esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
A total of 23/26 (88.5%) patients were positive for one or more viruses. In total, we detected as positive samples: 16 (61.63%) EBV1, 12 (46.15%) HHV7, 8 (30.76%) MCV and HHV6, 6 (23.07%) CMV, 2 (7.69%) HHV8, 1 (3.8%) HPVV6 and EBV2 (Figure 1). We detected tumors with unique viral infections (six), with double infections (four), with triple viral infections (eight) and four tumors were positive for DNA of four of these viruses.  
The tumor samples which were detected positive for four viruses were from hard palate (EBV1, HHV6, HHV7, HHV8), inferior lip (MCV, EBV1, CMV, HHV7), palatine tonsil (MCV, EBV1, HHV6, HHV8), and right buccal mucosa (MCV, EBV1, HHV6, HHV7). The triple virus presence was detected in the next cancers: inferior lip (MCV, EBV1, CMV), right half tongue (EBV1, CMV, HHV7), anterior third hard palate (MCV, EBV1, HHV6), right temporal (EBV1, CMV, HHV7), alveolar ridge (HPV6, EBV1, HHV7), base of tongue (MCV, EBV2, HHV7), and hard palate anterior part (MCV, EBV1, HHV6). The double virus presence was detected in the next tumors: larynx (EBV1, HHV7), retromolar trigone (EBV1, HHV7), left retromolar trigone (EBV1, HHV7), right retromolar trigone (HHV6, HHV7), and base of tongue (HHV6, HHV7). The single virus presence was seen in the following cancers: rhinopharyngeal (EBV1), pharyngeal (EBV1), buccal mucosa with skin extension (CMV), genian region (HHV6), uvula (EBV1), ulcerated superior lip (MCV). The highest detected viral load was detected for HHV8, in both cases (hard palate and glottis cancers). We detected significant association between the tested tumors and EBV1, HHV7, MCV, HHV6, CMV for multiple viral detection in the tissue samples (\(p<0.05\)) (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Tumor Samples**} & \multicolumn{8}{c}{**Tested Viruses**} \\ \hline  & EBV1 & HHV7 & MCV & HHV6 & CMV & HHV8 & HPVV6 & EBV1 & EBV1 \\ \hline positive samples & 16 & 12 & 8 & 8 & 6 & 2 & 1 & 1 & 1 \\ \hline \multicolumn{8}{c}{**Positive for 4 Viruses**} \\ \hline hard palate & \(p=0.001\) & \(p=0.001\) & & \(p=0.020\) & & \(p=0.617\) & & \\ \hline inferior lip & \(p=0.001\) & & \(p=0.020\) & & \(p=0.192\) & & \(p=0.724\) \\ \hline palatine tonsil & \(p=0.001\) & & \(p=0.020\) & \(p=0.020\) & & \(p=0.617\) & \\ \hline buccal mucosa & \(p=0.001\) & \(p=0.001\) & \(p=0.020\) & \(p=0.020\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Correlations between tumor samples and the number of positive viruses.  
Figure 1: The prevalence of tested viruses in tumor samples.
# Adaptive Multi-Grained Buffer Management for Database Systems
## 3 Adaptive Multi-Grained Buffer Management
### Operations of AMG-Buffer
#### 3.2.4 Write-Tuple Operation  
Algorithm 2 shows the write-tuple operation. This operation is similar to the read-tuple operation. First, we find the page descriptor via the mapping table. If the descriptor is in the F-buffer, we update the page containing the tuple in the F-Buffer directly. We also update both _VisitBitmap_ and _DirtyBitmap_. On the other hand, if the descriptor belongs to the S-Buffer but the tuple is not in the S-Buffer (the bit of the tuple in the _VisitBitmap_ is 0), we allocate space and write the tuple in the S-Buffer and update the page descriptor, including _VisitBitmap_ and _DirtyBitmap_. Note that when the S-Buffer is full, we will evict a sparse page from the S-Buffer via the LRU algorithm. Because a sparse page in the S-Buffer only contains some tuples of a normal page, we need to reload the original page and synchronous the dirty tuples to keep consistency. After that, we write the modified page into SSDs.
# Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs
## 4 Results and Discussion  
### Performance Metric  
In this study, we focused on evaluating the reliability and performance of WSNs based on two key metrics: maximum connected subgraph and average path length.  
The maximum connected subgraph (MCS) refers to the graph consisting of all sensor nodes that can connect to the base station and the edges connecting them. The size of the maximum connectivity graph reflects the robustness of the WSN when it suffers from  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Simulation 1** & **Simulation 2** & **Simulation 3** & **Simulation 4** \\ \hline Number of sensors & 400 & 400 & 400 & 400 \\ \hline Number of base stations & 1 & 1 & 1 & 1 \\ \hline Number of jammers & 1 & 1 & {1,2,3,4} & 1 \\ \hline Sensor communication distance & 8 & 8 & 8 & 8 \\ \hline Base station communication distance & 15 & 15 & 15 & 15 \\ \hline Jammer movable & False/True & True & True & True \\ \hline Jammer Interference Distance—High & 16 & 16 & 16 & 24 \\ \hline Jammer Interference Distance—Low & 11 & 11 & 11 & 18 \\ \hline Local Maintain & False/True & True & True & True \\ \hline Global Maintain & False & False/True & True & True \\ \hline Global Maintenance Frequency & & 10 & 10 & 10 \\ \hline Simulation Duration & 1/5 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings.  
Figure 5: Network instance.  
jamming. Specifically, it represents the number of sensor nodes that can upload sensing information to the base station, despite the presence of jammers.  
On the other hand, the average path length (APL) is calculated as the average number of hops required for all sensor nodes that can communicate with the base station to reach it. The higher the number of hops required, the greater the delay in information transmission and the lower the performance of the network. Thus, the average path length metric is a crucial factor in assessing the efficiency and effectiveness of WSNs.  
It is important to note a special case when measuring APL where not all sensor nodes are connected to the base station. In such cases, the number of sensor nodes connected to the base station is 0, which would lead to a division by zero problems. To address this issue, we set the APL value to 6 for this special case. This decision was made based on the observation that the jammer moves to a new position every tick, which triggers local maintenance to be performed spontaneously. As a result, the scenario where all nodes are disconnected from the base station typically lasts for only one tick and is significantly improved in the next tick. Moreover, we have noticed in our simulation experiments that the APL value in such cases usually ranges between 2 and 5. Setting the APL value too high in this scenario could mask the actual network performance observed in the simulation.
## Materials and Methods
### RSSI System Measurement  
The objective of this experiment was to gather the RSSI data in a short grass field to study how radio signal behaves in near ground conditions by using commercial WSN nodes model XBee-ZB-PRO operating at 2.4 GHz with ZigBee protocol. The elevation of the nodes was varied to observe the impact of the ground on the signal quality.  
A radio link was established between two nodes: one was intended for transmitting data frames and the other one was for receiving and measuring the RSSI values. Both nodes are provided a standard stub antenna with omni-directional radiation pattern in the E-plane. The transmitted power was \(P_{t}\) = 17 dBm, and the receiver sensitivity is \(-\)102 dBm.  
Figure 2: Detail of measurement campaigns: (**a**) soil field with \(H_{1}\); (**b**) short grass field with \(H_{1}\); (**c**) tall grass field with \(H_{1}\); and (**d**) Radio Signal Strength Indicator (RSSI) with waspnote nodes in tall grass field with \(H_{2}\).  
