T repeats the same event already described for Point P. The discussed mechanism supported by Points P-T repeats periodically and alternately on the upper and lower side of the cylinder surface causing the well-known von Karman vortex street. It can be concluded that the turbulence modeling applied to the energy equation did not result in an expressive change in the aerodynamic coefficients and Strouhal number. These observations are coherent with results reported by [PERSON] et al. [42] for the DVM, in which the turbulence modeling did not expressively change the aerodynamic forces behavior in two-dimensions. On the other hand, the present methodology will be more sensitive under roughness effects. The effects of roughness will be investigated in a following paper. A distribution for the time-averaged pressure coefficient was calculated and compared with experimental data given by [PERSON] [43] (see Figure 8). The present methodology was able to reproduce the pressure distribution showing a good agreement also. Both simulations show a local minimum close to 70\({}^{\circ}\) and the inflection points of the curves after this point are an estimation of the separation point. The test case considering buoyancy effects showed a smaller pressure coefficient and a slight delay in the separation of the boundary layer (81\({}^{\circ}\), against 80\({}^{\circ}\) from test case (A)). The experimental result from [PERSON] and [PERSON] [44] predicts the separation angle for the hydrodynamic boundary layer to be about 78\({}^{\circ}\). The numerical result with buoyancy forces clearly shows an increase in the base pressure. This behavior is physically consistent with the drag reduction (Table 2). The numerical behavior for Ri = 0.168 tends to reflect the expected physics for the problem, i.e., a delay in the boundary layer separation. Figure 9a,b, respectively, show the position of the wake vortices and wake temperature particles for test case (B) at the intermediary step of the computation (\(t\) = 22.5). The  
Figure 8: Time-averaged pressure coefficient distribution around the cylinder [43]. formation and shedding of large eddies in the wakes can be clearly identified. This process occurs alternately on the upper and lower surfaces of the circular cylinder. The vortex and heat pairing processes can also be identified, where the vortical structures and the heat rotate in opposite directions and are connected to each other by the vortex sheet and heat sheet, respectively. The rightmost part of the wakes corresponds to the numerical transient that occurs before a periodic steady state regime is reached (from \(t=10\) on, approximately).
# Computational Load Analysis of a Galileo OSNMA-Read Receiver for ARM-Based Embedded Platforms +
## 5 Computational Burden Analysis of the Complete Receiver
### Real-Time Processor Load Analysis
It can be observed how, among the embedded boards, only ODROID-C4 is able to bear such a load, whereas both ODROI-X2 and Raspberry Pi4 stopped working before 80 s for a buffer overrun error. Indeed, as better detailed in [20], an intermediate buffer is used to store the samples coming from the USB FE and makes them available for the channels' elaboration, in the so-called producer-consumer relationship. When new samples from the USB FE are available but the intermediate buffer does not have free locations, a buffer overrun happens. The percentage of available memory locations in the intermediate buffer is therefore a good indicator of the real-time capability of the receiver, as reported in Figure 10b for the embedded boards only. Other aspects that should be carefully considered are the different clock values and speed controller policies (governors) of the processors under consideration. For instance, ODROID-X2 has been overclocked to 2 GHz [20] and the governor set to performance, while no specific setup has been forced on Raspberry Pi4 and ODROID-C4 (default governor set to on-demand and performance respectively). As is clearly visible from the above analysis, the maximum number of signals simultaneously tracked (and acquired) in real-time strictly depends on the platform, since the software functions' execution time relies on the computational power offered by the specific processor, and on the OS task scheduling policy. In this regard, some duration tests have also been performed running the receiver for days: such tests confirm that ODROID-C4 (platform 4) is able to bear up to 16 satellites: this can be considered as a maximum limit in this current setup and for this specific platform. That limit drops to 12 for both ODROID-X2 (platform 2) and Raspberry Pi-4 (platform 3). Anyway, as pointed out above, a possible performance improvement for the platforms 2 and 3 cannot be excluded when implementing, for instance, a different task-core allocation rule, currently under investigation. According to this, newer and possibly more powerful boards are expected to be able to bear at least 12 satellites, which could be considered somehow a lower bound of general validity.
