esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Novel Deployable Solar Panel and Mechanism for 6U CubeSat of STEP Cube Lab-II
## 4 Dynamic Characteristics Investigation  
In the constrained layer damping strategy, the transmitted vibration energy dissipation is highly dependent on the viscoelastic core layer thickness, dynamic modulus, thickness of constraining layers, and vibration frequency [30; 31]. Therefore, a solar panel free-vibration test was executed at 25 \({}^{\circ}\)C ambient room temperature under the boundary condition in which the interfaces of the hinge and HRM holes were rigidly clamped. The roving hammer method was used to excite the solar panel in its free vibration. An accelerometer sensor was mounted at the solar panel center to obtain the time-domain frequency responses. To investigate the design usefulness or practicality for rapid suppression of panel chattering vibration during its in-orbit performance, the solar panel module's acceleration profile under deployment was also measured in the same room temperature environment. The dynamic characteristics investigation tests of a typical PCB panel of the same size without attaching additional stiffeners were also performed to compare the results obtained from the VMLSA. Figure 9a,b show the solar panel time histories in the free-vibration tests at the rigidly mounted condition and the acceleration profile under deployment, respectively. The results illustrate that stiffeners with viscoelastic acrylic tape can effectively suppress the transmitted vibration on the solar panel.  
Figure 8: Solar panel’s wire-tightening process.  
Table 7 summarizes the solar panel's free-vibration test results in the rigidly mounted condition. The results show that the VMLSA's first eigenfrequency and damping ratio were 110.1 Hz and 0.141, higher by a factor of 1.33 and 3.9, respectively, compared to those of the typical PCB solar panel. The results show that the application of viscoelastic multilayered thin stiffeners significantly improved the vibration attenuation along with increased solar panel rigidity due to shear deformation characteristics and tough surface roughness between the interlaminated layers. Furthermore, this design strategy could also be advantageous for rapid attenuation of in-orbit vibration on the panel caused by rigid body motion of the satellite during slew maneuver as the panel chattering vibration under deployment was effectively reduced.
# Security Analysis of the Symmetric Cryptosystem Tiny Jambu
###### Abstract  
Symmetric cryptography provides the best examples of cryptosystems to be applied in lightweight environments (e.g., IoT). A representative example is the cryptosystem TinyJambu, one of the ten finalists in the NIST Lightweight Cryptography Standardization Project. It is an authentication encryption with associated data scheme that is extremely lightweight and fast. In this work, we analyze the security of TinyJambu from two distinct and non-symmetric points of view: (1) the improvement of the best cryptanalytical attack found in the literature and (2) a randomness analysis of the generated sequences. Concerning item (1), we launched a differential forgery attack with probability \(2^{-65}.948^{\prime}\), which was improved considerably compared with previous numerical results. Concerning item (2), we analyzed the degree of randomness of the TinyJambu keystream sequences with a complete and powerful battery of statistical tests. This non-symmetric study shows the weakness of TinyJambu against cryptanalytic attacks as well as the strength of TinyJambu against statistical analysis.  
symmetric cryptography; TinyJambu; differential cryptanalysis; randomness; IoT 1
## 3 Results and Discussion
### Pharmacokine-Based Design
A model with an AUC value close to 1 shows that the pharmacophore model can effectively differentiate between active and inactive compounds with high accuracy and precision [84]. Among the ten models, Model 2 demonstrated the best performance, identifying 859 hit compounds with an AUC value of 0.97, an enrichment factor (EF) of 4.7, sensitivity of 96%, specificity of 84%, and overall accuracy of 85%. Based on these results, Model 2 was selected for subsequent pharmacophore-based virtual screening. Virtual screening, conducted using the validated Model 2 via LigandScout, revealed that all PBD compounds were identified as hit compounds, indicating strong pharmacophore feature alignment (Table 8). Notably, PBD-17 and PBD-20 exhibited the highest pharmacophore fit scores (45.20), suggesting a strong match with the pharmacophore model. These two compounds demonstrated key interactions involving one hydrogen-bond donor (HBD), one hydrogen bond acceptor (HBA), and two hydrophobic interactions  
Figure 9: Visualization of the essential features of a ligand-based pharmacophore.
# Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches
## 2 Security Challenges and Malware Attacks in the IoT
### Malware Analysis and Detection  
Malware is defined as malicious software that is executed within the system without the user's permission. Black hats, hackers, and crackers are all names for malware writers and developers. Writers have different intentions when creating this malicious executable software; e.g., internal threats, governance purposes, and spying on competitors. \"Traditional\" malware was often written using simple techniques and was designed with predictable intentions [21]. \"Next-generation\" malware, on the other hand, is designed with multiple malicious intents and leverages advances in technology for a more sophisticated design. The marriage of fast-spreading IoT systems and the inherent vulnerability and increased sophistication of malware attacks renders malware analysis and detection more critical but also more challenging.
###### Abstract  
To analyze the derotation of maxillary meso-rotated first permanent molars in subjects with Class II edge-to-edge dental malocclusion in mixed dentition treated with Invisalign Clear Aligners (CA). In total, 36 patients (16 males, 20 females, 9.9 \(\pm\) 1.9 years) treated with CA are enrolled from the Department of Orthodontics. Inclusion criteria are the following: Caucasian ancestry, mixed dentition, molar Class II edge-to-edge, no tooth/craniofacial anomalies, no caries/periodontal diseases. Pre-treatment (T1) and post-treatment (T2) digital casts, and final ClinCheck representations (T2 ClinCheck) are acquired. The Henry's angle (HA) is used to assess maxillary first molars rotation. The molars with an HA \(>\) 11\({}^{\circ}\) are taken (53 teeth). Five measurements are performed at T1, T2, and T2 ClinCheck: Henry's angle (HA), mesibocal-expansion (MBE), distubocal-expansion (DBE), mesibocal-asagittal (MBS), and distubocal-asagittal (DBS). A paired \(t\)-test was used to compare T2-T1 and T2 ClinCheck-T2. The T2-T1 shows a distal-totation (difference \(-\)6.3\({}^{\circ}\)) with an expansion of 2.2 mm for MBE and 1.5 mm for DBE. At T2, the mesibocal cusps show a distal movement of 1.0 mm and the distubocal cusps of 0.9 mm. The HA's T2 ClinCheck-T2 difference is \(-\)4.2\({}^{\circ}\). In the sagittal plane, the difference is 0.9 mm for the MBS and 0.7 mm for the DBS. The expansion showed the highest predictability (60% HA, 52.6% MBS, and 56.25% DBS). The CA effectively produces an arch expansion and upper molars' distal rotation. Upper molar derotation provides a 1 mm of gain in arch perimeter and occlusal improvement.  
class II malocclusion; clear aligner therapy; digital cast analysis 2022
# Gaussian Process Modeling of Specular Multipath Components
## Appendix A Variance of \(\
u_{k}\)  
Here, the variance of DMC \(\
u_{k}(\mathbf{p})\) within one estimated SMC amplitude is derived. For the sake of readability, we omit the agent position \(\mathbf{p}\). The DMC of an estimated SMC amplitude is given as  
\[\
u_{k}=\iint s(\tau-\lambda)\
u(\lambda)s^{*}(\tau-\tau_{k})\mathrm{d}\tau \mathrm{d}\lambda \tag{1}\]  
where the energy of the signal \(s(t)\) is assumed to be one, that is, \(\int_{-\infty}^{\infty}|s(t)|^{2}dt=1\). Using (1), the variance of the DMC of an estimated SMC amplitude  
\[\mathbb{E}\left\{|\
u_{k}|^{2}\right\} =\mathbb{E}\left\{\iiint s(\tau-\lambda)\
u(\lambda)s^{*}(\tau- \tau_{k})s^{*}(\tau^{\prime}-\lambda^{\prime})\
u^{*}(\lambda^{\prime})s(\tau ^{\prime}-\tau_{k})\mathrm{d}\tau\mathrm{d}\lambda\mathrm{d}\tau^{\prime} \mathrm{d}\lambda^{\prime}\right\}\] \[=\iiint s(\tau_{k}-\lambda)s^{*}(\tau^{\prime}-\lambda)s(\tau- \tau_{k})s(\tau^{\prime}-\tau_{k})S_{\
u}(\lambda)\mathrm{d}\tau\mathrm{d} \tau^{\prime}\mathrm{d}\lambda\] \[=\int\tilde{s}(\tau_{k}-\lambda)\tilde{s}^{*}(\tau_{k}-\lambda)S_ {\
u}(\lambda)\mathrm{d}\lambda \tag{2}\]  
where \(\tilde{s}(t)=\int s(\tau)s^{*}(t-\tau)\mathrm{d}\tau\). In the case a large bandwidth (UWB) is assumed, (2) can be approximated by  
\[\mathbb{E}\left\{|\
u_{k}|^{2}\right\} \cong S_{\
u}(\tau_{k})\int|\tilde{s}(t)|^{2}dt\] \[=S_{\
u}(\tau_{k})\int_{-\infty}^{\infty}|S(f)|^{4}\mathrm{d}f \tag{3}\]  
where \(S(f)\) is the Fourier transform of \(s(t)\) and \(T_{\mathrm{P}}=\int_{-\infty}^{\infty}|S(f)|^{4}\mathrm{d}f\) is the effective pulse duration.  
