esa-sceva/satcom-chunk-collection huggingface dataset:
# Allograft Customized Bone Blocks for Ridge Reconstruction: A Case Report and Radiological Analysis
## 1 Introduction  
Patients with a highly resorbed atrophic mandible who require implants have several treatment options. Inferior alveolar nerve lateralization is an alternative to augmentation; however, neurosensory dysfunctions, as well as mandibular fractures, may occur [1]. Short implants usually do not require simultaneous bone grafting, resulting in reduced surgery time and patient morbidity compared to bone grafting and regular implant placement in a one- or two stage approach [1]. However, when the bone volume is not sufficient for short implant placement, vertical bone augmentation needs to be considered as a serious treatment option.  
With the modern CAD/CAM technology, it is possible based on a CBCT- or CT-scan to create patient specific bone blocks to rebuild lost hard tissue volumes. Virtual planning of the bone block requires advanced knowledge of both anatomical, as well as implant, considerations. This method has been able to show promising results in several case reports over the last couple of years [2; 3; 4; 5; 6; 7]. Puros[8] Allograft Customized Bone Blocks (ACBB) have been available since 2014. They are prepared based on the multi-step Tutorial(r) tissue sterilization process, which was developed in 1969 [4]. After purification of the donor material and milling in a clean environment, the block was packaged and sterilized with low-dose gamma irradiation [8].  
Apart from the hard tissue grafting procedure, the appropriate soft tissue management is mandatory. The coronal advancement of the buccal flap technique described by [PERSON] [9], as well as coronally advanced lingual flap, are safe and predictable techniques to achieve tension-free soft tissue closure of the elevated flap without the risk of dehiscence [10]. Implants should be placed approximately after 6 months of healing time, since the allograft blocks will be integrated and remodeled by so-called \"creeping substitution\", which has been shown to happen after 5 to 6 months [11; 12].  
A previous paper showed the bone dynamics and remodeling considerations for autogenous cortico-cancellous blocks, which can be also here taken into consideration for the use of allograft bone block materials [13].  
Graft resorption during the initial healing period may be a result of the initial remodeling of the graft. Collagen-rich alleogenous bone remodels relatively fast, which might be accompanied by superficial bone loss. Soft tissue management remains crucial [3].
## 2 Study Site and Data Processing
### Eddy Covariance Measurements and Meteorology Data at the Xiangtang Station
Figure 2: Daily meteorological conditions and 8 day NDVI at the Xiangtang station, (**a**) the downward shortwave and longwave radiation, (**b**) air temperature, (**c**) vapor pressure deficit, (**d**) wind speed, (**e**) precipitation and (**f**) NDVI. The smoothed NDVI curve was obtained by the Hants method (NDVI\({}_{\text{hants}}\)). tions. The data quality check followed the method in [PERSON] et al. [36], and the low-quality data were discarded in this study. Approximately 18.7% of the half-hour data points were removed as a result of a malfunction of the sensors, rain/fog influence, calibrations, and quality checks. The net ecosystem exchange (NEE) measurements of carbon dioxide are then decomposed into GPP and \(\mathrm{R_{eco}}\) (Respiration of the ecosystem) using the nighttime-based partition method [41; 42], in which the \(\mathrm{R_{eco}}=\mathrm{f(T_{a})}\) relationship is established using the nighttime NEE data. The daytime \(\mathrm{R_{eco}}\) is then estimated using the Reco = f(Ta) relationship and daytime \(\mathrm{T_{a}}\), and therefore GPP during daytime is then estimated as the difference between daytime NEE and daytime \(\mathrm{R_{eco}}\). The surface energy balance closure at the Xiangtang station was 71% on a daily scale. The heat storage correction on soil heat flux was calculated using the method of [PERSON] et al. [43], and the energy balance correction on H and LE was performed using the Bowen-ratio method [44]. Data gaps were filled by three methods [45], including linear interpolation (gap \(<\) 2 h), look-up table method (gap within 2-12 h), and the multi-regression method (gap \(>\) 12 h). Missing net radiation, air temperature, relative humidity, and wind speed data were filled by data collected at the national standard meteorological station located near the Xiangtang station.