# Experimental Investigation in Drag Coefficient of Cubes
## 1 Introduction
[PERSON] and [PERSON] investigated the settling behavior of the cube in both Newtonian and Power law liquids [21]. [PERSON] and [PERSON] found the velocity of spheres was always greater than that of the cubes with the equal volume in the accelerating region with very high Reynolds number [22]. With the help of numerical methods, [PERSON] and [PERSON] studied the drag coefficient of the cubic particle at different orientation angles in the range of \(10\leq Re\leq 2\times 10^{2}\)[23]. However, the investigation of the relationship between the cube drag coefficient with different settling orientation angles and Reynolds numbers is still limited, especially when the Reynolds number is larger than \(2\times 10^{2}\). The existing experimental studies on cubic particles primarily use homogeneous materials, which fail to suppress unsteady motions during settling and struggle to measure drag coefficients at specified orientation angles. To address this, we conducted free-settling experiments using non-uniform spliced cubes to stabilize orientation angles and reduce unsteady motions. This approach enabled precise measurements of drag coefficients at typical orientation angles across a wide Reynolds number range. Based on the experimental data, we analyzed the relationship between the cube's drag coefficient, orientation angle, and particle Reynolds number, and developed a reliable correlation to predict the drag coefficient for cube.
# Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification
## 2 Related Work
### Loss Function
#### 2.1.1 Cross-Entropy Loss  
The Cross-Entropy (CE) loss function is usually used for classification tasks, and it measures the difference between the predicted values of the neural network and the true values of the samples. It is calculated as follows:  
\[\mathcal{L}_{\text{CE}}=-\frac{1}{n}\sum_{i}^{n}\log\frac{e^{W_{y_{i}}^{T}:x_{i }+b_{y_{i}}}}{\sum_{j}^{C}e^{W_{j}^{T}:x_{i}+b_{j}}} \tag{1}\]
## 5 Simulation and Analysis  
We used OPNET Modeler 16.0 [34] for the simulation of the routing protocols. We set the channel capacity to be 11 Mbps for all mobile hosts. The rest of the simulation settings and parameters are summarized in Table 1.  
We have only one source node and one destination node for data packets. The 28 remaining nodes are potential forwarding nodes. For each protocol, and with the above set of parameters, we generate 30 independent scenarios using 30 different seeds of the pseudo-random number generator available in OPNET. By doing so, we have 30 sets of pseudo-independent results for every metric for every protocol. The 30 results are then averaged. Confidence intervals are also calculated.
## 4 Results and Discussion
### Prediction of Optimal Planting Zones for Se-Rich Crops
#### 4.2.1 Fuzzy Weights-of-Evidence
Moreover, the regional thresholds for Se-rich crops were set  
Figure 3: Evidence layers used in the FWofE methods ((a): 500-m nine-ring buffers of Silurian; (b): 500-m nine-ring buffers of Permian; (c): 500-m nine-ring buffers of Triassic; (d): 500-m nine-ring buffers of Cretaceous; (e): 500-m nine-ring buffers of Paleogene; (f): distribution map of Factor3 scores; (g): distribution map of Factor4 scores). too high, resulting in a scarcity of Se-rich crop samples in the study area. Considering these factors, the Se threshold for Se-rich rice defined by the \"Selenium-Enriched Rice\" (GB/T 22499-2008) standard was adopted as the basis for training data in the FWofE model. Crops with Se content ranging from 0.040 to 0.300 mg/kg were considered Se-rich for the purpose of this study [51]. Using the FWofE model, eigenvalues and Student's t-distribution values for each evidence layer were calculated, and a schematic diagram illustrating the selection of fuzzy weights was generated. The multivalued fuzzy membership function (MSF) was assigned for each evidence layer, after which the fuzzy weights of the evidence layers were calculated accordingly. The fuzzy weights of the evidence layers are