esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Kalman Filtered Forecasts of Solar Radiation
#### 4.3.1 KF_TS Forecasts  
The statistics of MBE, MAE, RMSE, and nRMSE are listed in Table 3 to quantitatively assess the effects of applying the Kalman filter on the time-sequence of forecasts, in comparison with the WRF raw forecasts. It is worthwhile to point out that the values in the second and fourth rows of Table 3 are slightly different with the values in the last column of Table 2. The reason is that we excluded two-week period data from the original data series for training the Kalman filter. Examining the third row of Table 3, the positive value of MBE is observed. It indicates that the KF_TS forecasts of solar radiation are still overestimated for the Maizuru site; however, compared to the raw predictions, the MBE of KF_TS is reduced by 80.2%. The value of RMSE of KF_TS forecasts is much smaller compared to raw estimates, with a relative error reduction of 22.9%. Similarly, for the Qinghai site, smaller MBE value of KF_TS indicates that conducting the Kalman filter on a time-sequence has reduced part of systematic  
Figure 6: The solar radiation (W/m\({}^{2}\)) series over five days of clear (**a**), cloudy (**b**) and overcast (**c**) sky conditions for the Maizuru site. The solid black and dotted red lines stand for forecasts and the corresponding observations, respectively.  
errors in the raw forecasts. Consistent with MBE, MAE, RMSE, and nRMSE of raw forecasts are all improved to some extent.  
Given these results, we can affirm that KF_TS can provide apparent improvement in the situation where the raw forecast presents large errors.
# Enhanced Effective Filtering Approach (eEFA) for Improving HSR Network Performance in Smart Grids
## 4 Traffic Performance Analysis
### Unidirectional Communication
#### 4.1.2 In eEFA  
In eEFA, the first sent frame is not forwarded to nondestination DANH rings. Instead, it is forwarded to the destination DANH ring and all QuadBox rings. The number of network frames generated by the first sent frame, denoted by \(nf^{1}_{eEFA}\), is calculated as:  
\[nf^{1}_{eEFA}=n_{d}+\sum_{i\in QR}n_{i} \tag{6}\]  
where \(QR\) is the set of all QuadBox rings in the network.  
For unidirectional communication, eEFA also uses the Locking message to discover passive QuadBox rings. The number of network frames generated by the Locking message, denoted by \(nf^{lock}_{eEFA}\), is calculated as:  
\[nf^{lock}_{eEFA}=n_{d}+\sum_{i\in AQR}n_{i} \tag{7}\]Like EFA, eEFA does not forward the \(k\)th(\(k\geq 2\)) frame to nondestination DANH ring and passive QuadBox rings. The number of network frames generated by the \(k\)th frame (\(k\geq 2\)), denoted by \(nf_{eEFA}^{k}\), is calculated as:  
\[nf_{eEFA}^{k}=n_{s}+n_{d}+\sum_{i\in AQR}n_{i} \tag{8}\]  
Generally, the number of network frames generated in the network when the source sends \(N\) unicast frames to the destination in unidirectional communication in eEFA, denoted by \(nf_{eEFA}^{uni}\), is calculated as:  
\[nf_{eEFA}^{uni}=nf_{eEFA}^{1}+nf_{eEFA}^{lock}+(N-1)nf_{eEFA}^{k} \tag{9}\]  
By substituting \(nf_{eEFA}^{1}\), \(nf_{eEFA}^{lock}\), and \(nf_{eEFA}^{k}\), \(nf_{eEFA}^{uni}\) can be calculated as:  
\[nf_{eEFA}^{uni}=\sum_{i\in QR}n_{i}+2n_{d}+(N-1)(n_{s}+n_{d})+N\sum_{i\in AQR}n_ {i} \tag{10}\]
## 5 Results
### Comparison with Related Work  
In our study, we compared our VGG16 model to other related methods, and we mainly focused on types of cancers, datasets, and results as shown in Table 3. This comparative analysis was essential to evaluate the efficiency and contribution of our approach in terms of existing methodologies in skin cancer diagnosis. Our VGG16 model showed promising performance in the results compared to previous work.  