## 3 Numerical Results
### Two-Dimensional Channel Tests
Since the turbulent heat flux is computed as \(\langle u^{\prime}T^{\prime}\rangle=\alpha_{t}T_{,x}\) and the temperature profile perfectly matches the reference one, the overestimation of the heat flux is due to the modeled value of the eddy thermal diffusivity. At low Reynolds, in order to capture these components, an appropriate non-isotropic model may be needed. However this difference in the turbulent heat flux does not influence the temperature profile because the molecular heat flux has a great importance for these very low Prandtl and Reynolds numbers, and for very high \(y^{+}\) (see [5]), as can be seen from the comparison of \(q_{R}^{+}\) and \(q_{eff}^{+}\).
# Joint Power Allocation and Link Selection for Multi-Carrier Buffer Aided Relay Network
## 3 Proposed Solution
4:Find (a) \(\sum_{i=1}^{N}\sum_{t=1}^{T}R_{i,t}^{2*}=\sum_{i=1}^{N}\sum_{t=1}^{T}R_{i,t}^{2 *}-\sum_{i=1}^{N}R_{i,t}^{2}\), (b) \(\sum_{i=1}^{N}\sum_{t=1}^{T}R_{i,t}^{1*}=\sum_{i=1}^{N}\sum_{t=1}^{T}R_{i,t}^{ 1*}+\sum_{i=1}^{N}R_{i,t^{*}}^{1}\). 5:Assign \(\sum_{i=1}^{N}R_{i,t}^{3}=-\infty\), \(\alpha_{t^{*}}=1\). 6:Repeat step 3 to step 5 until \(\sum_{i=1}^{N}\sum_{t=1}^{T}R_{i,t}^{2*}\leq\overline{A}\). ```  
**Algorithm 1** Proposed solution 1. Now, it is remain to allocate optimal powers at both source and relay nodes. The power optimization at relay can be obtained through (11) while for the source node we have to solve the following optimization problem:  
\[\lim_{T\to\infty}\frac{1}{T}\sum_{t=1}^{T}\sum_{i=1}^{N}(\alpha_{t })R_{i,t}^{1*}\geq\]4 (14) s.t. \[\lim_{T\to\infty}\frac{1}{T}\sum_{t=1}^{T}\sum_{i=1}^{N}(\alpha_{t })R_{i,t}^{1*}\geq\]5  
Solution of (14) is similar to (11), hence, is excluded from the paper for simplicity. The above designed algorithm gives nearly optimal solution. However, it may be possible that \(\sum_{i=1}^{N}R_{i,t}^{3}\) maximizes for more than one value of \(t^{*}\). Thus, in order to make it more practical, we have also proposed another approach. Step-wise description is given in Algorithm 2. Please note that the proposed model can be extended to a multi-hop scenario in a straightforward manner. The separate power constraint at each transmitting node provides the opportunity to apply the solution to the multi-hop case without any key difference. Moreover, the link selection, at a given time slot, can also be considered for any hop directly. Thus, to avoid the heavy rotational burden, the solution for the multi-hop case is omitted for simplicity. ```
1:Initialize \(\overline{A}\). 2:Let \(\sum_{i=1}^{N}R_{i,t}^{3}=\sum_{i=1}^{N}R_{i,t}^{1*}-\sum_{i=1}^{N}R_{i,t}^{2*}\quad \forall t\). 3:And \(x=\max\sum_{i=1}^{N}R_{i,t}^{3}\). 4:For \(t=1\) to \(T\), find \(t^{*}\) such that \(\sum_{i=1}^{N}R_{i,t^{*}}^{3}=x\). 5:Then \(\sum_{i=1}^{N}R_{i,t^{*}}^{4}=\sum_{i=1}^{N}R_{i,t^{*}}^{3}-\sum_{i=1}^{N}R_{i, t^{*}}^{2}\quad\forall t^{*}\). 6:Select \(tt^{*}=\arg\max\sum_{i=1}^{N}R_{i,t^{*}}^{4}\). 7:Obtain (a) \(\sum_{t=1}^{T}\sum_{i=1}^{N}R_{i,t}^{2*}=\sum_{t=1}^{T}\sum_{i=1}^{N}R_{i,t}^{ 2*}-\sum_{i=1}^{N}R_{i,tt^{*}}^{2}\), (b) \(\sum_{t=1}^{T}\sum_{i=1}^{N}R_{i,t}^{1*}=\sum_{t=1}^{T}\sum_{i=1}^{N}R_{i,t}^{ 1*}+\sum_{i=1}^{N}R_{i,tt^{*}}^{1}\). 8:Put \(\sum_{i=1}^{N}R_{i,tt^{*}}^{4}=-\infty\), \(\alpha_{tt^{*}}=1\). 9:Repeat step 5 to step 8 until \(\sum_{i=1}^{N}\sum_{t=1}^{T}R_{i,t}^{2*}\leq\overline{A}\). 10:Update \(D=\sum_{i=1}^{N}\sum_{t=1}^{T}R_{i,t}^{2*}\). 11:Repeat steps 3 to 6 of Algorithm 1. 12:Refine \(D=\max\ (D,\ \sum_{i=1}^{N}\sum_{t=1}^{T}R_{i,t}^{2*})\). ```  