The firmware of the nodes was programmed to ensure a CW transmission. RSSI was calculated at the radio chip on the base station in real-time and provides useful implication of wireless link quality [9]. Later, following the indications of the manufacturer, the link quality between nodes is estimated in percentage from the RSSI values, according to the following flowchart (1)-(3):  
\[\text{if RSSI}\geq-50\;\text{dB then Quality}\;=\;100\% \tag{1}\] \[\text{else if RSSI}\leq-100\;\text{dB then Quality}\;=\;0\%\] (2) \[\text{else Quality}(\%)\;\approx\;2\times(\text{RSSI}(\text{dB })\;+\;100). \tag{3}\]  
Initially, both nodes were fixed at a height where the elevation of the antenna was \(H_{2}\) = 40 cm from the ground with \(d_{1}\) = 1 m as separation distance between them. The transmitter node was kept stationary and the receiver node was moved each time a step of 2 m, until completing a final separation distance of 50 m. RSSI was measured at each position of the receiver. After that, the nodes were placed on the ground so the elevation of the antennas from the ground was \(H_{1}\) = 20 cm, and the same procedure as the first one was repeated.  
In Figure 2(a), the sensor node XBee-ZB-PRO is shown. Figure 2(b) shows the programing interface used to modify the original firmware.
## 5 Experiments
### Impact of Simulation Environment Scale Setting  
Next, we proceed to verify the performance of the multi-scale convolutional encoder. In actual MCS tasks, UAVs could encounter various densities of overground PoIs. For regions with dense PoI distributions such as modern cities, we hope to perform finer-grained observations for higher feature resolution. While we can perform coarse-grained or lightsized observations for areas with sparse PoIs. In order to handle tasks of different observation scales and enhance robustness, we implement CNNs as the multi-scale encoder, which technically is more applicable than linear encoders for large-scale observations. Therefore, we expect to compare the front-end multi-scale convolutional encoder with original linear encoder using different local observation scales.  
In this experiment, we simulate different sizes of observation inputs by proportionally scaling the whole map, which could maintain the distribution of all elements and ensure comparison fairness. Specifically, we set the original environment setting introduced in Section 5.1 as scale 1.0 unit, and adjust the scale factor from 0.5 to 2.0 with the interval of 0.5 unit. The major settings of different scale factors are listed in Table 2.  
The evaluation results of four environment scales are presented in Figure 7. As size of local observation space varying with observation range \(R_{\text{obs}}\), we can observe that CNN encoder outperforms linear encoder consistently on episodic reward. As for CFE score, ms-SDRGN with local CNN encoder achieves better CFE score than linear encoder when the scale factor is greater than or equal to 1.0, while linear encoder exceeds CNN encoder by 0.04 points at scale 0.5. The above result demonstrates that linear encoder can efficiently extract features from small-size input. In addition, the local CNN used in our multi-scale convolutional encoder has better representational capacity for large observation space. This finding demonstrates the expansibility of ms-SDRGN towards various scales of raw observations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Environment Scale Factor** & **0.5** & **1.0** & **1.5** & **2.0** \\ \hline Environment Size in Pixels & \(100\times 100\) & \(200\times 200\) & \(300\times 300\) & \(400\times 400\) \\ Coverage Range \(R_{cov}\) & 5 & 10 & 15 & 20 \\ Observation Range \(R_{\text{obs}}\) & 7 & 13 & 20 & 26 \\ Communication Range \(R_{comm}\) & 9 & 18 & 27 & 36 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Environment Scale Experiment Settings.  
Figure 6: The evaluation results in environments with different communication dropout rate: (**a**) mean episodic reward and (**b**) CFE score.
