esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Performance Evaluation
### Performance Comparison of Different \(m\) and \(n\)  
There are two parameters, \(m\) and \(n\), on the \(LMMSE\) predictor in the previous section, should be tuned. In related work [18], experiments have been conducted to investigate the total prediction errors by varying the value of \(m\) and \(n\). The result is shown in Table 3, where \(n\) is set to be 10. Results under other settings and combinations of parameters give similar trends. Hence, we can find that the result in the Table 3 provides us with the basis for setting parameters of \(m\) and \(n\). We suppose that the different setting of \(m\) and \(n\) may have an effect on the performance of the _ASMSS_. In this section, we will conduct experiments to study the impact of different \(m\) and \(n\) on the performance of the _ASMSS_. In order to reduce complexity, we set \(m\) to be 6, and compare the performance of the _ASMSS_ by setting the different value of \(n\) when \(D\) is set to be 50%. The result is shown in Table 4. From Table 4, we find that the delivery ratio, delivery overhead and delivery delay are different but not very different when \(n\) takes different values. When \(n\) is 6, the corresponding delivery rate is larger. Hence, we set \(m\) to be 6 and \(n\) to be 6 in our experiments. Note that we just choose \(m\) less than 10 to do experiments for the performance comparison. That is because when the value of \(m\) and \(n\) is greater, the calculation complexity is relatively larger. We do not have to pay a huge calculation complexity for a small performance improvement.  
Figure 8: The comparison of delivery ratio (**a**), delivery cost (**b**) and delivery delay (**c**) based on the epidemic routing protocol in _Infocom6_ trace with the different T.  
Figure 7: The comparison of delivery ratio (**a**), delivery cost (**b**) and delivery delay (**c**) based on the epidemic routing protocol in _Infocom6_ trace.
## 2 Methods
### Experimental Approach to the Problem  
A cross-sectional protocol was employed to approach the problem of the study. The VO\({}_{\text{2 max}}\) was measured in a lab, whereas running and cardiorespiratory parameters were measured during five soccer matches. Of the 12 participants, 6 were soccer players, while 6 were nonathletes. The nonathletes did not have to participate systematically (<2 times/week) in sports activities. All of them performed anthropometric and VO\({}_{\text{2 max}}\) measurements. The players participated in five soccer matches where physiological indicators (HR) and match running distance (covered distance (m) and velocity (km/h)) were measured.  
One week before the beginning of the study, the participants familiarized themselves with the tests. Testing was incorporated within the laboratory. Soccer matches were performed on the team's field. More specifically, the anthropometric and VO\({}_{\text{2 max}}\) measurements were completed during the first visit to the laboratory after familiarization. At the beginning of the VO\({}_{\text{2 max}}\) measurement, all participants performed a 10 min warm-up and stretching, followed by a 10 min cool-down period. VO\({}_{\text{2 max}}\) measurements were performed under the same conditions in the laboratory. All participants avoided any strenuous activity the day before the measurements.  
In the field, before each soccer match, the soccer players performed a standardized 25 min warm-up consisting of 5 min submaximal running, 5 min of stretching exercises, ball-handling exercises for 5 min, three repetitions of 15 m run-outs at approximately 90% of maximal speed and 4 min of active recovery.
## 2 Materials and Methods
### Flowchart Representation of the Proposed ACNCL  
The flowchart in Figure 2 illustrates a structured approach to denoising and tumor detection in MRI and CT images using a Generative Adversarial Network (GAN)-based model integrated with Content and Noise Complementary Learning (CNCL). It starts with loading noisy MRI/CT images, followed by identifying noise types using ACNCL-Part 1, which classifies noise for appropriate processing. The GAN Generator then removes noise while reconstructing useful image data, and the GAN Discriminator evaluates the effectiveness of this denoising process. The denoised images are further enhanced using CNN-based methods, including U-Net, DnCNN, DenseNet, DWT, and CA-AGF, to improve structural clarity. The ACNCL-Part 2 predictor detects potential tumor regions in the processed images, after which PatchGAN ensures the structural preservation of tumor features. The refined images are compared with external medical databases to confirm tumor regions, and the final output presents tumor-marked images with diagnostic insights for medical evaluation.