1. Assuming a signal \(s(t)\) with block-spectrum, the effective pulse duration is given as \(T_{\mathrm{P}}=\int_{-\infty}^{\infty}|S(f)|^{4}\mathrm{d}f=T_{\mathrm{s}}\), where \(T_{\mathrm{s}}\) is the Nyquist sampling time.
2. For root-raised-cosine, raised-cosine, or similar waveforms having a rectangular spectrum with tapered ends, \(T_{\mathrm{P}}\) is less than the Nyquist sampling time \(T_{\mathrm{s}}\), under common symmetry conditions. For example, a raised-cosine pulse results in \(T_{\mathrm{P}}=\int_{-\infty}^{\infty}|S(f)|^{4}\mathrm{d}f=\frac{4-\beta}{4 }T_{\mathrm{s}}\), where \(\beta\) is the roll-factor.
# Analyzing Zone-Based Registration Using a Three Zone System: A Semi-Markov Process Approach
## 4 Numerical Results
### Effects of the Number of Cells in a Zone and the Registration Cost of One Registration  
In this section, the number of cells in a zone, \(n\), is assumed to be 4. The number of cells in a zone (\(n\)) directly affects the paging cost \(C_{P}\). As \(n\) increases, \(C_{P}\) increases proportionally. From the viewpoint of 3Z, the increase of \(n\) may degrade the performance of 3Z since the paging cost \(C_{P}\) increases steeply compared to 1Z or 2Z. As a result, if \(n\) is far greater than 4, 3Z may show worse performance than 2Z. On the other hand, if \(n\) is far less than 4, 3Z may show better performance than 2Z.  
Note that this kind of scalability can be applied to the other parameters in the similar way. For example, let us consider registration cost. In this section, the registration cost of one registration, \(U\), is assumed to be 4. \(U\) directly affects the registration cost \(C_{U}\). As \(U\) increases, \(C_{U}\) increases proportionally. From the viewpoint of 3Z, the increase of \(U\) may upgrade the performance of 3Z since the location registration cost \(C_{U}\) increases moderately compared to 1Z or 2Z. As a result, if \(U\) is far greater than 4, 3Z may show better performance than 2Z. On the other hand, if \(U\) is far less than 4, 3Z may show worse performance than 2Z.
# Technological Development and Application of Photo and Video Theodolites
## 3 Measurement and Processing Approaches
### IATS Basic Principles  
The combination of cameras with image sensors and theodolites was described long ago by [29; 56]. In this section, we explain the basic working principles of state-of-the-art IATS Leica M550, which are also applicable to MS60 and other IATS produced by manufacturers with two cameras (overview and telescope). Overview and telescope cameras offer the possibility to record high-resolution images or panoramic image mosaics, which can be directly linked and referenced to the 3D point measurement system. These images can be used for field documentation and, most importantly, for a posteriori photogrammetric processing. The photogrammetric resolution of 1 px of the telescope camera corresponds to 5 cc, which results in a resolution of 2 mm at 200 m [57]. The expert uses the overview camera for roughly aiming at the target, signalized or unsignalized. The overview camera image gives an overview of the surveying area; alternatively, a live video can be observed on instrument display. Using the telescope camera, which is located on the optical axis, a telescope magnification of 30\(\times\) is at the expert's disposal for very precise target aiming (Figure 9).  
