esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### CW Modulation Spectra  
Figure 8 shows a comparison of the difference reflection spectrum in the interband transition energy region caused by plasmon modulation and by current modulation (current = 1.56 A) with the temperature difference spectrum (\(\Delta\)T = 50 K). The contribution due to temperature rise is comparable for both modulation spectra since the laser intensity was 4.0 \(\times\) 10\({}^{-1}\) W/cm\({}^{2}\) and the amount of heat generated per unit time and unit area by a current of 1.56 A is 3.1 \(\times\) 10\({}^{-1}\) W/cm\({}^{2}\). The plasmon and the current modulation spectra qualitatively agree with the second derivative of the reflectance as expected for a modulation of the joint-electron distribution function of the valence band and the conduction band. However, the plasmon modulation spectrum is distinctly different from the current modulation spectrum and differs from the temperature difference spectrum, as well.  
The current modulation spectra of metals were previously studied by [PERSON] and [PERSON] [20]. They used the electric current as a method of Joule heating to obtain thermo-reflection spectra of Al, Au and Cu. The observed spectral shapes for all the metals have a characteristic feature of the second derivative of the stationary reflection spectra. They argued that this is caused by broadening of the Fermi distribution by temperature increase. However, they did not study the dependence on the magnitude of the current, so that the shift in the zero-crossing was not reported previously.  
Although the incidence angle is different between the plasmon/current modulation and temperature difference spectra, the reflection spectra are nearly the same, so that the difference in the incidence angle is irrelevant to the discrepancy noted above. Currently, there is no theoretical explanation for this observation, and this signal may contain some signature of the \(k\)-space wave function of the plasmon.
# DSVN: A Flexible and Secure Data-Sharing Model for VANET Based on Blockchain
## 3 System Preview
### Definitions
#### 3.2.3 Security Definition  
1. The computational Diffie-Hellman (CDH) assumption: Suppose there are cyclic groups \(\mathbb{G}_{0}\) and \(\mathbb{G}_{T}\) of the same prime order \(p\). \(g\) is a generator of \(\mathbb{G}_{0}\), and \(e:\mathbb{G}_{0}\times\mathbb{G}_{0}\rightarrow\mathbb{G}_{T}\) is a bilinear pair. Choose \(a\),\(b\) randomly from \(\mathbb{Z}_{p}\). The computational Diffie-Hellman problem constructs a polynomial adversary \(\mathcal{A}\) that takes the tuple \((\mathbb{G}_{0},p,g,g^{a},g^{b})\) as input and outputting \(e(g,g)^{ab}\in\mathbb{G}_{T}\), which has the advantage: \[Adv_{\mathcal{A}}=\Pr[\mathcal{A}(\mathbb{G}_{0},p,g,g^{a},g^{b})=e(g,g)^{ab}].\] (1)  
**Definition 1**.: _The CDH assumption holds if no probabilistic polynomial-time adversary \(\mathcal{A}\) has a nonnegligible advantage in solving the CDH problem._
2. Security model: Below, we define the ciphertext indistinguishability under chosen-plaintext attacks.  
Setup. The challenger \(\mathcal{C}\) runs the initialization algorithm, generates the public parameters, and sends them to the adversary \(\mathcal{A}\).  
Phase 1. The adversary \(\mathcal{A}\) is allowed to select a set \(S\) of attributes for a key query. The challenger \(\mathcal{C}\) randomly runs the registration algorithm and returns the result to \(\mathcal{A}\).  
Challenge. The adversary \(\mathcal{A}\) submits challenge access structure \(T^{*}\) and equal-length messages \(M_{0}\) and \(M_{1}\), which are sent to the challenger. The challenger \(\mathcal{C}\) chooses \(\theta\in\{0,1\}\) randomly, and runs pre-encryption and re-encryption algorithm to encrypt \(m_{\theta}\) and generates ciphertext \(CT^{\prime}\). Then \(\mathcal{C}\) return \(CT^{\prime}\) to \(\mathcal{A}\).  
