esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Comparative Experiments with Snapshot Serengeti Dataset
Similarly, when an identification problem is related to fine-grained classification, for example Lion female and male, the performance of ResNet-101 is obviously poor and the accuracy of SE-ResNeXt101 is still above 90%. From the above results and discussions, it is concluded that the SE-ResNeXt can obtain the differential characteristics of the wildlife monitoring images through the multi-branch aggregation and \"feature recalibration\" strategy, which can improve recognition performance without marking the locations of wildlife. Better experimental results are obtained on different datasets, which indicates that the integrated model has better generalization capabilities and performs more stably in recognition.
# Measuring Facial Illuminance with Smartphones and Mobile Devices
## 3 Results  
Device light sensor calibrations revealed three key findings. First, sensor response is highly linear, with all but one least square linear fit to the data having R\({}^{2}\) values in excess of 0.99. In all three environments, the sensor of the tablet reported higher values than the smart-phone (e.g., slopes of 0.70 vs. 0.52 with the hemispheric light source). The slopes below 1 for the wide field stimulus (Figure 4a), and the slopes > 1 for the small field stimulus (Figure 4b) reveal a narrower angular weighting for the detectors in each device compared to the wide field cosine weighting reported for the illuminance meter. Such narrower weighting will be more appropriate for visual field integration since the human monocular visual field is smaller than 2\(\pi\) sr due to restrictions of the brow, nose, and retina. For simplicity, we included the values of the slopes and the regression coefficients of the linear fitting lines of Figure 4a-c in Table 1. Results obtained under an office illumination environment with both devices (Figure 4c) indicate that the illuminance can be measure within a relative error lower than 25%.  
Figure 3: Facial illuminance being measured for a mannequin using a Samsung S6 Edge Plus smartphone camera.  
Figure 4: Comparison of illumininance measured by Galaxy Tab S2 tablet (red circles) and the Samsung S6 + Smartphone (blue triangles) ambient light sensors to those measured with calibrated illuminance meter (Konica Minolta Sensing T-10A). (**a**) Wide angle homogeneous source; (**b**) Small angular source; (**c**) Office environment. The dashed black line represents the line Y = X.  
Face illuminance was computed from camera pixel values by applying Equation (4) to the average pixel value obtained from the camera images surrounding the eyes are compared to those obtained from the smart-phone light sensor (Figure 5). The two faces with darker skin are expected to reveal lower estimates because of reduced skin reflectance. Table 2 shows the values of the fitting lines to the data in Figure 5.
## 4 Results and Discussion
### Thermal Energy System
#### 4.1.3 Environmental Impact Analysis  
The environmental impact assessments for the solar thermal system are detailed in Figures 6 and 7 using the ReCiPe 2016 method [26]. Figure 6, which presents an endpoint analysis, aggregates the broader impacts into categories like human health, ecosystem quality, and resource depletion. In the process level endpoint damage, solar thermal is driven by parabolic concentrator manufacture (0.63 mPt), heat transport fluid production (0.25 mPt) and thermal storage tank construction (0.32 mPt).  
This shows that while solar thermal systems excel during operation, largely due to their reliance on renewable energy, the manufacturing stages raise potential risks, including those related to fine particulate matter formation.  
In contrast, Figure 7 provides a midpoint analysis that dissects specific environmental stressors such as global warming potential, acidification, and particulate matter emissions.  
Figure 7: Midpoint environmental impact analysis (ReCiPe 2016) for solar thermal energy system at IIT Roorkee.  
The insights suggest that although the operational benefits are clear, significant environmental improvements could be realized by targeting reductions in fossil energy consumption during upstream manufacturing processes.
# An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction
## 4 Experimental Results
### Effect of the Training Sample Size
Figure 4: R\({}^{2}\) varying with training sample size of different surrogates in (**a**) North America, (**b**) Western Europe, (**c**) Caribbean Sea, (**d**) South America, (**e**) Russia, (**f**) Middle East, (**g**) East Asia and (**h**) Oceania. For clarity, R\({}^{2}\) of the PRS surrogate are indicated by the dashed purple line with the purple \(y\)-axis on the right, and the others are presented by the solid line with the black \(y\)-axis on the left.