## 3 Research Method
### Environment Model  
This research addresses an environment (farm) model consisting of three keys: sensory points (targets), UAV, and base station. Sensory Points (SPs) are target locations in the farm that should be visited by UAVs for remote sensing. They contain interesting agricultural data such as soil PH, crop growth and/or animal nesting, which should be collected and reported by using UAVs. SPs can be either static or mobile. The former is a particular fixed location in the farm (e.g., sensing plant/crop growth), whereas the latter moves throughout the field (e.g., monitoring the behavior of animal herds).  
UAV moves over the farm to visit SPs and remotely collect/aggregate agricultural data. This is highly power constrained, and equipped with on-board cameras and/or sensors to capture data samples and a GPS to find the location information. To reduce power consumption, the UAV avoids continuously transmit taken images and/or collected data via wireless/internet links to the user's access point/server. However, it utilizes a lightweight aggregation function to combine collected data samples and report the aggregated results when it returns home.  
The base station is the end-user access point that manages remote sensing missions. This factor usually has no power or communication constraints. Base station forwards the UAV into the farm for remote sensing journey, and collects sensory recordings reported by the UAV when it returns to deliver the collected data and/or re-charge the battery.  
As Figure 1 shows, the farm is partitioned as a grid according to the acquired location information from GPS. Each partition is labeled as no-fly, safe-fly, or SP zones according to the application. SP zones are the target areas to be visited by the UAV for remote sensing. Safe-fly zones have no data for the UAV to collect. However, a safe-fly zone might become an SP zone if a mobile SP (e.g., animal herds) moves into it. The UAV should avoid flying over no-fly zones, whereas it should minimize the flight time over safe-fly zones.The partitions are discovered, and the labels are updated during the environment exploration phase.
## 1 Introduction
Choosing an optimal single band or band pair through methods such as principle component analysis (PCA) [16], analysis of variance (ANOVA) [17], correlation analysis [18], and beta coefficient of partial least squares regression analyses [19] is well established for a detecting differences within and among samples. In addition, sequential forward selection (SFS) is the preferred method for finding an optimal combination of wavelengths since it chooses a subset of wavelengths without losing or deforming the data [20]. For example, [PERSON] et al. (2016) [21], used SFS methods as one feature selection method for reducing the dimension of hyperspectral imaging data. This study developed a model with machine learning methods for detecting chilling injury in cucumber. In another study, [PERSON] et al. (2014) [22], conducted feature-selecting methods including SFS to develop a model for detecting mechanically damaged mango. Choosing an efficient classifier is essential to effectively distinguish diseased rice from sound rice. This research conducted two classifier models. The support vector machine (SVM), discriminant analysis, and linear and quadratic discriminant analysis (LDA and QDA) methods have been widely used in agricultural applications and many other fields such as optical character recognition and object recognition due to generalization capability and effective performance with linear and nonlinear data [23]. SVM methods were successfully used for assessment of corn seed viability [24], strawberry ripeness [23] and detection of chilling injury in cucumber [21]. In addition to the SVM methods, because of the effectiveness of discriminant analysis, many studies have used these methods for classification and pattern recognition. For example, the moisture and lipid contents of individual green coffee beans were predicted by using LDA [25]. LDA was used as one of the machine learning algorithms investigated to discriminate lamb muscle [26]. Classification for fungal infected date fruits was conducted by using QDA and LDA [27]. In many studies, hyperspectral imaging has been used to detect early invisible disease symptoms so that pesticide control can be applied to suppress/prevent infection. The objective of this study was to develop a rapid and inexpensive means of discerning the difference between diseased versus non-diseased seeds, not rates (or incidence) of disease. As this is an emerging rice disease, efficient and objective methods for quantifying incidence of the disease have not yet been developed. In addition, this research aimed to provide optimal wavelength information for development of an effective optical system and a robust classification model for detecting diseased seed rice.
