esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Follow-Up Outcomes  
The changes in VAS and WOMAC scores in all three treatment groups at the 3- and 12-month follow-ups are presented in Figure 2. Significant improvement was observed in all groups at 3 months in both scores; however, at 12 months, a significant improvement as compared to baseline values persisted in the VAS score in the ACS group, and in both the VAS and WOMAC scores in the BMAC group (Tables 4 and 5). Pain was significantly more persistent in older patients as older patient age was associated with a higher VAS (r = \(-\)0.21, \(p\) < 0.001) and WOMAC score (r = \(-\)0.13, \(p\) = 0.008) at the 3-month follow-up.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Treatment Group** & **HA** & **ACS** & **BMAC** & **Significance** \\ \hline  & **Mean (SD)** & **Mean (SD)** & **Mean (SD)** & **\(p\) Value** \\ \hline OA grade & 2.13 \(\pm\) 0.74 & 2.35 \(\pm\) 0.73 & 2.62 \(\pm\) 0.54 & \(\dagger\)\(p\) = 0.007, \(\ddagger\)\(p\) = 0.003, \(\ddagger\)\(p\) \(<\) 0.001 \\ VAS & 3.94 \(\pm\) 1.55 & 4.51 \(\pm\)1.32 & 5.13 \(\pm\) 1.28 & \(\dagger\)\(p\) \(<\) 0.001, \(\ddagger\)\(p\) \(<\) 0.001, \(\ddagger\)\(p\) \(<\) 0.001 \\ WOMAC & 5.82 \(\pm\) 1.92 & 6.52 \(\pm\) 1.96 & 7.92 \(\pm\) 2.24 & \(\dagger\)\(p\) = 0.002, \(\ddagger\)\(p\) \(<\) 0.001, \(\ddagger\)\(p\) \(<\) 0.001 \\ \hline HA = hyaluronic acid; ACS = autologous conditioned serum; BMAC = bone marrow aspirate concentrate; SD = standard deviation; OA = osteoarthritis; VAS = Visual Analogue Scale; WOMAC = Western Ontario and McMaster Universities Osteoarthritis Index; statistical significance of the difference between \(\dagger\) HA and ACS, \(\ddagger\) ACS and BMAC, \(\#\) HA and BMAC. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Preintervention difference in grade of osteoarthritis, VAS and WOMAC scores between treatment groups.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Correlation between OA Grade**} & \multicolumn{2}{c}{**Correlation between OA Grade**} \\  & & \& VAS & \& WOMAC \\ \hline \multirow{2}{*}{**Treatment Group**} & **Spearman Correlation** & \multirow{2}{*}{**\(p\) Value**} & **Spearman Correlation** & \multirow{2}{*}{**\(p\) Value**} \\  & **Coefficient (r)** & & & **Coefficient (r)** & \\ \hline
**HA** & 0.53 \(\ddagger\) & \(<\)0.001 & 0.67 \(\ddagger\) & \(<\)0.001 \\
**ACS** & 0.35 \(\ddagger\) & \(<\)0.001 & 0.46 \(\ddagger\) & \(<\)0.001 \\
**BMAC** & 0.39 \(\ddagger\) & \(<\)0.001 & \(-\)0.26 & 0.788 \\
**Total** & 0.47 \(\ddagger\) & \(<\)0.001 & 0.48 \(\ddagger\) & \(<\)0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correlation between grade of OA and preintervention VAS and WOMAC score, respectively.