# On-Edge Deployment of Vision Transformers for Medical Diagnostics Using the Kvasir-Capsule Dataset
## 3 Proposed System Architecture
### Neural Network Architectures
#### 3.2.1 EfficientFormerV2  
EfficientFormer [34] was designed specifically for image classification tasks, aiming to run faster than MobileNet models but maintain small sizes. EfficientFormer achieves that by using CNNs to capture local features, followed by transformer blocks capturing global ones. This allows for results that balance between size, latency, and performance. EfficientFormerV2 [28] was then introduced with a more fine-grained joint search strategy that saves hardware resources, significantly improving latency. Four versions of EfficientFormerV2 exist: EfficientFormerV2-S0, EfficientFormerV2-S1, EfficientFormerV2-S2, and EfficientFormerV2-L. EfficientFormerV2-S2 was chosen as it performs better than EfficientFormerV2-S0 and EfficientFormerV2-S1 without compromising latency and size the way EfficientFormerV2-L does [28].
## 3 Experiments
### Intuitive Results of Saliency Detection  
To demonstrate the effectiveness of our frequency-based saliency detection method, experiments with existing sample-free saliency detection methods are carried out. The results are shown in 5 challenging surface scenes, including huge reflections with big waves, striped ripples, slender reflections, huge circular waves under uneven illumination, and far-view reflections. Figure 6 shows the results.  
Figure 6b-f show the results of five popular sample-free saliency detection methods. In Figure 6c,d, large ripples are mistaken for floating objects. In Figure 6b,e, near-view reflections cause plenty of false detections. Though some ripples are removed by the frequency-based method SR, shown in Figure 6f, important information of floating objects is removed as well. From these intuitive results, a preliminary conclusion can be drawn that the detection results of our method shown in Figure 6g are optimal in the task of floating object detection with respect to various interference factors.  
Figure 6: Saliency detection results in complex surface scenes (from top to bottom, they are large ripples, far-view reflections, near-view reflections, strip ripples, circular ripples, respectively). The methods are (**a**) original image, (**b**) result of Boolean Map for Saliency Detection (BMS), (**c**) result of Frequency-tuned method (FT), (**d**) result of Global Contrast-based Saliency Detection (RC), (**e**) result of Contrast-based Filtering for Saliency Detection (SF), (**f**) result of Spectral Residual approach (SR) and (**g**) ours.
## 3 Adaptive In-Transit Co-Processing
### Calculating Importance
```
1:\(\mathcal{B}=\{1,2,\ldots,N\}\): set of all \(N\) blocks
2:\(\mathcal{F}\): set of all active filters in pipeline
3:\(\mathcal{W}=\{w_{p}\}\): set of weights, where \(w_{p}\) is the weight of probe \(p\)
4:
5:\(\mathcal{I}=\{i_{1},i_{2},\ldots,i_{N}\}\): set of importances, where \(i_{b}\) is the importance for block \(b\in\mathcal{B}\)
6:\(\mathcal{A}=\{a_{1},a_{2},\ldots,a_{N}\}\): set of actions, where \(a_{b}\) is the action for block \(b\in\mathcal{B}\)
7:for each block \(b\in\mathcal{B}\)do
8:\(i_{b}\gets 0\); \(\triangleright\) Importance initially set to zero for block \(b\)
9:\(a_{b}\leftarrow\) NONE; \(\triangleright\) Action initially set to NONE for block \(b\)
10:for each filter \(f\in\mathcal{F}\)do
11:if\(a_{b}\) is in the scope of \(f\)then
12:for each probe \(p\) of filter \(f\)do\(\triangleright\)\(p\in\{\) Mean, Range, SD, AVGSEQ, Distinct, Entropy \(\}\)
13:if\(p\) analyzes a sample which is not allocated then
14: allocate sample of block \(b\);
15:if\(p\) analyzes a block which is not allocated then
16: allocate block \(b\);
17:\(i_{b}=i_{b}+p(b)/w_{p}\); \(\triangleright\)\(p(b)\) calculates the importance for probe \(p\) on block \(b\)
18:if\(i_{b}\) satisfies the condition of filter \(f\)then
19:\(a_{b}\leftarrow\) action of filter \(f\); \(\triangleright\)\(a_{b}\in\{\) NONE, Skip, RLE, LZ77, HOMO \(\}\)
