esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Ship Target Detection based on Morphological Reconstruction and Multi-feature Analysis
### Proposed Bright Ship Target Detection in TIR Images

Table 2: The statistical measurements of three shape descriptors of 700 TIR ship targets selected from VAIS database. ship target map and target position are acquired by excluding the non-ship targets if their computed shape description parameters are not within the ranges of Table 2. The whole of the proposed two-step ship verification strategy for TIR bright ship image based on contour feature representation and shape feature constraint is summarized in Algorithm 2. ```
Input: Pre-processed image \(u\), binary final saliency map for ship target image \(BSM\), and the distinctive threshold \(Tlr^{*}\). Output: Detected TIR ship target map. Step 1: Compute the regularized structure tensor \(ST\) of pre-processed image \(u\) according to (21). Step 2: Calculate the normalized large eigenvalue map \(\lambda_{\text{large}}\) of matrix \(ST\) according to (22). Step 3: Label the connected regions of \(BSM\) with numbers, and obtain the total number \(TN\) of connected regions in \(BSM\). Step 4:for label index \(k=1\):\(TN\)do  Compute the average eigenvalue measure of structure tensor (STAEM) \(\lambda_{aver}\) of \(k\)-th connected region according to (23). Eliminate the \(k\)-th connected region if the \(\lambda_{aver}\) is smaller than distinctive threshold \(Thr^{*}\). endfor Step 5: Obtain the map of candidate ship targets and acquire the total number \(SN\) of candidate ship targets. Step 6:for candidate ship index \(t=1\):\(SN\)do  Compute the shape description parameters of \(t\)-th candidate region according to (24)-(26). Exclude the non-ship targets if their computed shape description parameters are not within the ranges of Table 2. endfor Step 7: Acquire the output detected ship target map and target position
```  
**Algorithm 2** Proposed two-step ship verification strategy for TIR ship image
# Involvement of the Orexinergic System in Cancer: Antitumor Strategies and Future Perspectives
## 3 Orexins and Cancer
### Gastric Cancer  
OXR1, but not OXR2, has been detected in SGC-7901 and BGC-823 gastric cancer cells and it has been reported that orexin A, through the Akt signaling pathway, increased OXR1 expression, decreased caspase 3 apoptotic activity, and augmented both viability and cell proliferation in the latter cell type [82; 101]. Orexin A, through the ERK1/2 signaling pathway, augmented SGC-7901 cell viability/proliferation and exerted an anti-apoptotic effect [102]. The orexinergic system has been studied in patients with gastric cancer and in those with chronic atrophic gastritis (CAG) [103]. Orexin A was upregulated in cancer patients compared with normal and CAG groups, and its expression was higher in CAG patients than in normal individuals [103]. On the contrary, OXR1/OXR2 expressions were downregulated in patients with gastric cancer compared with the normal and CAG groups [103]. The expression of both receptors was higher in CAG individuals than in cancer patients and tumor tissues were depleted of prepro-orexin compared to that found in tissues from normal or CAG individuals [103]. Finally, inflammation was higher in gastric cancer tissues than in those taken from normal and CAG individuals; moreover, the expression of orexin A was not related to differential grades, age, and gender [103]. Thus, it seems that orexin A interacts with OXR1 and OXR2 and activates prepro-orexin, leading to inflammation in gastric tumor tissues.  
OXR1 overexpression, via the activation of MAPK-dependent caspases and src-tyrosine, promoted tumor regression in gastric adenocarcinoma [104]. However, another study has shown that orexin A, through OXR1 and the Akt signaling pathway, blocked apoptosis in BGC-823 gastric cancer cells [101]. The peptide increased the expression of OXR1 protein in these cells, improving their viability and proliferation and protecting them from apoptosis [101]. Moreover, orexin A increased the phosphorylated Akt protein and decreased the pro-apoptotic activity of caspase 3 [101]. These actions, mediated by orexin A, were blocked with PF-04691502 (an Akt antagonist) or SB-334,867 (an OXR1 antagonist). Orexin A, through ERK1/2, upregulates OXR1 expression and increases the proliferation of human SGC-7901 gastric cancer cells [102]. Thus, the peptide increased cell proliferation and viability, protected cells from apoptosis, decreased the pro-apoptotic activity of caspase 9, and increased ERK1/2 phosphorylation [102]. These actions were inhibited, to a certain extent, with the administration of U0126 (an ERK1/2 antagonist) or SB-334,867 (an OXR1 antagonist). Finally, GBC-823 gastric tumor cells expressed OXR1 that, after activation, blocked apoptosis through the Akt signaling cascade [101].
