esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Taxkorgan County is a bridge for the China-Pakistan Economic Corridor (CPEC) project. Landslides are numerous and widespread. The frequent landslides hinder CPECconstruction and threaten the locals' safety and livelihoods. Recently, climate change-related landslides have increased in alpine regions, particularly during the warm season. The scientific and accurate assessment and prediction of landslide susceptibility can provide a scientific reference for disaster prevention, land-use planning, and CPEC construction. In this paper, we focused on the static and dynamic environment factors (temperature and precipitation) and used the CF-LR model to evaluate and predict landslide susceptibility in Taskorgan County. The assessment and prediction were based on a catalog of climate change-related landslides over the past 20 years, the causative factors, and predicted climatic variables for the SSP1-2.6 scenario. The results indicated that elevation, slope, groundwater, LS factor, TWI, valley depth, and maximum precipitation were the key causes of slides below the snow line. The key causing factors of debris flow above the snow line were elevation, slope, topographic relief, aspect, LS factor, distance to the river, and maximum temperature. The accuracy of slide and debris flow susceptibility was 0.92 and 0.89, respectively. The area of slides with medium, high, and very high susceptibility is 25.5% of the total area. In addition, 82.6% of the slides occurred in this region, and 49.5% of the entire area is covered by debris flows with medium, high, and very high susceptibility. Moreover, this area accounts for 91.8% of all debris flows. Until 2060, the region's climate is anticipated to become warmer and wetter. Slides below the snow line will gradually decrease and shift eastward, and debris flows above the snow line will expand. Our findings will contribute to the management of landslide risks at the regional scale.  
Conceptualization, H.Q.; methodology, Y.P.; software, Y.P. and Y.Z.; investigation, Y.P. and Y.Z.; writing--original draft preparation, Y.P.; writing--review and editing, H.Q.; funding acquisition, H.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the International Science and Technology Cooperation Program of China (Grant No. 2018 YFE0100100).  
Not applicable.  
The original contributions presented in the study are included in the article, and further inquiries can be directed to the corresponding authors.  
We acknowledge the reviewers and academic editors for their positive and constructive comments and suggestions. The authors also thank Engineer [PERSON] from the Administration of Natural Resources of Kashgar for his assistance in the fieldwork.  
The authors declare no conflicts of interest.
# Robotic Systems and Navigation Techniques in Orthopedics: A Historical Review
## 3 Computer-Aided Navigation Systems (CANSs)
### Three Basic Elements
#### 3.1.3 Navigation  
Navigation is the kernel element and function of a navigation system. Given an accurate 3D model and accurate registration, the surgical tools and the patient's bone landmarks can be precisely tracked, either by a set of tracking devices or by the robot coordinate system. A sensor-based navigation system usually uses optical sensors or magnetic sensors to track the 3D positions of the target bones, the surgical tools, and any other objects (e.g., implants) that need to be tracked [25]. An optical IR-based system can track objects with high accuracy by using infrared (IR) light-tracking cameras and infrared light-reflecting markers [25]. However, the optical sensor-based tracking system can be easily affected by light-sight blocking. The magnetic sensor does not have this problem, but it may be affected by metallic objects within its workspace [25]. A novel approach for active optical navigation was proposed recently, where the optical tracking system (OTS) is installed on a robot that can actively adjust the pose of the OTS [72].  
Based on the working principles, CANS includes three typical types of navigation systems being used in orthopedic surgeries, i.e., CT-based, imageless, and fluoroscopic [25]. In 1994, two years after the ROBODOC was first used on humans, the first CT-based navigation system was developed and used in THA in Pittsburgh [17; 73]. CT-based navigation prevails among most of the previously introduced robotic systems. Although CT-based navigation has the highest accuracy, a big disadvantage is that acquiring preoperative CT images and making preoperative plans based on them are time consuming, which may bring more cost and radiation exposure to patients [25].  
Imageless navigation, also known as \"surgeon-defined anatomy\" technology, employs some other techniques, such as infrared (IR)-based tracking, stereotaxic technology, and bone-morphing technology. Note that, here, imageless or image-free means that no preoperative CT images are required but may still need camera-based intraoperative tracking for navigation. Together with the intraoperatively collected patient's anatomic data, an abstract of the patient's anatomy can be generated [17]. This kind of imageless technique is adopted by some robotic systems, such as the NAVIO system and Praxiteles. The accuracy of imageless navigation depends on the techniques adopted.  
Fluoroscopic navigation has a similar principle to imageless navigation but uses fluoroscopic images. Fluoroscopic navigation is good for use in trauma and spine surgeries but is limited in other orthopedic surgeries due to its cumbersome registration procedures [25].