20: Free the allocated memory of block \(b\);
21:return\(\mathcal{I}\), \(\mathcal{A}\);
```  
**Algorithm 1** Scheme used to calculate the importance of all blocks. The importance is used to determine the action of each block. A filter contains zero or more probes, which are used to analyze each block in sequence. Each probe executed by a filter is assigned a custom weight (by default, 1). Given a block \(b\), a filter \(f\) and a set of probes for filter \(f\), \(\mathcal{P}_{f}\), the resulting importance of block \(b\), \(i_{b}\), after a filter has been applied is  
\[i_{b}=\sum_{p\in\mathcal{P}_{f}}\frac{p(b)}{w_{p}}, \tag{1}\]  
where \(w_{p}\) is the weight of probe \(p\) and \(p(b)\) is the function by probe \(p\) to calculate the block importance of block \(b\). A filter also contains an action, a condition, and a scope. An action specifies how a block should be handled during iterating through each element in the block and then calculating the average value. In our testing, six different probes were used. The six probes are defined as follows:  
* Mean. Calculates the mean of all values in a block. * Range. Calculates the range of the values in a block. * SD. Calculates the standard deviation of the values in a block. * AVGSEQ. Calculates the average sequence length of identical values in a block. This probe is only used on non-sampled data, as sampled data would not retain enough information about the average sequence length. * Distinct. Calculates the number of distinct values compared to the total number of values in a block. * Entropy. Calculates the entropy of a block [10].
# A Blockchain-Based Cooperative Authentication Mechanism for Smart Grid
## 4 Our Proposed Mechanism
### The Dynamic Change Method of Smart Devices and Information
#### 4.3.3 Device Logout  
The device \(D_{i}\) can actively exit a domain. To do so, \(D_{i}\) signs the certificate with its own private key to obtain \(sig(Cert_{i})_{sk_{i}}\) and sends it to the domain manager \(M_{A}\) to request an exit from Domain A. \(M_{A}\) verifies the legitimacy of the signature and terminates the exit request if it is not legitimate. If the signature is legitimate, \(M_{A}\) forwards the request to the blockchain proxy node \(P_{A}\).  
Upon receiving the request, \(P_{A}\) updates the certificate status _state_ in the federated chain to 0 and reaches consensus through the consensus algorithm. After that, \(P_{A}\) sends the update result back to \(M_{A}\). \(M_{A}\) deletes the certificate information about device \(D_{i}\) in the local database upon receiving the update result and then sends the exit result back to the device, indicating that it has successfully exited Domain A.
# Resource Allocation in C-V2X Mode 3 Based on the Exchanged Preference Profiles
###### Abstract  
In this paper, we investigate the resource block (RB) allocation problem in cellular vehicle-to-everything (C-V2X) networks mode 3, where the cellular networks schedule the RBs for direct vehicular communications. First, we establish the communication model and introduce the effective capacity and queuing theory to describe the reliability of vehicle-to-vehicle (V2V) links. Then, we introduce the \(\alpha\)-fair function and formulate the joint power control and RB allocation problem considering the allocation fairness and the different quality-of-service (QoS) requirements for vehicle-to-infrastructure (V2I) and V2V links. Our objective is to maximize the sum capacity of all V2I links with the \(\alpha\)-fair function while guaranteeing the allocation fairness among V2I links and the transmission reliability for each V2V pair. To achieve this objective, we propose a novel matching game theory algorithm based on the exchanged preference profiles between the two participant sets, i.e., V2V and V2I links. Simulation results show that our proposed algorithm is adaptive to the dynamic vehicular network and achieves better efficiency and fairness trade-offs, outperforming the classic allocation method.  