While this study presents promising results in utilizing the VGG-16 model for skin cancer diagnosis, several limitations must be acknowledged. One significant concern is the potential bias in dataset selection, as the images were sourced primarily from Kaggle [47], which may not fully represent the diverse spectrum of skin types and lesions encountered in clinical practice. This limitation may affect the generalizability of the model, particularly when applied to less common skin lesions or those from diverse demographic groups. Additionally, variations in imaging techniques, lighting conditions, and image quality may introduce inconsistencies that impact the model's performance. Future research should aim to include more varied datasets and address these biases to enhance the robustness and applicability of the model across a broader range of skin conditions.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Ref** & **DL Model** & **Type of Cancer** & **Dataset** & **Accuracy** \\ \hline [PERSON] et al. [44] & VGG16 & Melanoma & Kaggle & 0.84 \\ \hline \multirow{4}{*}{[PERSON] et al. [45]} & DRANet, & \multirow{4}{*}{11 types (BCC, EC, S, EP, D, GA, NGG16, VGG19} & \multirow{4}{*}{1167 images} & DRANet (86.8\%), ResNet50 (85.5\%), \\  & ResNet50, & & EC, S, EP, D, GA, NGG16, VGG19 & \multirow{4}{*}{1167 images} & \multirow{4}{*}{1167 images} \\  & InceptionV3 & & N, LP, LMDF, & (86.3\%), VGG16 (82.1\%), VGG19 (83.8\%) \\ \hline \multirow{4}{*}{[PERSON] et al. [11]} & GoogleNet, & \multirow{4}{*}{Melaoma} & \multirow{4}{*}{7146 images} & \multirow{4}{*}{76.08\%, 73.96\%, 73.74\%, 74.68\%, 73.42\%} \\  & DenseNet50V2, & & & \\ \cline{1-1}  & VGG16, VGG19 & & & \\ \hline [PERSON] et al. & \multirow{4}{*}{VGG16} & 2 classes (benign/malignant) & \multirow{4}{*}{3297 images} & \multirow{4}{*}{83\%} \\  & & & & \\ \cline{1-1}  & VGG16 & 3 types (MEL, NV, BCC) & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative analysis of different methods used for skin cancer diagnosis.  
Figure 9: Results of Model 2’s diagnosis process for some images.
## 5 Data Transmission Tests
### End Node Installation  
The end node was set up bearing in mind that in future stages of the project it is expected to be mounted on the cages anchoring buoys (i.e., seamark buoys). A buoy like these is constructed as a beam, that has a truncated cone situated at one fourth from its lower extremity that allows for the buoyancy property, whose upper end is at 3.5 m above sea level. Therefore, the transmitting antenna will be placed at such height by directly installing it on the beam. However, for these measurements campaign it will be temporarily mounted on a pole feigning the aforesaid altitude above sea level. Actually, as it will be seen later on, two different heights for the transmitting antenna were tested in order to check the feasibility of the link simulating a worst case scenario. Hence, the transmitter was firstly at an altitude \(h_{TX_{1}}=3.5\) m and then at an altitude \(h_{TX_{2}}=2.1\) m above sea level. Moreover, the end node with the pole supporting the antenna was set up on board of a boat. Figure 5 displays the antenna installed on the pole lying on the floor of the boat and the view reaching the measurements spot.  
So as to enhance the strength of the wireless link, a directional Yagi-Uda antenna having a gain \(G_{TX}=9\) dBi was selected. Another interesting characteristic is that it is pretty lightweight (i.e., 400 g)  
Figure 5: Offshore setup for the measurements campaign: (a) offshore end node antenna and its pole; (b) view approaching the tests spot.  
thus well suiting to its future employment on top of a seamark buoy. Indeed, its limited weight could prevent the buoy capsizing. The antenna was connected to a B-L072Z-LRWAN1 discovery kit board produced by STMicroelectronics via a 4 m long coaxial cable whose overall loss, \(L_{TX}=2.10\) dB, was measured by means of a vector network analyzer.  
Eight different channels belonging to the 868 MHz ISM band were exploited to conduct the transmission tests so as to establish a frequency diversity scheme. Therefore, the B-L072Z-LRWAN1 discovery kit board performs a frequency hopping in a pseudo-random fashion for each transmission by switching amid the channels having the following carrier frequencies: 867.1 MHz, 867.3 MHz, 867.5 MHz, 867.7 MHz, 867.9 MHz, 868.1 MHz, 868.3 MHz and 868.5 MHz.  
Finally, so as to speed up the tests, 6 discovery kit boards were sorted out. They transmitted the string that was introduced in Section 5.1 respectively employing a different SF. All those boards were supplied by the boat cigarette lighter.
## 3 Results
### Comprehensive Performance Evaluation  
#### 3.2.1 Overall Performance Analysis  
Our comprehensive evaluation across twelve cross-lingual summarization tasks reveals significant performance improvements with the MuRXLS framework, as shown in Table 3. The results demonstrate consistent benefits of retrieval-based example selection across different models, language pairs, and shot configurations.  