Phase 2. The adversary \(\mathcal{A}\) can make a key query in the same way as in Phase 1, except that the set of attributes \(S\) satisfying access structure \(T^{*}\) related to the challenge ciphertext \(CT^{\prime}\) cannot be queried in the key query phase.  
Guess. The adversary \(\mathcal{A}\) outputs a guess \(\theta^{*}\) for \(\theta\). If \(\theta^{*}=\theta\), then \(\mathcal{A}\) wins the game. The \(\mathcal{A}\)'s advantage is defined:  
\[Adv_{\mathcal{A}} =|\Pr[\theta^{*}=\theta]-\frac{1}{2}|. \tag{2}\]  
**Definition 2**.: _If the adversary \(\mathcal{A}\) cannot selectively win by a non-negligible advantage in polynomial time, the scheme is secure against Chosen-Plaintext Attacks._
## 3 Background  
### Cooperative Virtual MIMO  
MIMO techniques are capable of providing high system performance without additional transmission power and bandwidth. However, due to the small form factor and limited energy of sensor nodes, it is often not realistic to equip each sensor with multiple antennas to implement MIMO. Instead, a cluster of single-antenna sensor nodes can cooperate to form a virtual antenna array (VAA) to achieve virtual MIMO communication. Virtual MIMO systems are distributed in nature because multiple nodes are placed at different physical locations to cooperate with each other. With proper timing and frequency synchronization between constituent nodes of the VAA, virtual MIMO can realize the advantages of true MIMO techniques for WSNs.
## 3 Discussion
The additional pixel information resulting from the color space transformations enhances the discriminatory power of the segmentation model. Supervised ML models demonstrated improved segmentation quality, which is consistent with the finding of [PERSON] et al. [29], who suggested that ML techniques that learn color features from multiple color orientations improve the robustness of the segmentation model. While the SVM model performed well, its high computational speed and memory requirements make it less feasible for high-throughput plant phenotyping. After segmenting the images, machine learning regression models were developed based on the different segmentation methods to predict plant SPAD values. Generally, the method with good segmentation quality produced high prediction accuracies. In this case, the MLP segmentation method outperformed the others, achieving a high R\({}^{2}\) (0.904, 0.821) and low RMSE (2.862, 3.680) for both greenhouse and field plants. This is in line with the [PERSON] et al. (2021) study, which asserts that neural networks have high potential in estimating the nitrogen nutrition index (NNI) in rice compared to other statistical and machine learning algorithms. They developed six machine leaning models, namely SVM, ANN, RF, k-nearest neighbor (KNN), partial least square regression (PLSR), and adaptive boosting (AB), based on rice RGB images from an unmanned aerial vehicle (UAV). Their results show that ANN can estimate the NNI at the early pointing and filling stages of rice with R\({}^{2}\) = 0.72 and 0.80, respectively. While [PERSON] et al. (2025) achieved a similar performance in predicting soybean chlorophyll by using a SVM model (R\({}^{2}\) = 0.86 and RMSE = 3.20), their method was based on a few randomly selected leaves which may not represent the actual scenario of chlorophyll dynamics in the field. Moreover, their object-recognition method (segmentation) is only feasible for small plant datasets. Overall, the MLP method achieved the best performance in all the conditions tested in this study. It produced the highest segmentation quality with lower variations. The MLP method is versatile and can be used to segment not just green vegetation in the glasshouse but also field-based plant leaves with variable colors resulting from, for example, a nutrient deficiency or senescence.
# RMFNet: Redetection Multimodal Fusion Network for RGBT Tracking
## 4 Implementation Details
### Online Tracking  
During online tracking with RMFNet, the three branches' last multi-domain binary classification fully connected layers need to be replaced with new classification layers to allow the tracker to learn instance representation features of new video sequences. The network needs to be fine-tuned to track a new video sequence using the first frame's marked target. The training process is similar to MDNet [12]. Specifically, the tracking process of the tracker is shown in Algorithm 1. During tracking, the convolution layers and  
Figure 6: Alpha-Refine module process.  