###### Abstract  
Robust prediction of Alzheimer's disease (AD) helps in the early diagnosis of AD and may support the treatment of AD patients. In this study, for early detection of AD and prediction of mild cognitive impairment (MCI) conversion, we develop an automatic computer-aided diagnosis (CAD) framework based on a merit-based feature selection method through a whole-brain voxel-wise analysis using baseline magnetic resonance imaging (MRI) data. We also explore the impact of different MRI spatial resolution on the voxel-wise metric AD classification and MCI conversion prediction. We assessed the proposed CAD framework using the whole-brain voxel-wise MRI features of 507 J-ADNI participants (146 healthy controls [HCs], 102 individuals with stable MCI [sMCI], 112 with progressive MCI [pMCI], and 147 with AD) among four clinically relevant pairs of diagnostic groups at different imaging resolutions (i.e., 2, 4, 8, and 16 mm). Using a support vector machine classifier through a 10-fold cross-validation strategy at a spatial resolution of 2 mm, the proposed CAD framework yielded classification accuracies of 91.13%, 74.77%, 81.12%, and 81.78% in identifying AD/healthy control, sMCI/pMCI, sMCI/AD, and pMCI/HC, respectively. The experimental results show that a lower spatial resolution (i.e., 2 mm) may provide more robust information to trace the neuronal loss-related brain atrophy in AD.  
Alzheimer's disease; mild cognitive impairment; early detection; merit-based feature selection; voxel-wise +
Footnote †: journal: Applied Sciences
# A Framework for Constructing a Secure Domain of Sensor Nodes
## 3 The Concept of a Security Domain of Sensor Nodes
### The Concept of Protection for Sensor Nodes
#### 3.2.3 Protection Used in Sensor Nodes  
The mechanisms and security solutions for all types of sensor nodes (i.e., M node and R node) used in the sensor's secure domain are very similar. These include the following:  
* The TPM module is an obligatory element of the hardware configuration of each node;
* The TPM module supports the procedures of creating and using a local trust structure, generating symmetric keys, and determining SHA-1 and HMAC hashes;
* Cryptographic keys used by the sensor node, and other sensitive node data (among others N_ID, PAN_ID, CH, and PIK) are stored in the non-volatile memory of the TPM;
* Sensitive data regarding the secure domain stored in the EEPROM of the sensor node are protected by cryptography with the NK key stored in the TPM;
* The Platform Configuration Registers (PCRs) of the TPM enable the protection of the software and hardware of the sensor node against unauthorized modification.
## 2 Methodology
### Wavelet Response and Trace Energy  
To calculate the trace energy in the first arrival event, the following steps are followed:  
1. Window the first wavelet of the first arrival head-wave event while maintaining the timing information (\(T_{n}\)) of the first arrival on each trace.
2. Shift each \(n\)th windowed trace such that its first sample is at \(t=0\). This produces the ideal trace wavelet response (\(G_{cn}(t)\)). Sum \(G_{cn}(t)\) across \(n\) to produce the ideal array response (\(G_{c}(t)\)) and calculate its corresponding array trace energy (\(E_{c}\)) as follows: \[E_{c}=\sum_{m=1}^{M}G_{cm}^{2}(t),\] (15) where \(M\) is the number of samples in \(G_{c}(t)\).