# Long-Term CBCT Evaluation of Mandibular Third Molar Changes after Distalization in Adolescents
## 4 Discussion  
Total arch distalization of the mandible in Class I bimaxillary protrusion cases can cause posterior crowding, which, in turn, can change the positions of developing third molars in adolescents [7]. Until now, there were no studies using CBCT to analyze the positional changes of molars after the distalization of mandibular dentition. In this study, mandibular third-molar positions were evaluated in Class III patients treated with elastics and maxillary MCPPs for total arch distalization. The mandibular first molars of the treated group in our study showed 2.3 mm of distalization, with distal crown tipping of 5.2\({}^{\circ}\) and extrusion of 1.3 mm, in patients distalized using Class III elastics. [PERSON] et al. [19] demonstrated 3.0-mm distal movement with 6.2\({}^{\circ}\) distal tipping with TSADs. Furthermore, [PERSON] et al. [20] reported 3.2-mm distal movement and 4.6\({}^{\circ}\) distal tipping with a ramal plate. Our results showed less distal movement compared to the results in other reports because we used Class III elastics instead of TSADs in the mandible. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Variables**}} & \multicolumn{2}{c}{**T3**} & \multicolumn{2}{c}{**Control**} & \multirow{2}{*}{_p-Value_} \\ \cline{2-2} \cline{4-7}  & **Average** & **SD** & **Average** & **SD** & \\ \hline
6 angulation & 82.15 & 5.44 & 83.03 & 4.95 & 0.492 \\
7 angulation & 79.37 & 8.87 & 78.36 & 7.56 & 0.614 \\
8 angulation & 26.33 & 15.31 & 71.37 & 10.30 & \textless{}0.001 \\
6 rotation & 73.08 & 11.97 & 72.90 & 12.13 & 0.952 \\
7 rotation & 53.71 & 21.94 & 46.51 & 25.43 & 0.229 \\
8 rotation & 36.34 & 21.86 & 31.37 & 17.38 & 0.301 \\
6 MB cusp to frontal (mm) & 32.25 & 2.51 & 32.95 & 2.8 & 0.456 \\
6 MB cusp to horizontal (mm) & 30.03 & 2.49 & 29.65 & 3.68 & 0.639 \\
6 MB cusp to midsagittal (mm) & 26.36 & 3.86 & 25.15 & 2.06 & 0.098 \\
7 MB cusp to frontal (mm) & 42.34 & 3.15 & 42.68 & 2.99 & 0.748 \\
7 MB cusp to horizontal (mm) & 27.61 & 3.14 & 27.35 & 2.95 & 0.729 \\
7 MB cusp to midsagittal (mm) & 29.28 & 5.11 & 27.72 & 3.41 & 0.135 \\
8 MB cusp to frontal (mm) & 46.31 & 2.58 & 49.88 & 3.28 & 0.002 \\
8 MB cusp to horizontal (mm) & 19.68 & 3.92 & 22.74 & 3.84 & 0.002 \\
8 MB cusp to midsagittal (mm) & 39.02 & 4.08 & 35.07 & 4.62 & 0.001 \\ Mandibular intercanine width & 28.01 & 1.90 & 26.93 & 1.27 & 0.043 \\ Mandibular internormal width & 52.75 & 7.68 & 50.62 & 2.72 & 0.203 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the measurements among long-term retention and the control group.
## 4 The Proposed Model
### Comparison with RR-MAC and Motivations
The use of this approach helps to enhance the MAC performance in  
Figure 6: RR-MAC Markov model. terms of reduction of collisions and fairness improving, resulting in an overall decrease in the total amount of energy wasted by nodes. Unfortunately, RR-MAC is a static model and does not adapt itself to the traffic channel conditions; this could represent a limit in scenarios in which nodes are concentrated in a certain sector, as verified in the previous section. The goal of the present work is to improve the efficiency of the round-robin scheduler in terms of energy management by providing an adaptive beamforming time for each sector that takes into account the size of the waiting queue for each sector. We denoted this novel approach as adaptive beamforming time with round-robin MAC (ABT-RRMAC). To figure out the relevance of using an adaptive sector time assignment, let us consider the situation discussed in Section 3 in which two different mobile nodes traffic distribution (uniform and not uniform) in the network are compared:  