C-V2X; mode 3; resource allocation fairness; matching game theory; preference profiles Article
# Functional and Healthy Features of Conventional and Non-Conventional Soundoughs
## 5 Bioactive Compounds  
Cereals contain different phytochemicals, such as phenolic acids, phytosterols, alkyl-resorcinols, tools, lignans, and folate [79]. Among other processing conditions, e.g., milling and malting, the sourdough fermentation is the one that most affects the levels and bioavailability of phytochemicals and increases the level of extractable phenolic compounds [14, 80]. Furthermore, bioactive compounds are synthesized during fermentation, while other components involved in grain-related digestion problems or pathologies, such as gluten sensitivity or gastrointestinal syndromes, are reduced [81].  
As reported by [PERSON] et al. [82], folate and other free phenolic acids increased up to seven and ten times in germinated rye during sourdough fermentation. Moreover, comparing the capability of different yeasts and LAB to affect the folate content in a rye sourdough, it was demonstrated that the synthesis of folate by bacteria was minimal, while the yeasts were able to increase its content over three-fold in the best case [83].  
Regarding the vitamin E,tocopherol and locotrienol, a reduction during the sourdough preparation and dough making has been reported, probably due to the sensitivity of the compounds to the air [14].  
Several authors have stated LAB as the microbes most suitable in bioactive peptides enrichment [55, 84]. According to [PERSON] et al. [49, 55], selected sourdough lactobacilli with specific proteinase and peptidase activities toward cereal proteins, were effectively used for releasing Angiotensin I-Converting Enzyme (ACE)-inhibitory peptides during a long-time sourdough fermentation. These peptides are of great interest for functional foods as they may be used for avoiding hypertension and for other therapeutic purposes [55]. Another study showed that sourdough LAB can increase the concentration of lunasin, a cancer-preventing peptide, during fermentation of various flours, including those of wheat, barley, amaranth, soybean, or rye [85]. Specifically, _L. curvatus_SAL33 and _L. brevis_ AM7 strains used as sourdough starters were able to synthesize this compound, increasing its concentration up to 2-4 times during fermentation.
## 6 Concluding Remarks  
In this paper, we have proposed an underwater routing scheme, namely _AURP_ (AUV-aided underwater routing protocol), to address the problem of the low delivery ratio and high energy consumption in a multi-hop underwater acoustic sensor network (UASN). To achieve a high data delivery ratio and low energy consumption, AURP exploits controlled mobility of multiple autonomous underwater vehicles (AUVs) as well as heterogeneous acoustic communication channels.  
In order to verify the proposed scheme, we have incorporated a realistic underwater channel model and various AUVs' movement patterns with NS2 simulators. The simulation results have shown that AURP can outperform other routing protocols in terms of the delivery ratio and energy consumption, and is a good candidate for many practical applications that require mass data transmissions with a high reliability.
## 2 Background and Related Works
### Background
#### 2.2.4 Relevant Triple Relationship Extraction  
Relevant triple relationship extraction involves identifying the structural graph patterns implied by the natural language input--specifically, which and how entities are connected.  
A triple in graph query terms is composed of (subject node)-[:relationship]\(>\)(object node). In Cypher, this translates into patterns like (:Person)-[:WORKS_AT]\(>\)(:Company).  
The extracted triple relationships guide the MATCH clause of Cypher queries by specifying the traversal path through the graph. These patterns are derived from understanding the semantic relationships expressed in the user's question.  
**Example:**: _\"Can you provide a list of actors who appeared in movies directed by [PERSON]?\"_  
Generated Cypher:  
MATCH (a:Actor)-[:ACTED_IN]\(>\)(:Movie)\(<\)[:DIRECTED]-(d:Director)  
WHERE d.name = \"[PERSON]\"  
RETURN DISTINCT a.name  
Relevant triples:  
(Actor)-[:ACTED_IN]\(>\)(Movie)  
(Movie)\(<\)[:DIRECTED]-(Director)  
Extracting these relevant triples correctly ensures that the model builds valid and meaningful graph traversal patterns reflecting the user intent. Without this step, even if the key-value pair is correct, the query may follow the wrong path and produce irrelevant or incomplete results.