The achievable accuracy of angular measurements using this camera is 1\". Using the integrated image sensor, single autofocus and continuous autofocus functions are possible. Integration of the image sensor is done in such way that a focused image is simultaneously visible on the instrument display and on the instrument optical path through the ocular. A Porro prism reflects the incoming light through the telescope optics onto the imaging CMOS sensor and directly onto the ocular for visual aiming if needed. The focusing module production guarantees a 1\" angular measurement accuracy. Adjustment and calibration processes are key factors to achieve the highest quality of the imaging functionality.  
Prior to the available IATS, there were RTS, which, with implemented ATR functions, are also IATS in the narrowest sense since image sensors or segmented photodiodes are used for signal target detection and aiming. However, these image sensors could not be used for image documentation and photogrammetric measurements because the expert did not have access to the images and because only the light in the spectrum measurement signal was detected by imaging sensors or photodiodes [58]. The difference today is evident in the way in which the expert extracts the information from the integrated camera, i.e., from a single image (point of time) or from a video (sequence of images). We can  
Figure 9: Leica MS50 schematic cross-sectional view of the telescope with image sensors and processor integration, as well as motorization for autofocus; scheme modified for this study from [57].  
also distinguish static or kinematic processes. With modern digital imaging sensors, both measuring tasks are possible and no differentiation of the instrument is necessary [8].
## 4 Overview of GNSSs
### Gps
#### 4.1.2 GPS Space Segment  
The space segment is composed of a constellation of 24 satellites, including 21 operational and three backup satellites. Four of satellites are evenly distributed in six orbital planes, inclined at 55\({}^{\circ}\). The difference between the ascending nodes of each orbital plane is 60\({}^{\circ}\), and the difference in latitude arguments for satellites on the same orbital planes is 30\({}^{\circ}\). This ensures that at least four GPS satellites can be seen at anytime and anywhere in the world. The average orbital altitude of GPS satellites is about 20,200 km, and their orbital period is about 11 h 58 min 2 s [81].
# Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies
## 4 Formation Damage Diagnosis and Technology Development Direction Prediction
### Combined Use of Laboratory-Based Experiments and Imaging Techniques to Diagnose Formation Damage  
Formation damage evaluation using logging data can only qualitatively determine whether the formation is damaged or not. Core flow experiments are suitable for studying single-factor damage, which can hardly reflect formation damage pattern on a large scale. Moreover, experimental conditions are idealized, difficult, and time-consuming when taking cores. Electron microscopy, CT scan, and NMR analysis are costly and cannot distinguish between damage factors. Mathematical models are highly targeted, lacking in universality, and obtaining computational parameters is difficult. Artificial intelligence methods lack widespread data support. Currently, no technique can quantify the impact of formation damage on pore structure and production capacity while comprehensively evaluating each damage factor in formation damage. Therefore, future researchers can combine two or more of these approaches, such as using core flow and imaging techniques, to reveal formation damage micro-mechanisms in terms of both seepage capacity and hydrocarbon formation space.
## 2 Simulation of Rail Surface Initiated RCF
### Surface Initiated RCF of Turnout Rail  
In order to analyze the surface initiated RCF of turnout rail under different wheel-rail creep conditions, the calculations are presented at the section of switch rail with the head width 35 mm in a high-speed railway turnout called CN60-1100-1:18. The situation where the rail profiles contact with a new profile of LMA tread is analysed. The axle load applied on the wheel-set is 12 t, and the nominal wheel rolling radius is 430 mm. The assumed material properties of the wheel are the same as those of the rail, the elastic modulus, Poisson's ratio and wheel-rail friction coefficient are \(2.1\times 10^{11}\) N/m\({}^{2}\), 0.3 and 0.3 [29], respectively, and the ultimate shear strength of the rail material is 350 MPa. When the lateral wheel-set displacement \(y_{w}\) results are 0 mm, 3 mm, 6 mm and 9 mm, respectively, the wheel-rail contact point positions of the selected section will be those as shown in Figure 4. In this figure, the coordinate zero point refers to the center of railway track, \(y\)_and_\(z\) refers to the lateral and vertical direction of railway line, respectively.  
Figure 3: Combined structure of stock/switch rails.  
As shown in Figure 4, when the lateral wheel-set displacement is 9 mm, the wheel comes into contact with the switch rail and stock rail at the same time. Multi-point contact is one of the main characteristics of wheel-rail contact relations in turnout zones. Therefore, this paper studies the surface initiated RCF of turnout rails under different wheel-rail creep conditions, taking the lateral wheel-set displacement of 9 mm as an example.