## 3 Simulation Results  
To apply the wave domain ANC that minimizes the circular harmonics coefficients of the noise source, fast Fourier transform (FFT), inverse FFT (IFFT), spatial FFT, and spatial IFFT processes are required. Control of the frequency domain causes a delay of the size of at least one FFT block [3]. To solve this problem, a spherical harmonics analysis in the time domain was performed [25; 26]. However, the group delay of the finite impulse response filter is inevitable, and this group delay lowers the ANC performance. As shown in Figure 3, when \(r_{ref}=R_{mic}\), a good wave field reproduction performance was attained with only a reference circle among other values of \(r_{ref}\). Having a reference circle implies matching of the sound pressure of the desired wave field at the position of the microphone array, which suggests that, when the sound pressure is reduced at the microphone position, \"anti\" wave fields will be reproduced, as shown in Figure 4b. The ANC performance shown in Figure 5 can be achieved when the conventional Filtered-x least mean square (FxLMS) algorithm, which uses the sound pressure of the microphone as a cost function, is used. Considering this, experiments and simulations were conducted using the conventional ANC. The ANC simulation was performed in the same way as the existing settings. In this case, the adaptive feedback FxLMS algorithm was used to effectively manage the periodic signals originating from all directions [3] (see Figure 6); here, \(d(n)\) is the disturbance signal and \(e(n)\) is the error signal. The secondary path \(S(z)\) included an acoustic transfer function and digital-to-analog and analog-to-digital converters. One of the microphone array signals was used as the reference signal \(\hat{d}(n)\). \(\hat{S}(z)\) is the estimated secondary path, and \(\hat{r}(n)\) is the estimated filtered reference signal.
###### Abstract  
To improve the performance of analog, RF, and digital integrated circuits, the cutting-edge advanced CMOS technology has been widely utilized. We successfully designed and implemented a high-speed and low-power serial-to-parallel (S\({}^{2}\)P) converter for 5G applications based on the 28 nm CMOS technology. It can update data easily and quickly using the proposed address allocation method. To verify the performances, an embedded system (NI-FPGA) for fast clock generation on the evaluation board level was also used. The proposed S\({}^{2}\)P converter circuit shows extremely low power consumption of 28.1 uW at 0.91 V with a core die area of 60 \(\times\) 60 \(\upmu\)m\({}^{2}\) and operates successfully over a wide clock frequency range from 5 M to 40 MHz.  
serial to parallel converter; digital controller; embedded system; embedded hardware system; 5G; RF Front-End; low power; 28 nm CMOS; FPGA +
Footnote †: journal: Applied Sciences
## 4 Discussion and Result Analysis
### Attack Implementation Testing  
The proposed mechanism was tested by implementing an attack on it. The attack worked as follows.  
A user went through the required configuration process and then authenticated several times. The user put together a group of 6 people to attempt authentication on the user's own account. The user provided his own username and password to this group of people consisting of 2 close friends, 3 close family members (mother, father, and brother), and the user's girlfriend. This was done to focus the testing on the novel algorithm and to simulate that the login credentials of the mechanism's first authentication factor had been compromised.  
Each person out of the group attempted authentication several times. In this initial round of attempts there were no successful authentications; in fact, no one was able to go through the image selection step. Only two of the \"attackers\" were able to correctly guess 2 images out of the 4 required. This was all the success achieved by the attackers in this initial test.  
However, it was important to know how easy it was for attackers to guess the relations as well, so the following test was set up in a way that the \"attackers\" skipped straight on to the establishing relations step. In this test a total of 3 relations were guessed correctly out of around 10 attempts by each attacker. The common trait among the compromised relations is that they described the relation between the user and the said \"attacker\".  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Vulnerability** & **Graphical Passwords** & **Proposed Method** \\ \hline Smudge attack & YES & NO \\ \hline Dictionary attack & NO & NO \\ \hline Spyware/Key loggers & YES & NO \\ \hline Shoulder surfing & YES & NO \\ \hline Guessing & YES & NO \\ \hline \hline \end{tabular}
\end{table}
Table 3: Common attacks which other graphical passwords are vulnerable to compared to the proposed method.  
Nonetheless, the requested pictures were refreshed in every attempt, as well as the relationship needed for authentication, making it difficult for the attacker to guess correctly in every new attempt. Furthermore, most of the attackers would not be close people to the users and, considering that, in this test, the attackers had no success in authenticating the full process, account breaches seemed very unlikely.
## 3 Results and Discussion
### Estimation of the Fibre Content  
As can be seen in Figure 11, the calculated fibre contents for both electrode arrays were not satisfactory. Only in two cases was a good estimation possible. The first case was an electrode array with angular distances of 180\({}^{\circ}\) for a fibre content of 40 kg/m\({}^{3}\) and low frequencies of the alternating current. The second case was an electrode array with angular distances of 90\({}^{\circ}\) for a fibre content of 80 kg/m\({}^{3}\) and high frequencies of the alternating current. In all other cases, the calculated results did not correspond to the produced  
Figure 10: Electrical resistivity in the vertical direction with different combinations of electrodes depending on fibre content at a specimen age of 9 days and an electrode array with angular distances of 90\({}^{\circ}\).  