## Abbreviations  
ALOSAdvanced Land Observation Satellite DEMDigital Elevation Model ERTElectrical Resistivity Tomography GISGeographic Information System GNSSGlobal Navigation Satellite Systems GPRGround Penetrating Radar InSARInterferometric Synthetic Aperture Radar MISMarine Oxygen Isotope Stage RIResistivity Imaging
# Wireless Caching: Making Radio Access Networks More than Bit-Pipelines
## 6 Recommendation: Making RANs More Proactive
### Joint Caching and Recommendation  
Recommendation systems (RSs), long recognized as an area of computer science, have been widely used by content providers such as YouTube, Netflix, Tik-Tok, etc. News websites, and even search engines also \"recommend\" what may interest a user. Nowadays a large portion of data services are driven by RSs. Both RS and caching predict what a user is likely to be interested in, as noted in [4]. A RS aims to a user her or his favorite content items, while a cache-empowered RAN steps further by sending them to the user before being requested. Naturally, joint caching and recommendation (JCR) has attracted some recent attention.  
Cache-friendly recommendation is a recent attempt in this area. Its intuitive idea is to push what an RS would recommend and let the user know. By this means, the hit ratio can be improved, as are caching gains. Meanwhile, the RS only recommends what it essentially wants to recommend and avoids showing a user what it is not interested in. In practice, however, the cached files may not be a user's most favorite ones. In this case, cache-friendly recommendation was conceived to recommend content items that are cached but not the most favorable [41]. In addition, recommendation may enhance users' common interests, thereby grouping them to achieve coded-caching or multicasting gain [42]. In both cases, a RAN can enjoy reduced peak-rate and improved SE and/or EE. A cost to be paid is that the user might be less satisfied with the RS if it frequently finds unwanted or useless recommendations. How undesired recommendations harm a user's experience needs more experimental study.
# Variable Selection Using Adaptive Band Clustering and Physarum Network
## 2 Theory
### Variable Selection Based on AP-PN  
AP-PN is a sequential combination of AP and PN. It uses AP to automatically divide the whole spectrum into many sub-spectral bands that are not necessarily equal to each other and then selects one wavelength from each sub-spectral band by using PN. In AP-PN variable selection, a spectral response at a wavelength is regarded as a data point, a sub-spectral band is regarded as a cluster of data points. The input \(S(i,k)\) required by AP in this paper is the sample correlation coefficient [24] between wavelength \(i\) and wavelength \(k\), which indicates to what extent the spectral information of the two wavelengths are correlated. It is calculated by using Formula (7)  
\[S(i,k)=\frac{\sum\limits_{j=1}^{N}\left(x_{ij}-\overline{x}_{i} \right)\left(x_{kj}-\overline{x}_{k}\right)}{\sqrt{\sum\limits_{j=1}^{N}\left( x_{ij}-\overline{x}_{i}\right)^{2}}} \tag{7}\]  
where \(x_{ij}\) and \(x_{kj}\) represent the spectral response of the \(j\)-th sample at wavelength \(i\) and \(k\), respectively, \(\overline{x}_{i}\) and \(\overline{x}_{k}\) are the average spectral response of all samples at wavelength \(i\) and \(k\), respectively, and \(N\) is the number of samples.  
The preference \(p_{k}\) is the input for controlling the number of clusters [20]. It is determined by maximizing the correlation function \(J(p_{k})\) (shown in Formula (8)) or finding the point where the increasing trend of \(J(p_{k})\) vanishes.  
\[J(p_{k})=\sum\limits_{n=1}^{L}\left(\frac{1}{M^{2}}\sum\limits_{i=1}^{M}\sum \limits_{j=1}^{M}S(c_{i},c_{j})\right)\frac{M}{m} \tag{8}\]  
where \(L\) is the total number of clusters, \(c_{i}\) and \(c_{j}\) represent the bands in the \(n\)-th cluster, \(M\) is the number of wavelengths in the \(n\)-th cluster, and \(m\) is the total number of wavelengths.  