## 3 The Receiving Process of the Composite Signal
### The Tracking Process of a BOC Signal  
The reception of a BOC signal is divided into two processes: acquisition and tracking. Acquisition realizes the rough estimation of the BOC signal PN code offset and Doppler frequency, and tracking achieves the high accuracy estimation of BOC signal parameters. The acquisition method used in this composite signal is the same as the traditional BOC signal acquisition method [27], which is not discussed here. After obtaining the rough estimations of the code offset and Doppler offset, the system inserts the two values into the tracking system for high accuracy estimation. Figure 4 shows the structure of tracking loop.  
The tracking loop is composed of a carrier tracking loop, code tracking loop, and subcarrier tracking loop. In addition, the numerically controlled oscillator (NCO) generates the local carrier according to the tracking discriminator. The carrier tracking loop adopts the second-order frequency locked loop (FLL) to assist the third-order phase locked loop (PLL), which has excellent performance under high dynamic conditions. The loop can track the frequency and phase of the carrier [29; 30]. The phase discriminators of the FLL and PLL are shown in Equations (8) and (9), respectively:  
\[e_{fk}=a\tan 2(Cross,Dot) \tag{8}\]  
\[e_{pk}=a\tan(Q_{ps},I_{ps}) \tag{9}\]  
where \(Cross\) and \(Dot\) are the cross product and dot product of the \(I\) channel and \(Q\) channel, respectively. \(Q_{ps}\) is the integration result of the \(Q\) channel, and \(I_{ps}\) is the integration result of the \(I\) channel.  
The code tracking loop adopts the delay locked loop (DLL), which achieves synchronization between the PN code offset generated locally and the received signal. Its inputs are the integration and accumulation results of the early, prompt, and delay channel data of the \(I\) and \(Q\) channels [31]. The interval between the three channels is a half chip. In addition, the discriminator is shown in Equation (10):  
\[ec_{k}=\frac{[I_{es}(k)-I_{ls}(k)]I_{ps}(k)+[Q_{es}(k)-Q_{ls}(k)]Q_{ps}(k)}{I_ {ps}^{2}(k)+Q_{ps}^{2}(k)} \tag{10}\]  
Figure 4: The structure of the tracking loop.  
where \(I_{es}\), \(I_{ps}\), and \(I_{ls}\) are the integration and accumulation results of the \(I\) channel delay, prompt, and early code, respectively. In addition, \(Q_{es}\), \(Q_{ps}\), and \(Q_{ls}\) are the integration and accumulation results of the \(Q\) channel delay, prompt, and early code, respectively.  
The subcarrier code tracking loop has the same DLL structure, and is named the subcarrier delay locked loop (SDLL) [32]. The loop achieves synchronization between the local subcarrier and the received subcarrier. However, the inputs of the SDLL are the integration and accumulation results of early, prompt, delay channel data of subcarriers.
## 2 Video Camera Imaging Based Method  
### Basic Framework  
Using video camera imaging, a series of image and signal processing techniques are required to extract vital signs from the image. Figure 2 shows the basic framework, which includes data acquisition, ROI detection, raw signal extraction, noise artefact removal and vital sign extraction.
# A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose
## 1 Introduction
We can choose multiple evaluation indicators and calculate their weights based on EWM to avoid the influence of human subjective factors, and then the sensor array can be evaluated in multiple dimensions. TOPSIS (technique for order preference by similarity to ideal solution) is an effective evaluation method for dealing with multi-indicator problems, but it does not involve the weight of evaluation objects [17]. If we combine the EWM with TOPSIS, we can overcome this defect of the TOPSIS method, and can establish a comprehensive evaluation model to optimize the sensor array. In order to solve the above-mentioned problems, this paper proposes a comprehensive evaluation model to realize the optimization of sensor array for E-nose. The multi-dimensional evaluation of sensor array was carried out according to the four indicators of sensitivity, selectivity, correlation, and repeatability, and a comprehensive evaluation model for sensor array optimization was designed based on the EWM-TOPSIS algorithm. In order to verify the effectiveness of the as-proposed model, it was applied to the optimization experiment of a E-nose system composed of 10 sensors, and the abilities of the E-nose to identify the CO-CH4 gas mixtures were investigated before and after the sensor array optimization.