**Algorithm 2** Proposed Solution 2.
# An Overview of CNN-Based Image Analysis in Solar Cells, Photovoltaic Modules, and Power Plants
## 3 Exhibition of Recent Literature  
In this section, the reader can observe the results and trends in recent CNN-based research. Overall, 27 studies were highlighted and examined in more detail, 12 from 2025 and 15 from 2024. These studies were chosen from open access journals, mainly MDPI, Elsevier, and Springer. The researchers predominantly used CNN models to solve classification and detection problems. Several studies have presented not only the analysis of cell or panel images, but also the results of condition assessments of entire power plants [68]. In such cases, IR as well as RGB images were used as samples.  
RGB samples can be refined to detect and catalog different faults, such as soiling (dust, bird droppings, etc.), partial or full shading, burnt spots, delamination, discoloration, glass ruptures, and cracks. Going further, the simultaneous use of RGB and IR cameras during aerial inspections has been proven to be an improved plant diagnostics solution. It offers a faster and easier classification of faults in comparison to using only one image type. Even though an additional RGB camera increases the cost of the UAV platform, demonstrations have shown that this cost is offset by the reduction in inspection times and workmanship costs [62].
## 2 FES for SCI
### FES for Upper Limb Paralysis  
Following the first report of FES for upper limb paralysis in quadriplegia due to cervical SCI [20], [PERSON] et al. developed the FES-based system, comprising an 8-channel fully implanted electrode, a stimulator, and an external controller, and tested the system in practical use [27]. In Japan, [PERSON] et al. developed a percutaneous implantable electrode FES system that achieved excellent control of quadriplegic upper limbs [28]. Since the 1990s, our group has been working on the reconstruction of the finger gripping function of quadriplegic upper limbs using this system, which has shown success in the reconstruction of practical movements such as eating, drinking, and writing [29].  
However, as these systems require percutaneous implantable electrode placement surgery, it is difficult to start treatment early after injury or onset. Accordingly, a new surface electrode type FES device has been developed that enables the initiation of treatment from an early stage after SCI without the need for percutaneous implantable electrode placement surgery [30].  
The NESS H200(r) Wireless system (Bioventus LLC, Durham, NC, USA) for the upper limbs is designed specifically to reconstruct finger function in paralyzed upper limbs, and consists of an orthotic component that houses a stimulation device, an internal surface electrode that attaches to the upper limb, and a control unit with a built-in FES program. The stimulation conditions can be set and transferred to the control unit by wireless operation. This device is particularly indicated for reconstruction of function in quadriplegia due to cervical SCI with residual functional levels of C5 or C6. [PERSON] et al. validated its safety and efficacy in 5 cases of C5 paralysis and 2 cases of C6 paralysis more than 3 years after SCI [30], and reported improved upper limb function and ADL according to the Fugl-Meyer Assessment [30]. We examined the therapeutic effect of NESS H200(r) from an average of 6 days after paralysis in 23 cases of incomplete limb paralysis due to cervical SCI. The average treatment period was 60 days, and upper limb movement was assessed by the Japanese Orthopedic Association Cervical Myelopathy Treatment Criteria (JOA Score). Functional items (socored out of 4 points) were significantly improved from 1.7 \(\pm\) 0.5 (mean \(\pm\) standard deviation [SD]) points at the start to 2.9 \(\pm\) 0.9 (mean \(\pm\) SD) points at the end of treatment [31]. In addition, two randomized controlled trials that compared therapy with FES alone or in combination with conventional therapy reported that FES improved voluntary grasping [24; 32; 33]. A systematic review concluded that FES alone or in combination with conventional therapy improved the function of the upper limbs in patients with SCI [33].