The steps of using AP-PN for variable selection are as follows:  
1. Calculate the similarity matrix \(S\) according to the Formula (7);
2. Set \(p_{k}\) as a value between 0 to 1, staring from 0.00001 and updating by using \(p_{k}=p_{k}+0.000001\);
3. Set \(r(i,k)=0\), \(a(i,k)=0\);
4. Calculate the responsibility information and availability information, and update them according to the Formulas(1)-(6);
5. Determine the clusters, and go to step 2 until all \(p_{k}\) are tried;
6. Determine the \(p_{k}\) according to Formula (8) and the corresponding clusters and exemplars;
7. Bring the calculated clustering results into the PN for variable selection.
## 2 Results and Discussion
### Optimization of HS-SPME Conditions
#### 2.2.2 HS-SPME Extraction Time and Temperature  
Optimal values for SPME extraction temperature and time were evaluated concurrently in a multifactor CCF model, and results are shown for both parameters in Figure 4. SPME extraction time had a significant effect for all three thiols (\(p<0.001\)), with the longest extraction time of 60 min decreasing detection limits by two to three orders of magnitude. Extraction temperature also had a significant effect on detection limits for the three thiols (\(p\leq 0.1\) for 4-MMP and 3-MHA; \(p<0.0001\) for 3-MH). The extraction temperature chosen for the optimized method was 70 \({}^{\circ}\)C.  
Figure 4: Effect of extraction temperature and extraction time on detection limits for thiols. The optimization was performed using a multifactorial CCF model. The red dotted lines show the mean values within a factor (**vertical lines**) or for a thiol detection limit (**horizontal lines**). The blue dotted lines represent the error bars.  
Figure 3: Detection limits for thiols obtained for dry vials or for vials reconstituted with 5 or 10 mL of aqueous buffer. Error bars represent \(\pm\) 1 standard deviation for two replicates.
## 3 Application Results
### ARCH Model  
The ARCH-type models are nonlinear models that provide a platform for emphasizing the second-order momentum (variance) and the stochastic element of hydrological variables (in this research of streamflow). As part of ARCH development, the distribution used in this study was Gaussian white noise that the equation predicts the next month's streamflow in principle of this month's streamflow. With the purpose of estimating ARCH parameters, the log-likelihood method was used. The ARCH model is implemented by JMulti software. Using observed data and their variance, ARCH time-series for Dizaj and Tapik stations were calculated using a Gaussian method. Table 5 shows the calculated models with their loglikelihood value. Since the Akaike Information Criterion (AIC) was unutilized for defining the lengths of lags.
# QoS-Aware Flexible Handover Management in Software-Defined Mobile Networks
## 2 Related Works
From the network operators' view, a satisfactory level of QoS is important because it is one of the important components of the service level agreement (SLA), which is an official agreement between a service provider and client or between service providers [39]. In 5G, SLA has been focused again because of various types of applications and service requirements [40; 41]. This means that the proposed scheme can be an efficient methodology for aiding network operators in efficiently operating the limited network resources for handovers as well as for satisfying the QoS level, which is an important aspect of the SLA. In the proposed scheme, flows are classified into four classes based on the QoS requirements. Then, the different service flow handovers according to class are provided by the SDN controller collaborating with the forwarding nodes without requiring any modification on the user device as a network-based mobility management protocol.