3. Shift each \(G_{cn}(t)\) by its corresponding \(\Delta Te_{xm}\) to produce the trace wavelet response with position errors only (\(G_{xm}(t)\)). Sum \(G_{xn}(t)\) across \(n\) to produce the array response with position errors only (\(G_{x}(t)\)) and calculate its corresponding array trace energy (\(E_{x}\)) as follows: \[E_{x}=\sum_{m=1}^{M}G_{xm}^{2}(t),\] (16)  
The decibel (\(dB\))-scaled normalized array energy with position errors only \(E_{Nx}^{\prime}(dB)\) is calculated as follows:  
\[E_{Nx}^{\prime}(dB)=20\log_{10}E_{Nx}, \tag{17}\]  
where \(E_{Nx}\) is the normalized array trace energy with position errors only defined as:  
\[E_{Nx}=\frac{E_{x}}{E_{c}}, \tag{18}\]
4. Starting with \(G_{cn}(t)\) again, shift each \(G_{cn}(t)\) by its corresponding \(\Delta Te_{xm}\) to produce the trace wavelet response with elevation errors only (\(G_{zm}(t)\)). Sum \(G_{zm}(t)\) across \(n\) to produce the array response with elevation errors only (\(Gz(t)\)) and calculate its corresponding array trace energy (\(E_{z}\)) as follows: \[E_{Z}=\sum_{m=1}^{M}G_{Zm}^{2}(t),\] (19)  
The \(dB\)-scaled normalized array energy with elevation errors only \(E_{NZ}^{\prime}(dB)\) is calculated as follows:  
\[E_{NZ}^{\prime}(dB)=20\log_{10}E_{NZ}, \tag{20}\]  
where \(E_{Nz}\) is the normalized array trace energy with elevation errors only defined as:  
\[E_{NZ}=\frac{E_{Z}}{E_{c}}, \tag{21}\]
5. Starting with \(G_{cn}(t)\) again, shift each \(G_{cn}(t)\) by its corresponding (\(\Delta Te_{xn}+\Delta Te_{zm}\)) to produce the perturbed wavelet response of the trace with combined position and elevation errors (\(G_{zxn}(t)\)). Sum \(G_{xzm}(t)\) across \(n\) to produce the array response with both position and elevation errors (\(G_{xz}(t)\)) and calculate its corresponding array trace energy (\(E_{xz}\)) as follows: \[E_{xZ}=\sum_{m=1}^{M}G_{xZm}^{2}(t),\] (22)  
The \(dB\)-scaled normalized array trace energy for the perturbed case \(E_{NxZ}^{\prime}(dB)\) is calculated as follows:  
\[E_{NxZ}^{\prime}(dB)=20\log_{10}E_{NxZ}, \tag{23}\]where \(E_{Nxz}\) is the normalized array trace energy for the perturbed case defined as:  
\[E_{Nxz}=\frac{E_{xZ}}{E_{c}}, \tag{24}\]
## 5 Simulation and Results
### Simulation Results and Analysis for Urban Traffic Scenarios
At this optimal point, compared to the conventional OFDM point (N, K, M) = (8, 8, 4), the OFDM-IM system achieves 87.5% of the spectral efficiency of traditional OFDM but with a lower bit error rate and only 50% of the energy consumption, providing better system performance. Compared to the second-best point (N, K, M) = (8, 3, 4), although there are slight deficiencies in the energy consumption and bit error rate, the higher spectral efficiency makes the two points' energy efficiency values very close, making the second-best point a viable alternative when users are more concerned about energy consumption and the bit error rate. Figure 14c shows that, with N = 12 subcarriers, the highest energy efficiency Y value is achieved at (N, K, M) = (12, 5, 4). Compared to the traditional OFDM point (N, K, M) = (12, 12, 4), this optimal point achieves 79.15% of the spectral efficiency of traditional OFDM but with a lower bit error rate and  
Figure 14: Output results chart for urban traffic scenario. The star indicates the maximum point of the Y value. only 41.6% of the energy consumption, improving the overall system performance. The second-best point (N, K, M) = (12, 9, 2) has overall performance metrics that are inferior to the optimal point. In Figure 14d, with N = 16 subcarriers, the energy efficiency Y value is maximized at (N, K, M) = (16, 6, 4). This optimal point achieves 75% of the spectral efficiency of traditional OFDM (N, K, M) = (16, 16, 4) but with a lower bit error rate and only 37.5% of the energy consumption, delivering better system performance. Compared to the second-best point (N, K, M) = (16, 7, 2), the optimal point has higher spectral efficiency while reducing both the bit error rate and energy consumption, although the second-best point outperforms other points in terms of overall performance, leaving its energy efficiency value second only to the optimal point. From the simulation results, it can be concluded that the design of the fuzzy logic-based energy efficiency evaluation system is both effective and reasonable for urban traffic scenarios. By analyzing the characteristics and user needs of this scenario and synthesizing the overall performance of the three factors, accurate and reasonable membership functions and fuzzy mapping rules are established. The simulation results ultimately confirm that the optimal index parameters determined by the fuzzy logic output indeed maximize the system performance, allowing the parameters of OFDM-IM to be adaptively regulated according to the scenario.