Several key findings emerge from the performance analysis. MuRXLS consistently outperforms random sampling across most experimental configurations, with improvements ranging from modest gains to substantial enhancements depending on the translation direction and language pair. The framework shows particular strength in scenarios involving linguistically distant language pairs, where semantic similarity becomes more crucial for effective cross-lingual transfer.
## 3 Instrument Description, Calibration, and Relevance to ERB Measurement Challenges
### Uvsq-Sat Map Reconstruction Examples
Tropical regions, despite their warmth, often show suppressed OLR levels because of persistent cloud cover and deep convective activity, especially within the ITCZ. Thick cumulonimbus clouds in these areas reflect incoming solar radiation and trap outgoing infrared radiation, reducing the net energy emitted at TOA. Over the oceans, OLR patterns are governed by SST, atmospheric moisture, and cloudiness. Warm oceanic regions such as the western Pacific Warm Pool, tropical Indian Ocean, and parts of the Atlantic emit relatively high OLRs under clear skies. However, intense convection and persistent cloud cover in these zones, particularly deep convective cloud systems with cold, high-altitude tops, significantly lower the OLR by emitting less infrared radiation than the warm ocean surface below. This results in the characteristic \"cold patch\" signature on OLR maps along the ITCZ. Subtropical ocean regions, typically dominated by descending air in the Hadley circulation, are more cloud-free and thus display higher OLR values due to clearer skies and relatively warm waters. These include areas like the eastern Pacific and Atlantic subtropics. The spatial and temporal variability of OLRs is a critical indicator of surface heating and atmospheric dynamics, including phenomena such as ENSO, monsoonal cycles, and tropical cyclone formation. Continuous satellite monitoring of OLRs provides essential data for understanding Earth's energy balance and improving climate and weather forecasting models.
## 3 Results
### Pitch Parameters  
The overall mean pitch parameter measurements are shown in Table 3. The mean elbow valgus torque was 30.6 \(\pm\) 7.7 N-m. Ball velocity was significantly positively correlated with elbow valgus torque (r = 0.80, \(p\) < 0.001), while no significant correlations were found between elbow valgus torque and the other pitching motion parameters measured by the wearable sensor (Table 2, Figure 6). Subsequently, all participants were divided into two groups based on the mean elbow valgus torque measured in this study and the ultimate valgus torque resistance strength of the UCL in previous reports [3,10]: the \(>\)30 (high torque [HT]; n = 33) and \(<\)30 N-m (low torque [LT]; n = 30) groups. Between-group pitch parameters and physical factors were then analyzed. Elbow valgus torque was 36.4 \(\pm\) 5.1 and 24.3 \(\pm\) 4.4 N-m in the HT and LT groups, respectively, with a significant between-group difference (_p_ < 0.001). Ball velocity was also significantly higher in the HT group (HT, 107.1 \(\pm\) 8.4 km/h; LT, 95.3 \(\pm\) 6.9 km/h; \(p\) < 0.001), whereas no significant between-group differences were found in the other pitch parameters (Table 3).
# Quantum Stream Cipher Based on Holevo-Yuen Theory
###### Abstract  
In this review paper, we first introduce the basic concept of quantum computer-resistant cryptography, which is the cornerstone of security technology for the network of a new era. Then, we will describe the positioning of mathematical cryptography and quantum cryptography, that are currently being researched and developed. Quantum cryptography includes QKD and quantum stream cipher, but we point out that the latter is expected as the core technology of next-generation communication systems. Various ideas have been proposed for QKD quantum cryptography, but most of them use a single-photon or similar signal. Then, although such technologies are applicable to special situations, these methods still have several difficulties to provide functions that surpass conventional technologies for social  
Pad cipher, because its data rate is too slow. Y-00 for data encryption is extremely novel in its ability to prevent eavesdroppers from obtaining the ciphertext of the symmetric key cipher. In addition, it is amazing that the strong quantum-ness is created by modulation scheme with multi-ary coherent state signals without any quantum device\".  
Let us now turn our focus to quantum cryptography. Both of these quantum technologies are based on designing communication systems to make it difficult for eavesdroppers to steal signals on the communication channels. Such a function to protect the signal itself cannot be realized by mathematical cryptography. As mentioned above, there are two possible system operation methods for these quantum cryptography techniques. One is to use BB-84 quantum key distribution for key delivery and conventional mathematical cryptography for authentication and data encryption. The other is to use Y-00 quantum stream cipher for data encryption and conventional public key cryptography (or quantum computer resistant type) for authentication and key delivery. These quantum cryptography technologies are positioned as technologies to ensure the ultimate security of communication between data center stations, that is of special importance in next-generation 5G and 6G systems. In the following, we will explain the technical contents, applicability to the real world, and development trends.