## 3 The Proposed Method
### PolSAR Data Pre-Processing
#### 3.1.2 Generating Pauli RGB Image to Obtain the Spatial Feature  
The Pauli decomposition of the scattering matrix \(S\) is often employed to represent all the polarimetric information in a single PolSAR image, and its form is:  
\[S=\begin{bmatrix}S_{HH}&S_{HV}\\ S_{VH}&S_{VV}\end{bmatrix}=aS_{a}+bS_{b}+cS_{c}+dS_{d} \tag{8}\]  
where \(S_{a}\), \(S_{b}\), \(S_{c}\) and \(S_{d}\) constitute a set of orthogonal Pauli bases, and \(a\), \(b\), \(c\) and \(d\) are coefficients. \(S_{a}\) is the odd scattering mechanism, representing the terrain scattering body; \(S_{b}\) is the dihedral scattering mechanism rotating \(0^{\circ}\) around the axis, and its echo polarization and incident polarization are on the mirror symmetry; \(S_{c}\) is the dihedral scattering mechanism rotating \(45^{\circ}\) around the axis, and its echo polarization and incident polarization are orthogonal; \(S_{d}\) is the antisymmetric component. Since the corresponding scattering mechanism does not exist in the nature, the weighted coefficient \(d\) is 0 generally. After the Pauli decomposition, the Pauli RGB image is synthetized by a pseudo-color process using the energy corresponding to \(a\), \(b\) and \(c\), as is described in Equation (9):  
\[\left|a\right|^{2}\to Blue,\left|b\right|^{2}\to Red,\left|c\right|^{2}\to Green \tag{9}\]  
The synthesised Pauli RGB image contains rich contour, texture, and color features, which is in greet agreement with those of real ground scenes. This enables recognition by the naked eye. In addition, the Pauli RGB image can reduce the interference of other data on the feature extraction, improving algorithm robustness. Furthermore, the CNN is good at dealing with color images, so the Pauli RGB image is suitable. For these reasons, many classification algorithms use the Pauli RGB image as their input [16; 17], and so does our method.
## 2 Background
### Protection Levels and Permission Flags
#### 2.2.1 Normal Protection Level  
Systems usually grant access to normal protection levels without needing compliance from the user. Normal permissions are sometimes misused by malware to bypass mobile device security to access device resources and other private data. Malware authors may define some functionalities as access control mechanisms to communicate with pre-defined applications to access the resources. For instance, when these permissions are excessively requested by the over-privileged applications, this poses security issues by increasing the threat level of a device.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Code Name** & **Platform Version** & **API Level** & **Number of Permissions** \\ \hline Android 11 beta & 11 & 30 & 167 \\ Q & 10 & 29 & 158 \\ PI & 9 & 28 & 148 \\ Oreo & 8.0\(-\)8.1 & 26\(-\)27 & 144 \\ Nougat & 7.0\(-\)7.1 & 24\(-\)25 & 135 \\ Marshmallow & 6 & 23 & 131 \\ Lollipop & 5.0\(-\)5.1 & 21\(-\)22 & 120 \\ KitKat Watch & 4.4 W & 20 & 113 \\ KitKat & 4.4 & 19 & 112 \\ Jelly Bean & 4.1\(-\)4.3.1 & 16\(-\)18 & 104 \\ Ice Cream Sandwich & 4.0.1\(-\)4.0.4 & 14\(-\)15 & 98 \\ Honeycomb & 3.0\(-\)3.2 & 11\(-\)13 & 95 \\ Gingerbread & 2.0\(-\)2.3.5 & 9\(-\)10 & 94 \\ Froyo & 2.2. & 8 & 87 \\ Éclair & 2.0\(-\)2.1 & 5\(-\)7 & 86 \\ Donut & 1.6 & 4 & 85 \\ Cupcake & 1.5 & 3 & 81 \\ Base & 1\(-\)1.1 & 1\(-\)2 & 73 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Android code name release versions showing the increase in the API level and number of permissions.