the parameters of the first two classification layers, FC4 and FC5, trained on the previous dataset, are loaded, and the network is fine-tuned using the first frame's image. Around the ground-truth box of the target in the first frame, 500 and 5000 positive and negative sample boxes \(S_{1}^{+}\) and \(S_{1}^{-}\) are generated and used for 50 iterations of fine-tuning training, updating only the parameters of the classification layers. To obtain precise bounding boxes, we also trained a bounding box regression network on the first frame's target, which is used for regression adjustment of reliable pre-localization in subsequent frames. We collected samples near the ground truth in the first frame for long-term and short-term network updates. When tracking the target at frame \(t\), 256 candidate boxes are generated around the ground truth of \(t-1\) in the t-th frame, and the network calculates their scores \(f(X_{t}^{i})\), and the one with the highest score is the location of the target predicted by the tracker. The score calculation formula is shown in Equation (8):  
\[X_{t}^{*}=\underbrace{\text{argmax}}_{i=0,\dots,255}f\left(X_{t}^{i}\right) \tag{8}\]  
```
Input : Pretrained CNN filters { \(W_{conv}\), \(W_{fc4-fc5}\)}.  Initial target state \(X_{1}\). Output : Estimated target state \(X_{t}^{*}\).
1: Randomly initialize \(W_{fc6}\) ;
2: Train a bounding box regression model \(\mathbf{B}\) R(-) ;
3: Draw positive and negative samples \(S_{1}^{+}\), \(S_{1}^{-}\) ;
4: Update \(\{W_{fc}\}\) using \(S_{1}^{+}\) and \(S_{1}^{-}\);
5: Initialize short-term and long-term samples \(\zeta_{5}\leftarrow\{1\}\)and \(\zeta_{l}\leftarrow\{1\}\)
6:repeat
7: Draw target candidate samples \(X_{t}^{i}\).
8: Compute the optimal target state \(X_{t}^{*}\) by (8).
9:if\(f(X_{t}^{*})\) >0.5 then
10: Draw training samples \(S_{t}^{+}\) and \(S_{t}^{-}\);
11: \(\zeta_{5}\leftarrow\zeta_{5}\cup\{t\}\), \(\zeta_{l}\leftarrow\zeta_{l}\bigcup\{t\}\)
12: \(X_{t}^{*}\) = \(\mathbf{B}\) R(\(X_{t}^{*}\));
13:if\(f(X_{t}^{*})\) <0.5 then
14: Performing short-term updates \(\{W_{fc}\}\) with \(\zeta_{5}\).
15:else if t mod 10 = 0 then
16: Performing long-term updates \(\{W_{fc}\}\) with \(\zeta_{l}\).
17: until end
```  
**Algorithm 1**Online Tracking
## 4 Discussion
### Uncertainty  
Remote-sensing products may have uncertainties due to sensor characteristics, cloud and atmospheric conditions, and processing algorithms. These uncertainties can affect the accuracy and reliability of the data. Although this study validated PML and MODIS products based on 11 flux sites, the limitations of the data station availability only allowed us to obtain data from one cropland site. Since croplands constitute more than 20% of the study area, collecting more sites for a comprehensive assessment of remote-sensing product accuracy is a future goal. Furthermore, this study utilized several different remote-sensing products. An overlay analysis typically requires resampling all datasets to the same spatial resolution, which may alter the values of certain pixels. Employing imagery with the same resolution could contribute to further enhancing the reliability of results. Incorporating additional products or models is another way to improve accuracy. Additionally, the use of remote-sensing data with higher spatial and temporal resolution than those used in this study may lead to more meaningful conclusions and is worth further exploration.