Figure 7a represents a network scenario in which mobile nodes are almost uniformly distributed among sectors of the sectorized plane; the communications are ruled by the round-robin scheduling; therefore, the current active sector is Sector 2, and the beam is then active in that sector. As noticed from the analysis accomplished in Section 3, assuming a uniform activity degree of nodes, in this case, the waiting queue time is acceptable, as is the MAC layer performance; in Figure 7b, it can be observed that the overall node distribution is unbalanced among sectors as well as the activity degree of nodes. More specifically, most of the mobile nodes are mainly distributed in Sector 1, which, as an assumption, also has the highest activity degree among sectors; supposing that the current active sector is again Sector 2, which contains a very low number of nodes (compared to Sector 1) together with a very low burden of active communications, the allocated resources in that sector may be under-used; in particular, due to the static sector time reservation assignment provided by the round-robin algorithm (the assigned sector time is the same for all sectors), the overall beamforming process becomes inefficient, resulting in a considerable decreasing of the MAC performance especially in terms of throughput and energy consumption. Indeed, in Figure 7b, it is easy to observe that, due to unbalanced disposition of nodes among sectors, the energy consumption in sectors \(S_{2},S_{3},S_{4},S_{5},S_{7},S_{8}\) (representing the 80% of the overall plane) is quite inefficient because the beam, in that sector, is performed for a time \(T_{s}\) that is much higher than the required quantum of time required for emptying the queues. It is important to highlight that, because we are analyzing the worst condition case, we assume that the scenarios illustrated in Figure 7b remain unchanged for a long time or change very slowly.
# Magnetic Resonance Roadmap in Detecting and Staging Endometiosis: Usual and Unusual Localizations
## 3 Localization
### Deep Infiltrating Endometrosis (DIE)
#### 3.2.1 Bladder and Urinary Tract  
Urinary tract endometriosis affects up to 15% of women with DIE and the bladder is the most frequently involved, especially the bladder dome, followed by the vescico-uterine pouch and the bladder base [14; 39; 40].  
Apart from pain, women with endometriosis of the urinary tract may experience the following symptoms: dysuria, urgency, frequency and, in some cases, hematuria [41].  
Transvaginal ultrasound and MRI are the methods of choice for a correct diagnosis and evaluation of disease extension; in particular, the MRI plays a predominant role in preoperative planning [40].  
The most common sign of MRI is a diffused or localized bladder wall thickening involving the muscularis layer; more rarely, lesions infiltrate the mucosa and protrude into the lumen [41].  
In order to perform a correct evaluation, the bladder must be at a degree of repletion suitable for the correct study of its walls and its lumen: those nodules or masses are responsible for the loss of the normal hypointense signal of the muscularis layer on T2 WI. Hemorrhagic content may also be seen as hyperintense foci on T1 WI, and it is also possible to observe hyperintense cystic areas on T2 WI [7; 14] (Figure 1).  
In the case of a mural mass protruding into the lumen, mucosal or submucosal edema is well depicted on T2 WI [40].  
From the perspective of a surgical approach, it is necessary to assess the distance between the lesion and the ureteral meatus. In fact, the involvement of the ureters requires a more aggressive approach, which in severe and extensive cases leads to ureteral resection-reimplantation or even nephrectomy. Ureters may appear dilated caused by either extrinsic compression or intrinsic infiltration [42].
# Research on Personalized Course Resource Recommendation Method Based on GEMRec
## 4 Experiments and Results Analysis
### Knowledge Graph Similarity Calculation Experiment
Most notably, the traditional collaborative filtering methods (user-based CF: 0.185, item-based CF: 0.201) performed significantly worse due to their heavy reliance on user interaction histories. This demonstrates that GEMRec's approach of leveraging course knowledge structure through graph edit distance and comprehensive content understanding through multimodal features is particularly effective for addressing the cold-start challenge in educational recommendations. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision@10** & **Recall@10** & **NDCG@10** \\ \hline User-based CF [28] & 0.142 & 0.156 & 0.183 \\ Item-based CF [28] & 0.158 & 0.142 & 0.201 \\ CBR [29] & 0.173 & 0.189 & 0.215 \\ DeepFM [30] & 0.231 & 0.228 & 0.242 \\ NCF [31] & 0.213 & 0.229 & 0.236 \\ KGAT [32] & 0.265 & 0.257 & 0.281 \\ KGCN [33] & 0.261 & 0.259 & 0.288 \\ CAmgr [34] & 0.264 & 0.262 & 0.293 \\ GEMRec * & 0.267 & 0.265 & 0.297 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of effects of different methods.