## 1 Introduction  
Melanoma of the uveal tract is an uncommon malignancy, mainly affecting middle-aged adult patients, primarily arising from the choroid and less frequently from the ciliary bodies and iris [1, 2, 3, 4]. Uveal melanoma (UM), despite representing the most common primary intraocular malignant tumor of the adult age, is exceptionally unusual in the pediatric population in which it usually has congenital origin with poorer outcome and more frequent extraocular extension at the diagnosis [5]. Risk factors that have historically been reported to be involved in the development of this neoplasm include: Fitzpatrick phototypes I-II, type 1 neurofibromatosisis (NF-1), oculo-dermal melanocytosis (Ota nevus), dysplastic nevus syndrome, and choroidal nevi [5; 6; 7]. Although UM typically tends to remain asymptomatic for years before the diagnosis, a sudden retinal detachment often represents the presenting symptom of the tumor [1; 2; 3; 4; 5]. UM is a slow-growing tumor with indolent biological behavior but consistently poor prognosis, characterized by the development of liver metastases within 10-15 years after the diagnosis in about 50% of cases [6; 7; 8; 9]. In recent years, the search for genetic and epigenetic prognostic and/or predictive factors of therapeutic response has been one of the most studied topics in cancer research [10; 11]; in this regard, loss-of-function mutations of BRCA1 associated protein-1 (BAP1), that have been observed in most metastasizing UM cases and correlated with the immunohistochemical loss of BAP-1 protein, represent one of the most reliable prognostic factors of this tumor [12; 13; 14]. Serine and arginine-rich splicing factor 1 (SRSF1) is part of the SR protein family, being involved in canonic and alternative pre-mRNA splicing and in the regulation of mRNA transcription [15; 16]. Moreover, SRSF1 has also been reported as a proto-oncogene positively involved in tumor angiogenesis by regulating the alternative splicing of vascular endothelial growth factor-\(\alpha\) (VEGFA) [17; 18; 19; 20; 21]. In this regard, it has been suggested that SRSF1 tends to promote the formation of the pro-angiogenic isoform of VEGFA, instead of the anti-angiogenic one [17; 18; 19; 20; 21]. Consistent with its stimulatory role on tumor angiogenesis, SRSF1 has been found overexpressed in several human neoplasms, including prostate, brain, breast, colorectal, liver, pleural and lung tumors [22; 23; 24; 25; 26; 27]; however, to the best of our knowledge, no data on the role of this protein in UM are present in literature to date.  
We herein investigated the immunohistochemical expression of SRSF1 on a series of 85 cases of primary UMs and correlated it with clinico-pathological features and prognostic data of the cases from our cohort; moreover, in order to further confirm the pro-angiogenic role of SRSF1, we performed a correlation between its immunohistochemical expression and blood vascular microvessel density (MVD) in tumor tissue.
## 6 Conclusions  
From the results presented in this paper we can conclude that typical signal-processing tasks performed at each cognitive radio before the collected samples are sent to the fusion center must be taken into account when investigating soft-values fusion algorithms, as the performance results may vary significantly between an idealized and a realistic model. Furthermore, the realistic model shows that the impact of impulsive noise is not as negative in real life as it could be implied from an idealized model.  
We also conclude that GLRT performs better under IN circumstances, closely followed by MMED. The performance of MED and ED is drastically degraded by the effect of IN, with a clear advantage of MED over ED, since the latter did not work at all in any of the simulated scenarios. The superior performance of GLRT and MMED is attributed to the inherent ability of the eigenvalues of the covariance matrix \(\mathbf{Y}\) in reflecting the presence of IN. MED and ED, on the contrary, are very sensitive to IN and suffer from the need to use the thermal noise variance that, in practice, is very difficult to estimate if IN is present. Noise variance uncertainty can itself bring forth severe performance degradation in MED and ED [6, 26]. Additionally, the decision thresholds for all detection techniques investigated under the conventional model had to be drastically modified from the situation of absence to the presence of IN for the techniques to work. This would be a strong limitation in practice, since it would demand detecting the presence of IN for posterior adaptation of the threshold.  
Last but not least, we conjecture that the performance of MED and ED under the implementation-oriented model can be improved if the known normalization gains before the ADC are taken into account for the design of new test statistics and for producing the estimate of the thermal noise power in each cognitive radio. ED has a stronger demand for such improvement, since it simply does not work based on the test statistic (6). As already stated, this remains an open problem for future investigation.