## 2 Mechanical, Bulk Electrical Energy Storage (EES), and the Potential of CAES
Proposed aquifer storage potential for the UK would be offshore [15], thereby increasing costs, which currently thus seems less likely than salt cavern storage. Depleted field storages appear even more unlikely with a potential hazard posed by residual hydrocarbons in the depleted gas formation. Introducing compressed air presents the risk of ignition and explosion, both underground and during discharge [28]. Additionally, and although more expensive options, gas storages have and still operate in abandoned mines and unlined or lined, conventionally mined rock storages. Similar constructions could host CAES in regions lacking cheaper geological alternatives [7; 8] and have been considered (S1, Tables S4-S6). Various CAES test facilities have operated briefly in Japan and Korea, and long-standing plans for CAES in a former limestone mine at Norton, Ohio were finally shelved in 2013 [29]. Small tests for adiabatic CAES are currently ongoing in an unlined Swiss tunnel [30] and a lined old mine working in Austria [31]. Whilst under consideration in, for example, USA, Mongolia, and Australia, such storages may be considered unlikely in the UK. Non-geological CAES schemes offering storages of small volume. Though not considered here they include aboveground, or shallowly buried steel vessels or pipes [32; 33], energy bags secured to the seabed [34], wind turbines linked with energy storage in supporting legs [35], or those in which power is converted directly from the rotor by means of gas/air compression within the rotor blades [36].
## 4 Security Notions
### Security Analysis  
We can prove that our proposed solution will withstand the other attacks discussed in Section 2.6 and ensure the integrity of the aggregated data, whether total aggregation or local aggregation.  
Eavesdropping resistance  
Our proposed scheme supports the openness of communication flow. Whether it is the internal node with access to the communication flow in a community or the external eavesdropper, they can only get the encrypted individual data \((x^{i}_{(j,d)}+r^{i}_{(j,d)})\), local aggregation value \((LS(i,d),LT^{i}_{(j,d)})\) or total aggregation value \((AS(d),AT(i,T))\) sent by GW to CC. However, all of them can not obtain the fine-grained data. We have proved that even if all but one node is compromised, object metering still cannot be leaked. Hence, the proposed encryption method satisfies the security requirement \(R1\).  
False command from the GW  
The GW attempts to obtain object user's meterings by issuing false billing commands in the name of [PERSON], even if he cannot compromise its pairwise nodes. He tries to obtain valuable information from them at any timestamp. However, even so, he can only get the indistinguishable, individual meterings, due to the Equations (14)-(17).  
We cannot exclude the possibility that all pairwise keys of \(user_{i}\) at a timestamp are all compromised nodes by the malicious aggregator or external attackers. In this case, the object \(user_{i}\)'s privacy is exposed. That is the \(user_{i}\) does not select any one honest node, then the probability is \(1-(\frac{k}{n-1})^{n-1-|c|}\). Obviously, the larger the value of \(|c|\) is, the smaller the value of \(k\) is, and the bigger the probability is. We improve the probability as much as possible and assume \(n=1000\), \(k=30\), and \(|c|=500\) (\(50\%\) nodes are compromised), and then the probability is \(2.47\times 10^{-7}\), so much small probability implies it is almost impossible that one user does not select any one honest node in one timestamp. Even if we fix a bigger pairs period \(T=1\) month, then we would have to cost 38.51 years to acquire individual data.
## 3 Results. Maximum Power Point (MPP) Determination
### R.T.C. France Solar Cell and Photowatt PWP201 Solar Panel  
In the first place, the data from the R.T.C. France solar cell and the Photowatt PWP201 solar panel are used. Two points of the \(I\)-\(V\) curve, with their corresponding slope (one at each side of the MPP), were selected for both photovoltaic devices:  
* R.T.C. France solar cell. Point 1: (\(V_{i}\), \(I_{i}\)) = (0.4137, 0.728), and Point 2: (\(V_{i}\), \(I_{i}\)) = (0.4784, 0.632).
* Photowatt PWP201 solar panel. Point 1: (\(V_{i}\), \(I_{i}\)) = (11.8018, 0.963), and Point 2: (\(V_{i}\), \(I_{i}\)) = (13.1231, 0.8725).  
The results from the parameter extraction are included in Tables 4 and 5, together with results from other authors' works (the results can also be compared to the ones included in Tables 2 and 3). From these results, it can be assumed that the proposed method presents similar (but slightly larger)values of the normalized root mean square error, \(\xi\), when compared to other methods that use the MPP instead of an operation point that replaces it. Besides, it also seems that the accuracy is higher if the voltage of operation point is larger than the voltage at MPP. Finally, it should also be highlighted that the accuracy depends on the determination of the slope of the \(I\)-\(V\) curve at the short-circuit point, which is quite challenging in this case due to the lack of points within this area in both \(I\)-\(V\) curves from the aforementioned work of [PERSON] et al.  