###### Abstract  
The increase in capacity demand for vehicular communication is generating interest among researchers. The standard spectra allocated to VANET tend to be saturated and are no longer enough for real-time applications. Millimeter-wave is a potential candidate for VANET applications. However, millimeter-wave is susceptible to pathloss and fading, which degrade system performance. Beamforming, multi-input multi-output (MIMO) and diversity techniques are being employed to minimize throughput, reliability and data rate issues. This paper presents a tractable channel model for VANET in which system performance degradation due to error is addressed by concatenated Alamouti space-time block coding (ASTBC) and Bose-Chaudhuri-Hocquenghem (BCH) coding. Two closed-form approximations of bit error rate (BER), one for BCH in Rayleigh fading and the second for BCH with ASTBC, are derived. These expressions comprise SNR and code rate and can be utilized in designing VANET architectures. The results show that the BER using concatenated ASTBC and BCH outmatches the conventional BER ASTBC expression. The analytical results are compared with numerical results, thereby showing the accuracy of our closed-form expressions. The performance of the proposed expressions is evaluated using different code rates.  
milimeter-wave; VANET; BCH coding; Alamouti space-time coding; bit error rate +
Footnote †: journal: _electronics_  
_Acticle_  
Millimeter-Wave Channel Modeling in a Vehicular Network Using Bose-Chaudhuri-Hocquenghem (BCH) Code  
Footnote †: journal: _electronics_
# Optimization of Image Capture Distance for Facial Thermograms in Dentistry
## 3 Results
### Correlations of the Mean Temperature at ROI  
The mean temperature in the ROI was negatively correlated to the room temperature [\(\tau_{\rm s}\) (1348) = \(-\)0.150, \(p\) < 0.001] and room humidity [\(\tau_{\rm s}\) (1348) = \(-\)0.058, \(p\) = 0.033]. Wearing tight clothing or spectacles positively influenced the mean temperature in the ROI [tight clothes: \(\tau_{\rm s}\) (1348) = \(-\)0.206, \(p\) < 0.001; spectacles: \(\tau_{\rm s}\) (1348) = 0.136, \(p\) < 0.001; Table 6] while wearing a scarf/headphones/cap/hat/shawl on the head or wearing an object on the neck negatively influenced the mean temperature in the ROI (\(p\) < 0.001; Table 6).  
The mean temperatures in the ROI of the participants under medications were negatively correlated [sympathetic blockers: \(\tau_{\rm s}\) (1348) = \(-\)0.058, \(p\) = 0.034; steroids: \(\tau_{\rm s}\) (1348) = \(-\)0.123, \(p\) < 0.001; vasoactive drugs: \(\tau_{\rm s}\) (1348) = \(-\)0.058, \(p\) = 0.034; Table 6]. The mean temperature in the ROI was positively influenced by having undergone dental treatments [\(\tau_{\rm s}\) (1348) = 0.160, \(p\) < 0.001] and negatively correlated to having undergone physiotherapy sessions [\(\tau_{\rm s}\) (1348) = \(-\)0.179, \(p\) < 0.001] in the last 24 h, respectively (Table 6). Procedures in oral care influenced the mean temperature in the ROI [tooth brushing: \(\tau_{\rm s}\) (1348) = \(-\)0.183, \(p\) < 0.001; inter-dental cleaning: \(\tau_{\rm s}\) (1348) = 0.214, \(p\) < 0.001; mouthwash use: \(\tau_{\rm s}\) (1348) = 0.228, \(p\) < 0.001; Table 6]. Consuming coffee as well as smoking negatively influenced the mean temperature in the ROI [coffee: \(\tau_{\rm s}\) (1348) = \(-\)0.119, \(p\) < 0.001; smoking: \(\tau_{\rm s}\) (1348) = \(-\)0.219, \(p\) < 0.001; Table 6], while all other food and beverage consumption were positively correlated to the mean temperature at the ROI [bonbons: \(\tau_{\rm s}\) (1348) = 0.090, \(p\) < 0.001; eating: \(\tau_{\rm s}\) (1348) = 0.056, \(p\) = 0.040; drinking: \(\tau_{\rm s}\) (1348) = 0.056, \(p\) = 0.041; energy drinks: \(\tau_{\rm s}\) (1348) = \(-\)0.183, \(p\) < 0.001; Table 6]. Hair dryet use was positively correlated (\(p\) < 0.001) while the use of hot water on the face (\(p\) < 0.001) and shaving (\(p\) = 0.005) were negatively correlated to the mean temperature at the ROI (Table 6).
# CFAM: Estimating 3D Hand Poses from a Single RGB Image with Attention
###### Abstract  
Precise 3D hand pose estimation can be used to improve the performance of human-computer interaction (HCI). Specifically, computer-vision-based hand pose estimation can make this process more natural. Most traditional computer-vision-based hand pose estimation methods use depth images as the input, which requires complicated and expensive acquisition equipment. Estimation through a single RGB image is more convenient and less expensive. Previous methods based on RGB images utilize only 2D keypoint score maps to recover 3D hand poses but ignore the hand texture features and the underlying spatial information in the RGB image, which leads to a relatively low accuracy. To address this issue, we propose a channel fusion attention mechanism that combines 2D keypoint features and RGB image features at the channel level. In particular, the proposed method replans weights by using cascading RGB images and 2D keypoint features, which enables rational planning and the utilization of various features. Moreover, our method improves the fusion performance of different types of feature maps. Multiple contrast experiments on public datasets demonstrate that the accuracy of our proposed method is comparable to the state-of-the-art accuracy.  