## 3 Results
### Validation Using the Ground Measured Reflectance  
Based on the above mentioned experimental results, using the 2021 comprehensive gains as the calibration coefficient of the satellite sensor, the satellite data were processed with absolute radiometric correction and atmospheric correction. Then, the satellite reflectance data corresponding to the ground sample points (GSPs) were obtained and compared with the measured surface reflectance of the GSPs. The measured reflectance and retrieved reflectance for each GSP are shown in Figure 16. The solid line represents the measured surface reflectance, and the dotted line indicates the surface reflectance retrieved by satellite data.  
It is noted that the surface reflectance retrieved from satellite data was visually consistent with the measured ground reflectance of cotton and was basically consistent with the measured reflectance curve of cotton with the wavelength. However, the reflectance of the first 16 bands was slightly higher than that measured. In contrast, the latter 16 bands were the opposite. The main reason for this may be an error in the input atmospheric parameters used in the 65 atmospheric correction or a spatial matching error in measurement samples, etc.
## 2 Information Reconciliation in QKD Post-Processing
### Rate-Adaptive LDPC Codes and Efficiency Metric of Information Reconciliation
**Generation of polynomial hash function and hash values calculation**: To generate the polynomial hash function, _[PERSON]_ first defines the hash value length (\(l_{\text{hash}}\)) and then randomly selects the parameters of the polynomial base \(\alpha\) and the prime modulus \(\rho\). Specifically, \(\alpha\) is chosen from the set \(\alpha\in[2,3,\,\ldots,\,\rho-2]\), and \(\rho\) is a prime number constrained by \(\rho\)\(\sim\)\(2^{l_{\text{hash}}}\). Subsequently, the polynomial hash function is applied to calculate the hash values of the reconciled keys in each subblock \(SB_{i}^{\ A}\). This can be mathematically represented as \[h_{\text{PolyR}}(SB_{i}^{A})=\Big{(}k_{0}^{A}+\ k_{1}^{A}\cdot\alpha\ +\ k_{2}^{A}\cdot\alpha^{2}+\ \cdots\ +\ k_{N_{\alpha}-1}^{A}\cdot\alpha^{N_{\alpha}-1} \Big{)}\ \ \text{mod}\,\rho,\] (19) Following this calculation, _[PERSON]_ transmits the resulting hash values for each subblock \(h_{\text{PolyR}}\big{(}SB_{i}^{\ A}\big{)}\) to _[PERSON]_. This transmission corresponds to the parameters of the polynomial hash function \(\alpha\) and \(\rho\), which were previously chosen by _[PERSON]_. 6. **Hash verification and result confirmation**: On _[PERSON]_'s side, he uses the received parameters \(\alpha\) and \(\rho\) to generate the polynomial hash function and computes the corresponding hash values for his reconciled keys in each subblock \(h_{\text{PolyR}}\left(SB_{i}^{\ B}\right)\), as defined in Equation (19). To verify the identical keys, _[PERSON]_ compares \(h_{\text{PolyR}}\left(SB_{i}^{\ B}\right)\) with the received \(h_{\text{PolyR}}\left(SB_{i}^{\ A}\right)\) for each subblock. This step is considered successfully completed if the hash values match for all subblocks from \(1\) to \(m\), which is expressed as: \[\forall i\in\{1\,,\ 2\,,\ \ldots\,\ m\}:h_{\text{PolyR}}(SB_{i}^{A})=h_{\text{PolyR}}(SB_{i}^{B}).\] (20) Otherwise, _[PERSON]_ sends feedback to _[PERSON]_ indicating the confirmation failure and identifying the mismatched \(i^{th}\) subblock. Then, only the reconciled keys from the mismatched subblocks are reprocessed, and the procedure returns to the steps of syndrome-based error estimation and rate-adaptive reconciliation in the additional round. During this round, specific irregular LDPC codes with block lengths \(N_{sb}\) are employed to estimate QBER, optimize the code rate, and correct errors using the same procedure. The process continues until the subblocks are successfully verified using polynomial-based hashing. Ultimately, _[PERSON]_ and _[PERSON]_ obtain the identical keys, denoted as \(K_{ilen}^{A}\) and \(K_{liden}^{B}\), respectively.