With regard to the maximum power point (MPP) estimated from the equivalent circuit based on the calculated parameters, the results are the following:  
* R.T.C. France solar cell (Point 1): (\(V_{mp}\), \(I_{mp}\)) = (0.4529, 0.6874), which implies a 0.197% error with regard to the maximum power supplied (Table 1).
* R.T.C. France solar cell (Point 2): (\(V_{mp}\), \(I_{mp}\)) = (0.4497, 0.6932), which implies a 0.329% error with regard to the maximum power supplied (Table 1).
* Photowatt PWP201 solar panel (Point 1): (\(V_{mp}\), \(I_{mp}\)) = (12.5249, 0.9212), which implies a 0.018% error with regard to the maximum power supplied (Table 1).
* Photowatt PWP201 solar panel (Point 2): (\(V_{mp}\), \(I_{mp}\)) = (12.6174, 0.9157), which implies a 0.155% error with regard to the maximum power supplied (Table 1).
## 2 Related Work
### Optical Character Recognition (OCR) for Labels  
Optical Character Recognition (OCR) technology, a cornerstone of automated text extraction from images, has undergone remarkable advancements in recent decades. These advancements have transformed OCR from a niche technology into a widely adopted tool for diverse applications, ranging from document digitization and archival to automated data entry and information retrieval. A plethora of OCR engines are currently available, each possessing varying strengths and weaknesses in terms of accuracy, speed, language support, and robustness to image variations. These engines span the spectrum from sophisticated commercial solutions, often incorporating proprietary algorithms and extensive training datasets, to accessible open-source tools, fostering wider accessibility and community-driven development.  
Among the prominent open-source OCR engines, Tesseract OCR [10] and EasyOCR [9] stand out as particularly influential and widely utilized. EasyOCR, in particular, has garnered significant attention and adoption due to its user-friendly interface, extensive multilingual capabilities, and demonstrated robustness across a range of image conditions [9]. Its support for a substantial number of languages, including complex scripts like Thai, makes it especially relevant to our research, which focuses on PDPA compliance within the Thai context.  
The application of OCR technology has been extensively explored across numerous domains, each presenting unique challenges and requirements. Document analysis [12], for instance, has long been a driving force in OCR research, with applications in automated document processing, archival systems, and digital libraries. License plate recognition [13] represents another prominent application area, demanding high accuracy and speed under varying environmental conditions for traffic management, security, and law enforcement purposes. More recently, the application of OCR to product label reading [14] has gained increasing attention, driven by the growth of e-commerce, automated retail, and supply chain optimization.  
Extracting information from product labels, however, presents a distinct set of challenges. Labels often exhibit significant variations in font styles, sizes, and layouts, reflecting diverse branding strategies and regulatory requirements. Furthermore, label images can be subject to noise, distortion, and low resolution, particularly when captured in real-world scenarios using mobile devices or automated scanning systems. For languages with complex scripts, such as Thai [15], the challenges are further amplified due to the intricate nature of the script, diacritical marks, and potential ambiguities in character segmentation and recognition. Our research directly addresses these challenges by leveraging EasyOCR, known for its Thai languagesupport, and by focusing on the specific context of label text recognition for PDPA compliance, where accuracy and reliability are of paramount importance.
## 4 EE Improvement Issues for Each Component
### Channel Estimation Error  
As we witnessed in the previous subsection, the reduction of RS is inevitable in the realization of LSAS. As a result, due to insufficient RS, channel estimation errors are also inevitable. In this regard, we show TP variations with channel estimation errors in Figures 10 and 11.  
Figure 9: RS overhead (%) versus the number of TX antennas, \(N_{t}\) for 1 ms coherence time.  