Keywords:hand pose estimation; CFAM; 3D keypoint; RGB image; attention +
Footnote †: journal: Applied Sciences
# Research on Crack Width Measurement Based on Binocular Vision and Improved DeeplabV3+
###### Abstract  
Crack width is the main manifestation of concrete material deterioration. To measure the crack information quickly and conveniently, a non-contact measurement method of concrete planar structure crack based on binocular vision is proposed. Firstly, an improved DeeplabV3+ semantic segmentation model is proposed, which uses L-MobileNetV2 as the backbone feature extraction network, adopts IDM structure to extract high-level semantic information, introduces ECA attention mechanism, and optimizes the loss function of the model to achieve high-precision segmentation of crack areas. Secondly, the plane space coordinate equation of the concrete structure was constructed based on the principle of binocular vision and SIFT feature point matching, and the crack width was calculated by combining the segmented image. Finally, to verify the performance of the above method, a measurement test platform was built. The experimental results show that the RMSE of the crack measurement by using the algorithm is less than 0.2 mm, and the error rate is less than 4%, which has stable accuracy in different measurement angles. It solves the problem of fast and convenient measurement of the crack width of concrete planar structures in an outdoor environment.  
crack width; non-contact measurement; binocular vision; image processing; DeeplabV3+
# Polar Cap Patches Scaling Properties: Insights from Swarm Data
## 2 Data and Methods  
### Swarm Data  
Data used in this paper span approximately 3.5 years; more specifically, we investigate Swarm A observations between 16 July 2014 and 28 February 2018. Swarm A is one of the three satellites constituting the constellation, that was launched by ESA in November 2013 and is still in orbit today [22]. The constellation is designed so that two satellites, Swarm A and C, fly side-by-side at an altitude that was initially 460 km, and one satellite, Swarm B, flies around 50 km above the other two spacecrafts. The three satellites are equipped with identical instruments designed to guarantee high-precision measurements of physical quantities such as magnetic field, electron density, and temperature [23]. The orbital configuration of the constellation, with inclinations of 87.35\({}^{\circ}\) for Swarm A and C and of 87.75\({}^{\circ}\) for Swarm B, ensures that each polar cap region (Northern and Southern)is crossed once per orbit, making these satellites particularly suitable for investigating phenomena occurring at high latitudes.  
The study relies on electron density measurements from Swarm A, on corresponding values of the Rate Of Change of electron Density Index (RODI), on the PCP flag, and on LoL events identified in previous studies through the slant Total Electron Content (sTEC) measurements [24]. Swarm data are freely available at [[http://swarm-diss.eo.esa.int](http://swarm-diss.eo.esa.int)]([http://swarm-diss.eo.esa.int](http://swarm-diss.eo.esa.int)) (accessed on 31 August 2023).
# Machine-Learning-Based Android Malware Family Classification Using Built-In and Custom Permissions
###### Abstract  
Malware family classification is grouping malware samples that have the same or similar characteristics into the same family. It plays a crucial role in understanding notable malicious patterns and recovering from malware infections. Although many machine learning approaches have been devised for this problem, there are still several open questions including, \"Which features, classifiers, and evaluation metrics are better for malware familial classification\"? In this paper, we propose a machine learning approach to Android malware family classification using built-in and custom permissions. Each Android app must declare proper permissions to access restricted resources or to perform restricted actions. Permission declaration is an efficient and obfuscation-resilient feature for malware analysis. We developed a malware family classification technique using permissions and conducted extensive experiments with several classifiers on a well-known dataset, DREBIN. We then evaluated the classifiers in terms of four metrics: macrolevel F1-score, accuracy, balanced accuracy (BAC), and the Matthews correlation coefficient (MCC). BAC and the MCC are known to be appropriate for evaluating imbalanced data classification. Our experimental results showed that: (i) custom permissions had a positive impact on classification performance; (ii) even when the same classifier and the same feature information were used, there was a difference up to 3.67% between accuracy and BAC; (iii) LightGBM and AdaBoost performed better than other classifiers we considered.  
Android malware; malware family classification; machine learning; built-in permission; custom permission; balanced accuracy; Matthews correlation coefficient +
Footnote †: journal: Applied Sciences  
1  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Android Malware Family Classification Using Built-In and Custom Permissions.