## 2 Research Background
### Spectrum-Based Fault Localization  
Spectrum-based fault localization uses test cases and their corresponding code coverage to evaluate each program element to determine the possibility of errors in the program. Spectrum-based fault localization was first applied in botanical studies by [PERSON] [14] in 1901. Since then, many researchers have used it in other fields (including software debugging) and further improved it. In 2017, [PERSON] et al. [15] compiled the currently used spectrum-based fault localization technology, and in 2018, [PERSON] et al. [16] selected  
Figure 1: Flowchart. From left to right: if-then-else and do-while controls.  
4 methods with the best results from the existing 18 spectrum-based fault localization methods. The ranking order of these methods was selected to be [PERSON] et al.'s [17], [PERSON] et al.'s [18], [PERSON] et al.'s [19], and [PERSON] et al.'s [20] methods. Fault localization techniques on real vs. artificial faults were compared and reported for the first time by [PERSON] et al. [21]. It was found that techniques that localized artificial faults best did not perform well on real faults. [PERSON] et al. [22] proposed spectrum-based fault localization to represent the effectiveness of the risk evaluation formula framework based on the concept that the determinant of the effectiveness of a formula is the number of statements with risk values higher than the risk value of the faulty statement. [PERSON] et al. [23] demonstrated that SBSE could be used to automatically design such a formula by recasting this as a problem for genetic programming.  
The system detects the wrong program and the program spectrum; records the path of the program in the test; blocks the statement of the program, which is relatively active when the test fails; and applies the formula to calculate each statement or program area in the program. In the error probability score for the block, the higher the score is, the higher the probability of an error is when the program is executed. Its formula usually contains:  
1. \(\mathrm{N_{f}(e)}\). The test successfully executes line e of the program, and the test result is the number of failures;
2. \(\mathrm{N_{f}(\overline{e})}\). The test does not execute line e of the program, and the test result is the number of failures;
3. \(\mathrm{N_{s}(e)}\). The test successfully executes the e-th line of the program, and the test result is the number of successes;
4. \(\mathrm{N_{s}(\overline{e})}\). The test does not execute line e of the program, and the test result is the number of successes.  
Using these factors, the formula is as follows:  
\[\mathrm{Jaccard^{\prime}s\ formula}=\frac{\mathrm{Nf(e)}}{\mathrm{Nf(e)}+ \mathrm{Nf(\overline{e})}+\mathrm{Ns(e)}}.\]
## 2 Limit of Optical Phase Compensation
To reveal this limit, based on the schematic shown in Figure 1, we designed an indoor transfer experiment of a frequency signal at 10 GHz over a 50 km fiber link with a fast fiber stretcher, and the experimental results for a free-running and compensated link are demonstrated in Figure 2. From Figure 2a, We can find that the time fluctuation for the free-running frequency transmission link is increasing continuously, and the peak-to-peak timing fluctuation within 10,000 s is about 40 ps. Figure 2b shows that the timing fluctuation for the optical compensated link is only suppressed in sections of short time, when the timing delay introduced by fiber link is in the range of the stretcher. Once it goes out of the range of the time section, the timing fluctuation exceeds the adjustable range of the stretcher. At this time, the phase compensation fails and the timing fluctuation cannot be compensated. Figure 2: Timing fluctuation results for RF transfers with and without optical phase compensation. (**a**) Free-running link without compensation. (**b**) The link with compensation.