shown in Table 13. By integrating multiple evidence layers, the posterior probabilities of Se-rich crops were calculated, and a posterior probability map of Se-rich crops in the study area was produced. The workflow of the constructed FWofE model is shown in Figure 4a. Figure 4: Schematic diagram of the FWofE (**a**), BP-NN (**b**), and SVR (**c**) model. Using the geometric interval method [13], the posterior probability map of Se-rich crops was divided into three zones: Se-low crop planting zones, second-class Se-rich crop planting zones, and first-class Se-rich crop planting zones (Figure 5a). This division delineated the optimized planting zones for Se-rich crops in the study area. According to Figure 5a, the areas covered by first-class Se-rich crop planting zones, second-class Se-rich crop planting zones, and Se-low crop planting zones were 27.97 km\({}^{2}\), 188.37 km\({}^{2}\), and 159.49 km\({}^{2}\), accounting for 7.44%, 50.12%, and 42.44% of the study area, respectively. The prediction results of the FWofE model showed significant spatial correlations with the distributions of geological strata and soil types. First-class Se-rich crop planting zones were primarily concentrated in the northeastern part of the study area, coinciding with the distribution of Silurian sandstone and yellow-brown earth. Conversely, Se crop content was relatively lower in calcareous soil areas. This conclusion aligns with the findings of [PERSON] et al. (2024) [52].
# Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems
## 5 The Proposed Prediction Models
### Direct Similarity Computation  
The Euclidean distance is a multi-dimensional distance computation method, and can be easily extended to measure the distance of QoS values.  
\[\overline{distance}=\frac{1}{|S_{uv}|}\sqrt{\sum\limits_{i\in S_{uv}}\left(q_{ ui}-q_{vi}\right)^{2}} \tag{1}\]  
where \(S_{uv}\) represents the service set, in which the services are commonly invoked by user \(u\) and user \(v\). A smaller value of Equation (1) means higher similarity. So the similarity of user \(u\) and user \(v\) can be computed as the reciprocal of the Euclidean distance of the invocation records, which is shown as follows.  
\[sim(u,v)=\frac{1}{1+\frac{1}{|S_{uv}|}\sqrt{\sum\limits_{i\in S_{uv}}\left(q_{ ui}-q_{vi}\right)^{2}}} \tag{2}\]  
where the number 1 in the denominator is a way of Laplacian smooth to avoid the denominator being 0. \(q_{ui}\), \(q_{vi}\) are the QoS values of user \(u\) to service \(i\) and user \(v\) to service \(i\), respectively. It can be seen that the high closeness of the invocation records will lead to high similarity. However, Equation (2) has two defects:  
1. The fluctuation range of QoS values can be very large. For example, the response time value may be any value in the range of 0 s-20 s or 30 s. If two users receive very different QoS values from the same service, the final similarity can be generated with a large bias.
2. In the case that the number of services commonly invoked by two users is small, the similarity computation result tends to be vulnerable. In an extreme case that two users only share one service that is commonly invoked, the similarity result turns to be quite unreliable.  
To solve the above issues, we improve Equation (2) as follows.  
\[sim(u,v)=\frac{1}{1+\frac{1}{|S_{uv}|}\sqrt{\sum\limits_{i\in S_{uv}}\left( \left(q_{ui}-\overline{q}_{i}\right)-\left(q_{vi}-\overline{q}_{i}\right) \right)^{2}}} \tag{3}\]  
where \(\overline{q}_{i}\) is the average value of QoS values received by user \(u\) and user \(v\) invoking service \(i\). Similar to the user similarity computation, the service similarity is computed as  
\[sim(i,j)=\frac{1}{1+\frac{1}{|U_{ij}|}\sqrt{\sum\limits_{u\in U_{ij}}\left( \left(r_{ui}-\overline{q}_{u}\right)-\left(r_{uj}-\overline{q}_{u}\right) \right)^{2}}} \tag{4}\]  
where \(r_{ui}\) and \(r_{uj}\) are the QoS values of user \(u\) invoking service \(i\) and service \(j\) respectively. \(\overline{q}_{u}\) denotes the average value of the QoS values of user \(u\) invoking services \(i\) and \(j\).