Figure 9: Electrical resistivity in the vertical direction with different combinations of electrodes depending on fibre content at a specimen age of 9 days and an electrode array with angular distances of 180\({}^{\circ}\).  
concrete. It is obvious that the fibre content is underestimated by the array with more electrodes, while the concrete with 80 kg/m\({}^{3}\) is strongly overestimated by the array with angular distances of 180\({}^{\circ}\). Independent of fibre content and electrode array, the frequency of the alternating current showed an effect on the calculated results, where a higher frequency resulted in a higher fibre content. This is because without knowing the phase angle of the impedance, which cannot be detected with this test setup, it is not possible to determine the real part of the impedance and thus, based on those results, it is not possible to evaluate the possibility of determining the fibre content via the electrical conductivity. For a higher specimen age, the tendency is almost the same, while all calculated fibre contents are increased by 5 to 15% (see Figure A6 in Appendix A), the electrode array with angular distances of 90\({}^{\circ}\) provides a large discrepancy between the results and expectations and the results become better fitting for the other electrode array.
###### Abstract  
Chronic kidney disease (CKD) is a progressive pathology characterized by gradual function loss. It is accompanied by complications including cardiovascular disorders. This study involves 4-h electrocardiographic records from the Telemetric and Holter ECG Warehouse (THEW) project database to analyze the dynamics in heart rate variability (HRV) indices of 51 patients with CKD. It proposes three algorithms to process long-term electrocardiography records: QRS complex and R-wave detection, premature ventricular contraction (PVC) identification, and tachyograms. PVCs were analyzed with the consideration of the changes occurring before, during, and after hemodialysis, especially during the interdialytic period. The hour with the highest PVCs occurrence was identified and used to assess HRV fluctuations and segmented into 5 min blocks with a 0.77 min overlap, yielding a dynamic HRV vector, one for each of seven HRV indices selected to evaluate autonomic nervous system balance. R-wave and PVC identification resulted in 97.53% and 85.83% positive predictive values, respectively. PVCs' prevalence and HRV changes' relationship in 48 h records could relate to cardiovascular risk. The stratification of hemodialysis patients into three distinct PVC patterns (\(p<0.001\)) identified two clinically significant high-risk subgroups: Class 1, indicative of electrical instability, and Class 3, of advanced autonomic dysfunction, demonstrating divergent arrhythmogenic mechanisms with direct implications for risk stratification.  
autonomic nervous system balance; chronic kidney disease; dynamic heart rate variability; interdialytic period; premature ventricular contractions patterns
## 2 Relevant Theoretical Basis
### YOLOv7 Algorithm  
YOLOv7 [19] is the basic model in the YOLO series, which exceeds the speed and accuracy of most existing target detectors in the range of 5-160 frames/s [20]. The structure of YOLOv7 consists of four main parts: the input, the backbone feature extraction network, the enhanced feature extraction network and the head prediction portion.  
In general, YOLOv7 initially resizes the input image to 640 \(\times\) 640, subsequently feeding it into the backbone network. The backbone of YOLOv7 primarily incorporates a CBS module, ELAN module, MPConv module and SPPCSPC module. The CBS module comprises a Conv layer, BN layer and SiLU activation function. ELAN modules effectively facilitate learning and convergence by controlling gradient paths, whereas deeper networks employ MPConv modules for downsampling. SPPCSPC utilizes both spatial pyramid pooling (SPP) and CSP structures to achieve richer gradient combinations. The enhanced feature extraction network implements multi-scale feature fusion and adaptive convolutional operations, enabling YOLOv7 to attain a significantly faster detection speed while maintaining high detection accuracy and meeting real-time requirements. Subsequently, the header network outputs three layers of feature maps of varying sizes, and the prediction results are ultimately generated via RepConv. The network structure of YOLOv7 is shown in Figure 4.  
Figure 3: Schematic diagram of electroluminescence original image and polarization degree image.