###### Abstract  
Millimeter-wave radar is emerging as a key sensor technology not only for autonomous driving but also for various industrial applications, such as vital sign monitoring and structural displacement sensing using millimeter-wave FMCW radar, which must detect extremely small displacements on the sub-micron scale. Accurate displacement measurements fundamentally rely on obtaining precise intermediate frequency (IF) phase data over slow time (i.e., chirp-to-chirp intervals or pulse repetition time) generated by the radar sensor system. In this study, we developed a millimeter-wave FMCW radar sensor for displacement sensing using a 77-81 GHz radar transceiver MMIC (Monolithic Microwave Integrated Circuit) and evaluated its accuracy and precision through a series of experiments. First, we assessed the MMIC's phase performance under static conditions using a rigid RF waveguide, and second, we measured a vibrating target using an industrial vibration shaker as a reference. The experiments demonstrated a maximum accuracy error of +0.359 degrees (1.907 \(\upmu\)m displacement) and a maximum 3-sigma precision of \(\pm\)0.358 degrees (\(\pm\)1.180 \(\upmu\)m displacement), validating the feasibility of using millimeter-wave radar to measure very small displacements.  
displacement measuring; vibration monitoring; condition-based monitoring; CbM; FMCW radar; millimeter wave; 79 GHz +
Footnote †: journal: Applied sciences
## 4 Case Study and Verification
### Fault Diagnosis of Bearings in the Roller Table System of the Annealing Kilin
Furthermore, the confusion matrix of the bearing conditions is shown in Figure 20. Few normal and fault roller table bearings were misjudged as normal and fault through shaft bearings, respectively. Comprehensively, the transfer model can effectively identify the fault condition for the bearings in the roller system of the annealing kil. Figure 17: Time-frequency representation and their histogram equalization of roller table and through shaft bearing. (**a**) Normal condition of roller table bearing. (**b**) Fault condition of roller table bearing. (**c**) Normal condition of through shaft bearing. (**d**) Fault condition of through shaft bearing. (**e**) Normal condition of roller table bearing. (**f**) Fault condition of roller table bearing. (**g**) Normal condition of through shaft bearing. (**h**) Fault condition of through shaft bearing. Figure 18: Loss function and accuracy curve of training process. Furthermore, the collected monitoring data from the bearing system of the annealing kiln were put into CNN-LSTM, DCORAL, and DDA neural network to identify the working condition. The results in Table 4 show that identification accuracy of the proposed method for the intelligent fault transfer diagnosis of the bearing system in the annealing kiln is 99.85%, and the mean error is 0.08%. Compared to other method, the result is better.
## 1 Introduction
### Review of State-of-the-Art Detection Methods
#### 1.2.3 Super-Resolution and Subspace Methods  
To improve resolution and noise robustness, several high-resolution techniques have been investigated. The Multiple Signal Classification (MUSIC) algorithm exploits the eigenstructure of the covariance matrix to estimate signal subspaces and locate spectral peaks with sub-bin resolution [5]. Similarly, the Matrix Pencil Method (MPM) estimates the frequencies of damped sinusoids by solving a generalized eigenvalue problem using pencil matrices [6]. These techniques, while offering superior resolution, are computationally expensive and sensitive to model order selection and noise statistics.
# Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review
## 3 Search Results and Meta-Analysis
### Ensemble Methods  
The ensemble method is a meta-algorithm that combined manifold intelligent techniques into one predictive technique [33; 98]. The main goal of ensemble methods is to address the weakness of the individual models relative to stronger models [98]. Every ensemble technique follows a different purpose; for example, boosting manages to decrease bias, bagging tries to decrease variance and stacking attempts to enhance predictions [33; 98]. Several ensemble techniques were used in several studies [33; 98]. Based on the reviewed articles, random forest (RF) is the most commonly used ensemble technique in the literature [33; 98]. In particular, it outputs the median prediction for regression tasks and the mode of the classes of the single trees in classification problems. A recent study showed that RF performed better than other compared methods [64].  
Bagging, which is also called Bootstrap Aggregating (BA), generates several samples from the training instances with replacements. Based on the reviewed papers, several research studies applied bagging techniques for financial fraud detection [33]. By changing the distribution of the training dataset based on the accuracy of the predecessor, boosting aims to train weak learners serially [28]. One of the popular boosting techniques is AdaBoost, [28]. AdaboostMI is a multi-instance Adaboost that was used in [33] to repetitively execute different distributions of SVM throughout the training dataset and then combined the classifiers into a separate hybrid classifier. Stacking is another ensemble ML method that aggregates different classification or regression models [2]. Unlike bagging, which uses samples of the training data, stacking uses the entire dataset and uses models that are typically different from one another.