## 2 Related Work
### Hybrid Models Combining CNN and Transformer  
By combining the merits of a CNN and a transformer, the model can capture both global and local features. This improves the overall performance of the task. In visual processing, CNNs have evolved. They have moved from early single deep learning models to multi-scale feature fusion, lightweight design, and integration with a transformer. These changes have significantly improved the accuracy, performance, and adaptability of image processing. While a transformer has strong global modeling capabilities, its computational complexity still needs improvement. Therefore, an important research direction is how to combine a CNN and a transformer. This would maximize their complementary strengths while reducing computational costs.  
For example, [PERSON] et al. [20] proposed the Att-UNet model, which adds an attention gate to the standard U-Net architecture. This allows the model to focus on important feature areas in the image, improving the accuracy of target extraction. [PERSON] et al. [29] designed the ACC-UNet model, which combines the inductive bias of a CNN with transformer design principles. This further enhances the modeling ability for multi-scale features. [PERSON] et al. [30] proposed the BRAU-Net++ architecture, which combines CNN and transformer merits. BRAU-Net++ uses a hierarchical encoder-decoder structure to capture complex features at different scales. Additionally, it incorporates a bi-level routing attention mechanism to balance local feature learning and global semantic information. This significantly reduces the model's computational load. Hybrid models that combine a CNN and a transformer show great potential in image feature recognition tasks. By fully leveraging CNNs' merits in detailed feature extraction and transformers' strengths in global feature modeling, these hybrid models improve recognition accuracy and enhance system robustness and adaptability.  
ResNet50 introduces residual connections, which effectively alleviate the vanishing gradient problem during the training of deep networks, enabling the network to go deeper and extract more complex features. Therefore, the role of ResNet50 as a feature extraction network in DRSTnet is evident. It can extract rich local features from input images, especially excelling at capturing low-level and mid-level features such as edges, textures, and shapes. These features are then sent to the Swin Transformer module for global modeling. By combining them, ResNet50 provides Swin Transformer with rich local feature information, while Swin Transformer is able to model complex semantic relationships in the image at a global level, significantly enhancing the overall performance of the task.
## 1 Introduction
More recently, the relationship between water levels and GNSS displacements measured in the mitigation of the dam was analysed in [44] by processing a remote sensing dataset (including both optical and SAR images). Water levels can be indirectly determined by mapping the reservoir water surface extent using optical images [13; 15; 44]. Over the study area, two other approaches based on similarity indexes have also been proposed to retrieve the water levels using remote sensing datasets with different resolutions and under different conditions [45]. The present work aims to demonstrate the possibility of monitoring both the forcing factors and the resulting displacements of a dam via complementary remote sensing approaches and shows that these estimates are reliable if compared with both forcing factors and displacements measured by deploying onsite sensors. In particular, the trustworthiness of the behaviour of the total displacements has been assessed by comparison with GNSS positioning data acquired on the crest dam during the same hydrological year. Remote sensing displacements were evaluated by employing an interferometric approach based on a quasi-PS-InSAR technique. A full graph Multi Baseline Connection (MBC) method and a nonlinear displacement trend model were selected according to [46]. In Section 2, the study area is introduced along with a summary of the different in situ and remote sensing datasets and the methods deployed for their analysis. In Section 3, the results are shown along with a discussion of their significance, and in Section 4 concluding remarks are made.
## 2 Materials and Methods
### Tocopherol Analysis  
Analysis of tocopherol isoforms was performed on an Agilent 1260 HPLC with a UV-Vis detector (Agilent Technologies, Santa Clara, CA, USA). A Spherisorb Silica-80 column (250 \(\times\) 4.6 mm, 5 \(\upmu\)m particle size) from Waters (Milford, MA, USA) was used, and the mobile phase consisted of \(n\)-hexane:2-propanol (99:1 \(v/v\)) in isocratic mode at a flow rate of 1 mL/min. Samples (sunflower oil and the lipids extracted from fish, avocado, almonds, and spinach) were dissolved in \(n\)-hexane before being injected into the HPLC. The injection volume was 20 \(\upmu\)L, and detection was performed at a wavelength of 292 nm. Tocopherol standards (\(\alpha\), \(\beta\), \(\gamma\), and \(\delta\)) were injected separately to identify their retention time. Quantitative results of tocopherols in food matrices were reported in mg/100 g fresh weight (fw), considering the moisture content of all samples to convert data from dry weight (dw) to fw.