## 3 Results
### Parameters of Proposed Models
Figure 2: Visual comparison results of GAN models for the Kazakh word: (**a**) real data; (**b**) TextStyle Brush; (**c**) GanWriting; (**d**) Scrabble Gan. WER and CER on real data showed 25.65% and 11.15%, respectively. As can be seen from Table 2, the results of the trained model with the generated data show a significant improvement in WER and CER of 10.44% and 3.02%, respectively. Experiment 5 In this experiment, we tested the accuracy of the model on real data after training it on the generated data. Below are graphs of the values of the loss function and the CER (character accuracy rate) metric during the training process (Figure 4). The CAR (Character Accuracy Rate) metric was calculated for each type of symbol in order to visually see which symbols the model classifies well and which ones have difficulties (Figure 5). The average CAR was 82.13%. Looking at specific examples, you can make sure that the model copes even with hard-to-read handwriting options (Figure 6). On the test dataset, the model achieved results of 17.11% CER. The obtained results are sufficient to use the models in projects that do not require absolute accuracy. Additionally, it is worth remembering that applying post-processing techniques to the strings received from the neural network can increase the accuracy by another couple of percent (mostly when working with dictionaries, since many words are more or less known and the output of the sequence of letters is rarely random). We were not able to objectively compare the accuracy of the model with other works due to the lack of work on handwriting recognition, specifically in the Kazakh language. Because of this, we compared them with works for Cyrillic handwritten Russian recognition, as they are similar with the exception of some characters (o, n, i, r, Y, Y, h, e). According to the experimental result, our model outperforms other approaches with 17.11% CER in the test set of our dataset (Table 3). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **WER (\%)** & **CER (\%)** \\ \hline real 45 k & 25.65 & 11.15 \\ \hline real + generated 59 k & 23.21 & 9.24 \\ \hline real + generated 67 k & 17.34 & 9.01 \\ \hline real + generated 89 k & 15.21 & 8.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: WER and CER results of the model for the different combinations of datasets. Figure 3: Transformer-based architecture of the OCR recognition model for Kazakh language. Figure 4: Results of model errors at different training epochs. Figure 5: CAR metric for each Cyrillic character in Kazakh language. Figure 6: (**a,c**) Real handwritten sentences in Kazakh language; (**b,d**) recognition results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Accuracy (\%)** & **CER (\%)** \\ \hline ([PERSON] et al., 2021) [12] & 75.88 & 21.54 \\ \hline ([PERSON] et al., 2019) [36] & 73.32 & 27.96 \\ \hline Our proposed method & 78.17 & 17.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative results.
###### Abstract  
This study investigates the deformation characteristics and base stability of a circular diaphragm wall support system (external diameter: 90 m, wall thickness: 1.5 m) with pit bottom reinforcement for the South Anchorage deep foundation pit of the Zhangjinggao Yangtze River Bridge, which uses layered and partitioned top-down excavation combined with lining construction. Through field monitoring (deep horizontal displacement of the diaphragm wall, vertical displacement at the wall top, and earth pressure) and numerical simulations (PLAXIS Strength Reduction Method), we systematically analyzed the deformation evolution and failure mechanisms during construction. The results indicate the following: (1) Under the synergistic effect of the circular diaphragm wall, lining, and pit bottom reinforcement, the maximum horizontal displacement at the wall top was less than 30 mm and the vertical displacement was 0.04%H, both significantly below code-specified thresholds, verifying the effectiveness of the support system and pit bottom reinforcement. (2) Earth pressure exhibited a \"decrease-then-increase\" trend during the excavation proceeds. High-pressure jet grouting pile reinforcement at the pit base significantly enhanced basal constraints, leading to earth pressure below the Rankine active limit during intermediate stages and converging toward theoretical values as deformation progressed. (3) Without reinforcement, hydraulic uplift failure manifested as sand layer suspension and soil shear. After reinforcement, failure modes shifted to basal uplift and wall-external soil sliding, demonstrating that high-pressure jet grouting pile reinforcement had positive contribution basal heave stability by improving soil shear strength. (4) Improved stability verification methods for anti-heave and anti-hydraulic-uplift were proposed, incorporating soil shear strength contributions to overcome the underestimation of reinforcement effects in traditional pressure equilibrium and Terzaghi bearing capacity models. This study provides theoretical and practical references for similar deep foundation pit projects and offers systematic solutions for the safety design and deformation characteristics of circular diaphragm walls with pit bottom reinforcement.  
circular diaphragm wall; deep foundation pit; deformation characteristics; high-pressure jet grouting piles; anti-heave stability; anti-hydraulic-uplift stability