# Driver Intention Recognition for Mine Transport Vehicle Based on Cross-Modal Knowledge Distillation
## 3 Result and Discussion
### Driving Intention Recognition Analysis Based on Video Data  
For video data classification, six methods are evaluated. A stratified sampling approach randomly selects 8 video segments per category, yielding 32 video segments for the test set. The remaining 124 segments are divided into training and validation sets using a 5-fold cross-validation, maintaining a 100:24:32 ratio for training, validation, and testing. Each video segment is processed by extracting 16 frames for both training and testing. Training data is resized to 112 \(\times\) 112 pixels, followed by image augmentation through affine transformations, which includes random vertical and horizontal scaling translations, with the maximum translation scale being 0.1 times the maximum pixel value, and then standardized. The models are trained for 150 epochs using L2-regularized cross-entropy loss with a regularization parameter of 0.01. Optimization is performed using the AdamW algorithm [36] with a learning rate of \(3\times 10^{-5}\), reduced by a factor of 0.1 after 100 epochs. The batch size is set to 8. The selected methods for driving intention recognition from video data include several advanced models. The R3D Network [37] enhances video sequence analysis by integrating 3D convolutional layers into ResNet, leveraging cross-layer residual connections to effectively capture spatio-temporal features. The R(2+1)D Network [38] improves efficiency by decomposing 3D convolutional networks into separate 2D and 1D temporal convolutions, achieving a balance between computational cost and performance. The LRCN Network [39] combines the image feature extraction capabilities of CNNs with the temporal modeling strengths of recurrent neural networks (RNNs), allowing for dynamic feature extraction and understanding of semantic content in video sequences. In this experiment, two variants of LRCN are utilized: LRCN_18, which employs ResNet18, and LRCN_34, which uses ResNet34 as the feature extraction structure. Then, a BiLSTM network is used to further extract the temporal information from the images at each time step, and the hidden state of the last time step is selected to determine the final state. The SlowFast Network [40] employs two parallel pathways, one for low frame rates to capture slow changes and the other for high frame rates to detect fast actions, enabling detailed analysis of both slow and fast movements in video data. Two variants of SlowFast are used in this experiment: SlowFast_32, which utilizes ResNet32, and SlowFast_50, which employs ResNet50 as the backbone. To evaluate the performance of different algorithms, the average evaluation metrics of each optimal model were computed on the test set during five-fold cross-validation. These metrics include the average precision (Avg. Precision), average recall (Avg. Recall), average F1 score (Avg. F1) for each category, overall average accuracy (Avg. Accuracy), and the model file size (MB). The results are presented in Table 2.
## 7 LPWAN Topologies and Architecture
### Mixed Hybrid Architectures  
The basic architecture provides direct device connectivity to the access station associated with the LPWAN technology. Other access configurations may also be considered. Two such prominent ones are discussed here.  
Figure 5 shows the architecture wherein the primary connectivity to a device is provided by different access technologies like ZigBee, Wi-Fi, and so on. The corresponding gateway interfaces with the access point of an LPWAN. This is particularly relevant for cellular LPWANs.  
Another architecture that uses multiple LPWAN networks for providing a range of interfaces to end devices [15] is shown in Figure 6. It shows an example where devices have access to both LoRa and Sigfox systems. Such multi-technology resident hybrid networks are particularly relevant for complex applications requiring different LPWAN technologies. Each LPWAN technology collects the data from the devices and nodes located in their coverage areas. The corresponding base or access stations are deployed consistent with their coverage area. The LPWAN gateway or nodes can communicate the user traffic data to the core network and the cloud. In such a mixed architecture, the associated network server or core network entities perform the functions of device management such as registration, authentication, resource allocation, and data traffic management to the devices connected to their network.  
New approaches and architectures based on artificial intelligence (AI) to create cognitive-LPWAN solutions are also being studied [16]. These approaches require powerful cognitive computing ability to support advanced communication, handle heterogeneous IoT applications, and enable software defined networking (SDN). Cognitive-LPWANs support co-existence and interworking of a mix of a variety of LPWAN technologies and provide users with more efficient and convenient intelligent services. It supports several applications, including smart cities, green IoT, and general heterogeneous networks, as well as AI applications such as smart home, health monitoring, automatic driving, and emotional interaction.  
Figure 5: Generalized cellular type architecture.