## 5 Results of the Experiment
Due to the influence of the fluctuation of the midpoint potential, the upper and lower capacitor voltages were 10.1 V and 9.9 V, respectively, which were basically half of the DC voltage, and the fluctuation amplitude was small. From this, it can be inferred that the control strategy was able to effectively manage the deviation of the midpoint potential. According to the waveform diagrams of the phase A and phase B line voltages measured in the experiment, as shown in Figure 16a, it can be observed that the line voltage waveform was a five-level staircase wave with a voltage amplitude of 20 V, which was equal to the output DC voltage \(U_{\text{dc2}}\). Shown in Figure 16b is a waveform diagram of the output phase voltages. It can be observed that the phase voltage waveform was a nine-level staircase wave with an amplitude of 13.3 V. The experimental and simulated waveforms matched closely, which also validated the effectiveness of the dual closed-loop control SVPWM modulation algorithm. Compared to the two-level inverter, it exhibited a significantly sinusoidal characteristic, with the five-level and nine-level staircase waves being closer to sinusoidal waveforms. This fully demonstrates the advantages of the three-level inverter, confirming the correctness of the theory. The output DC voltage \(U_{\text{dc2}}\) was input into the NPC three-level inverter circuit. In the field of coal mining, underground equipment's motor frequency regulation range is mostly 40-60 Hz. By adjusting the frequency magnitude of the NPC three-level inverter development board, the final choice was to output three different frequencies (\(f_{1}\)-\(f_{3}\)) of the three-phase output phase voltages. As shown in Figure 17a-c, the three-phase output phase voltages were symmetrical in phase and had good sinusoidality. So, the experimental results were in accordance with the simulation and expectations. Figure 16: (a) Output line voltage. (b) Output phase voltage. Figure 17: Three-phase inverter output phase voltage waveforms: (a) \(f1=40\) Hz; (b) \(f2=50\) Hz; (c) \(f3=60\) Hz. (Notes: The measurement data in the right column were defined as the “RMS value, frequency, and maximum level” of the three-phase output voltage. At frequencies of 40 Hz, 50 Hz, and 60 Hz, the RMS values of the three-phase AC voltage were approximately between 7.81 V and 7.97 V, while the maximum level was about 14.1 V.)
## 1 Introduction
### Overview and Contribution  
In this paper, we focus on mitigating pipeline conflicts at a high operating frequency in a layered decoding schedule. This is achieved by the following contributions:  
(1) Double update queues replace the single update queue in the layered LDPC decoder. In comparison with [15], the percentage of up-to-date LLR read operations per iteration with double update queues during the decoding was increased by up to 31%.  
(2) The priority-based layered decoding schedule is proposed to update LLRs as frequent as possible when the pipeline conflicts cannot be avoided. Due to a higher percentage of updated LLRs and more frequent update of LLRs, the priority-based decoder with double update queues lower performance loss from 0.6 dB to 0.2 dB compared with [15]. Comparing with residue-based decoder [14] using double update queues, the proposed decoder shows an advantage of 0.1 dB.  
(3) As a direct result of the proposed layered LDPC decoder, the throughput of our proposed decoder for 5G NR is up to 2.85 times that of [15] with the same quantization on the Xilinx VC709 evaluation board at the same signal-to-noise ratio (SNR).  
The remaining sections are organized as follows. In Section 2, the layered decoding schedule is introduced. In Section 3, the proposed priority-based layered decoding schedule and double update queues are described. The structure of the proposed priority-based layered decoder with double update queues and the flow chart of the proposed decoder are also described. In Section 4, the results of simulation and hardware implementation are presented. Finally, Section 5 provides a conclusion to this paper.