## 3 Results  
This section is divided into five subsections: one per piece of hardware. A detailed description of each component is presented together with the preliminary obtained results.  
Every piece of equipment was first simulated using Mathworks-Simulink(tm) and Pspice(tm) and then prototyped, and some of them were then sent to manufacturing. Since our aim is only to present the system's architecture and preliminary outcomes, some hardware has been completed, other parts have been prototyped and a few have only been simulated. The last subsection presents in depth the global aspect, components and sensors of the prototype, which is already functional when used at full throttle in the same way as NOVA2400(tm) but produces nearly three times its output power.
# Comparison of Enhanced Noise Model Performance Based on Analysis of Civilian GPS Data
## 4 Modelling the Noise  
In this section, we develop some statistical models for the observed noise. A model is a relationship between random variables, \(\{X_{t+k}\}_{k\in\mathbb{K}}\), in the following form:  
\[h(\{X_{t+k}\}_{k\in\mathbb{K}})=g(\{E_{t+\ell}\}_{\ell\in\mathbb{L}})\,, \tag{4}\]  
where \(\mathbb{K}\) and \(\mathbb{L}\) are sets of integers, \(h\) and \(g\) are fixed functions, and \(E_{t}\sim\mathcal{N}(0,\sigma^{2})\). Here, the quartet (\(h\), \(g\), \(\mathbb{K}\), \(\mathbb{L}\)) constitutes the model to be inferred. To simplify the task, we presume that \(h\) and \(g\) are linear in all their variables (so-called linear models). The stochastic model is then  
\[\sum_{k=0}^{\infty}h_{k}X_{t-k}=\sum_{\ell=0}^{\infty}g_{\ell}E_{t-\ell}, \tag{5}\]  
where \(h_{k}\) and \(g_{\ell}\) are all constants with \(h_{0}=g_{0}=1\) (convenience), and causality restricts summations to the current or past values of \(X_{t}\) or \(E_{t}\)[41]. This model requires an infinite number of coefficients, which cannot be estimated using a finite number of observations. Therefore, we focus on finite-parameter linear models and estimate the unknowns via the maximum likelihood method, in which the likelihood function below is constructed for Gaussian processes.  
The likelihood function, \(L\), is the joint probability density of obtaining the actually observed data \(\mathbf{x}=(x_{1}\), \(x_{2}\),..., \(x_{N})\), according to the model. It is a function of the unknown parameters \(\mathbf{\beta}=(h_{0}\), \(h_{1}\),..., \(g_{0}\), \(g_{1}\),...), and \(\sigma^{2}\). Under general assumptions, the log-likelihood function of a finite parameter linear model is:  
\[\log\Bigl{(}L(\mathbf{\beta},\sigma^{2})\Bigr{)}=-\frac{1}{2}\biggl{[}N\log(2\pi )+\log\Bigl{(}\det\Bigl{(}\sigma^{2}\,\mathbf{\Sigma}(\mathbf{\beta})\Bigr{)}\Bigr{)} +\frac{1}{\sigma^{2}}\mathbf{x}^{T}\mathbf{\Sigma}^{-1}(\mathbf{\beta})\,\mathbf{x}\biggr{]}\]  
where the \((m,n)\)-th component of the covariance matrix \(\sigma^{2}\mathbf{\Sigma}(\mathbf{\beta})\) is calculated from  
\[\sigma^{2}\Sigma_{m,n}=\frac{\sigma^{2}}{2\pi}\int_{-\pi}^{\pi}e^{i(m-n)\omega }\,\biggl{|}\frac{g_{0}+g_{1}z+g_{2}z^{2}+\cdots}{h_{0}+h_{1}z+h_{2}z^{2}+ \cdots}\biggr{|}_{z=e^{-i\omega}}^{2}d\omega.\]  
Although the likelihood function, \(L\), is derived under Gaussian processes, if the time series is causal and invertible, then maximising this likelihood leads to asymptotically consistent  
Figure 5: The partial autocorrelation function, \(\widehat{\rho}_{\text{P}}(h)\), for longitude, latitude, and altitude at all geographic locations visited. The lag is measured in seconds.  
estimators [42; 43]. This approximation is convenient, as the asymptotic likelihood function has a unique stationary point, the global maximum [44].  
Below, we introduce and analyse finite-parameter linear noise models and compare their performance, including the Gaussian white noise model for a benchmark.