We modeled the estimated channel as follows,  
\[\mathbf{\hat{H}}=\xi\mathbf{H}+\sqrt{1-\xi^{2}}\mathbf{E} \tag{24}\]  
where \(\mathbf{\hat{H}}\) is the estimated channel matrix, \(\xi\in[0\,,\,1]\) is the error factor which reflects the degree of channel estimation error, and \(\mathbf{E}\in\mathbb{C}^{K\times N_{t}}\) is the error matrix with the same statistical characteristic but independent of the channel. Using (24), the effective SINR of MF, \(\hat{\gamma}_{k,mf}\) and ZF precoding, \(\hat{\gamma}_{k,zf}\) can be represented as follows:  
\[\hat{\gamma}_{k,mf} = \frac{\sum\limits_{n_{t}^{\rho}}^{\rho}\left|\xi\mathbf{h}_{k} \mathbf{h}_{k}^{H}+\sqrt{1-\xi^{2}}\mathbf{h}_{k}\mathbf{e}_{k}^{H}\right|^{2} }{\frac{\rho}{N_{t}^{\rho}K}\sum\limits_{l\
eq k}\left|\xi\mathbf{h}_{k} \mathbf{h}_{l}^{H}+\sqrt{1-\xi^{2}}\mathbf{h}_{k}\mathbf{e}_{l}^{H}\right|^{2} +N_{0}B}, \tag{25}\]  
where \(\mathbf{e}_{l}\) is the \(1\times N_{t}\) channel estimation error vector.  
\[\hat{\gamma}_{k,zf} = \frac{\frac{e_{N_{t}}^{N_{t}}}{K}\left|\mathbf{h}_{k}^{H}(\mathbf {\hat{H}}^{H}\mathbf{\hat{H}})^{-1}\mathbf{\hat{h}}_{k}\right|^{2}}{\frac{e_ {N_{t}}^{Ni}}{K}\mathbf{h}_{k}^{H}(\mathbf{\hat{H}}^{H}\mathbf{\hat{H}})^{-1} \mathbf{\hat{H}}_{[k]}^{H}\mathbf{\hat{H}}_{[k]}(\mathbf{\hat{H}}^{H}\mathbf{ \hat{H}})^{-1}\mathbf{\hat{h}}_{k}+N_{0}B}, \tag{26}\]  
Figure 11: EE variations with channel estimation errors, (**a**) ZF precoding, (**b**) MF precoding.  
Figure 10: TP variations with channel estimation errors, (**a**) ZF precoding; (**b**) MF precoding.  
where \(\mathbf{\hat{H}}_{[k]}^{H}=[\mathbf{\hat{h}}_{1}^{H},\mathbf{\hat{h}}_{2}^{H},\cdots,\mathbf{\hat{h}}_{k-1}^{H},\mathbf{\hat{h}}_{k+1}^{H},\cdots,\mathbf{\hat{h}}_ {K}^{H}]\in\mathbb{C}^{N_{t}\times(K-1)}\).  
Then, TP and EE with channel estimation errors can be represented as:  
\[TP\approx\alpha\cdot K\cdot B\cdot\log_{2}(1+\gamma). \tag{27}\]  
\[EE\approx\frac{\alpha\cdot K\cdot B\cdot\log_{2}\left(1+\gamma\right)}{\frac{1 }{\gamma}P_{tx}+P_{RF}+P_{bb}}. \tag{28}\]  
where \(\gamma\) can be \(\hat{\gamma}_{k,mf}\) or \(\hat{\gamma}_{k,zf}\) depending on the situation. As we can see, the variation of TP and EE due to channel estimation errors is significant. For example, even when there is relatively high accuracy of the channel information, \(\xi=0.8\), in the case of ZF, the TP degradation is 43.18% (17.7 (bps/bps) \(\rightarrow\) 10.0 (bps/bps)), and EE degradation is 43.36% (99.4 ((bps/W)/(bps/W)) \(\rightarrow\) 56.3 ((bps/W)/(bps/W))). Table 3 shows the TP and EE when \(\xi=1\) and 0.8.  
Thus, preventing channel estimation errors is a key factor to increase EE. This will be discussed more in the next section.