###### Abstract  
To avoid degradation of navigation performance in the navigation warfare environment, the multi-radio integrated navigation system can be used, in which all available radio navigation systems are integrated to back up Global Navigation Satellite System (GNSS) when the GNSS is not available. Before real-time multi-radio integrated navigation systems are deployed, time and cost can be saved when the modeling and simulation (M&S) software is used in the performance evaluation. When the multi-radio integrated navigation system M&S is comprised of independent function modules, it is easy to modify and/or to replace the function modules. In this paper, the M&S software design method was proposed for multi-radio integrated navigation systems as a GNSS backup under the navigation warfare. The M&S software in the proposed design method consists of a message broker and function modules. All the messages were transferred through the message broker in order to be exchanged between the function modules. The function modules in the M&S software were independently operated due to the message broker. A message broker-based M&S software was designed for a multi-radio integrated navigation system. In order to show the feasibility of the proposed design method, the M&S software was implemented for Global Positioning System (GPS), Korean Navigation Satellite System (KNSS), enhanced Long range navigation (eLoran), Loran-C, and Distance Measuring Equipment/Very high-frequency Omnidirectional Radio range (DME/VOR). The usefulness of the proposed design method was shown by checking the accuracy and availability of the GPS only navigation and the multi-radio integrated navigation system under the attack of jamming to GPS.  
GNSS backup; alternative navigation; multi-radio integrated navigation system; M&S software; message broker; navigation warfare; jamming +
Footnote †: journal: Electronics Electronics  
0000-0001-5800-7880]J.J.A.H.H.
## 4 Findings Results
### Descriptive Findings  
#### 4.1.1 Keyword, Title, and Abstract of Publication  
This research focused on \"supply chain management\" and \"demand forecasting\", using SCOPUS as the primary source database; consequently, 119 representative articles were selected. Using VOSviewer, a bibliometric analysis tool, keywords that appeared at least ten times were identified, and a keyword co-occurrence network diagram was constructed. The network diagram highlights frequently mentioned phrases, such as \"supply chain management\", \"machine learning\", \"forecasting\", \"artificial intelligence\", and \"deep learning\". These phrases summarize the content of the articles and illustrate the composition of research within this field.  
The prevalence of these keywords see Figure 3 suggests that there are numerous demand forecasting methods based on machine learning (ML) and deep learning (DL) solutions for supply chain management, as frequently discussed in the literature. This study highlights a significant emphasis on machine learning (ML) and deep learning (DL) tools to enhance the accuracy and efficiency of demand forecasting. This trend reflects a broader effort by researchers and industry professionals to integrate cutting-edge AI systems forimproved supply chain management. The repeated appearance of these keywords indicates their growing importance in addressing complex prediction problems.  
A closer examination of these keywords provides valuable insights into the key research issues and themes in the field. The analysis underscores the importance of advanced AI methods in contemporary supply chain demand forecasting, demonstrating the increasing adoption of such technologies for enhanced performance. This comprehensive investigation lays the foundation for further exploration of the use and impact of machine learning (ML) and deep learning (DL) technologies in supply chain management, emphasizing their potential to transform demand forecasting practices.
# An Evaluation of Image Slicing and YOLO Architectures for Object Detection in UAV Images
## 2 Related Work
[16] proposed a new SCA-YOLO model to improve object detection in UAV images. Based on YOLOv5, the SCA-YOLO model combines spatial and coordinate attention mechanisms to provide better feature extraction for object detection. They also added the SEB (simple and efficient bottleneck) module to reduce the effect of complex backgrounds. [PERSON] et al. [17] introduced a frequency domain decomposition approach to improve the overall performance of object detection in UAV images. Compared to the spatial decomposition in traditional methods, they observed that the differences in the frequency domain are more noticeable in the detection of objects. They used two learnable filters to separate domain-independent and domain-specific spectra, allowing the model to better generalize to complex and invisible target areas. They designed two new image- and sample-based contrastive losses. [PERSON] et al. [18] proposed feature enhancement, fusion, and context-aware YOLO (FFCA-YOLO) to detect objects in remote sensing images with the help of the Feature Enhancement Module (FEM), Feature Fusion Module (FFM), and Spatial Context Aware Module (SCAM). These modules worked together to enhance the weak feature representations of small objects while suppressing confusing backgrounds. They tested a lightweight model called L-FFCA-YOLO, designed to provide high speed and efficiency with lower computational power requirements. [PERSON] et al. [19] conducted a study on object detection in images captured by low-altitude UAVs. They proposed a feature-guided enhancement (FGE) module that emphasizes discriminative features, along with a scale-aware weighted (SAW) loss function. This loss function dynamically adjusted the importance of different objects, with a particular focus on smaller objects. [PERSON] et al. [20] introduced LUD-YOLO, a YOLO-based object detection network for detecting objects in UAV images. LUD-YOLO is an enhanced version of YOLOv8 and was optimized specifically considering UAVs' limited processing power and memory capacity. With LUD-YOLO, they proposed new approaches such as multi-scale feature fusion and dynamic sparse attention mechanism. [PERSON] et al. [21] proposed a novel method combining the YOLO-CSP architecture with the Slicing Assisted Hyper Inference (SAHI) framework to detect fundus lesions. Their method achieved successful results in their study using the DDR dataset.