## 2 Methodology  
### Branch Pipe Detection FMC-TFM Detection Model  
FMC is the basis of TFM images. The full matrix data acquisition model is shown in Figure 1. The ultrasonic transducer with the number of array elements of \(N\) excites a single array element of the transducer, which, in turn, collects and stores the echo data of \(n\) array elements and finally obtains the matrix data \(S_{ij}(t)\) of \(N^{*}N\), where \(1\leq i\leq N\), \(1\leq j\leq N\).  
TFM focuses the full matrix data to each point of the detection image and obtains the image by using the amplitude number of ultrasonic echo. The inclined wedge is used as the intermediary for branch pipe detection, and the principle diagram of the full-focus imaging algorithm is shown in Figure 2.  
Figure 1: Working principle of FMC.  
Figure 2: TFM imaging model of branch pipe weld.  
The wedge is in close contact with the branch pipe through the coupling agent. In this paper, the projection point of array element 1 on the surface of the sample is taken as the origin o, the transverse axis \(X\) is along the axis of the main pipe, and the longitudinal axis \(Z\) is along the axis of the branch pipe. The detection area is divided into a certain number of pixels, as shown by the dots in Figure 2. These pixel points are the focus of the acoustic beam. Take point \((x,\,z)\) as an example; to obtain the ultrasonic echo amplitude value of this point, all signals of the full matrix data set are superimposed at this point, and the point amplitude \(S\left(x,\,z\right)\) is shown in Formula (1).  
\[S(x,z)=\sum_{i=1}^{N}\sum_{j=1}^{N}S_{i,j}\left(t_{i,j}(x,z)\right) \tag{1}\]  
In Formula (1), \(S_{i,j}\big{(}t_{i,j}(x,z)\big{)}\) is the echo amplitude of the point \((x,\,z)\) transmitted by the \(i\)-array element and received by the \(j\)-array element, where \(t_{i,j}(x,z)\) represents the delay time of the sound wave from the \(i\)-array element to the \(j\)-array element. In combination with the branch pipe detection model, \(t_{i,j}(x,z)\) is represented by Formula (2).  
\[t_{i,j}(x,z)=\frac{\sqrt{(x_{i}-x)^{2}+z^{2}}+\sqrt{\left(x_{j}-x\right)^{2}+z ^{2}}}{c_{s}}+T_{w} \tag{2}\]  
In Formula (2), \(T_{w}\) is the delay time of the sound wave in the wedge during the transmission of the \(i\) array element and the reception of the \(j\) array element, and \(c_{s}\) is the wave velocity of the sample. Combined with the branch pipe detection model, \(T_{w}\) is specifically expressed in Formula (3).  
\[T_{w}=\frac{\sqrt{\left(x_{i}-x_{i0}\right)^{2}+z_{i}^{2}}+\sqrt{\left(x_{j}-x_ {j0}\right)^{2}+z_{j}^{2}}}{c_{w}} \tag{3}\]  
In Formula (3), \(c_{w}\) is the wedge wave velocity.
## 3 Results and Discussion
### pH  
The application of gelatin in the product is affected by pH shown in Figure 6. Gelatin with neutral pH is applied to meat products, pharmaceuticals, chromatography, and paints. Meanwhile, gelatin with a low pH value is used for the food industry, and high pH gelatin is used for the pharmaceutical industry. The pH value will depend on the solvent used during the demineralization process [23]. In this study, the solvent used was acid and alkaline. The washing process played an important role in neutralizing the pH of the gelatin. For this study, washing was carried out with plain water, and this method was less effective, so it should be washed using acid so that the pH of alkaline gelatin could become more neutral. The optimum pH value in this study was the lowest pH of 10.49 in the immersion treatment with a 5% NaHCO\({}_{2}\) extraction time of 6 h. For immersion with HCl, it has a relatively acidic pH. This pH condition will affect the isoelectric point produced. Based on research conducted by [PERSON] that the soaking process in acidic conditions will produce more gelatin [24]. At very low pH, a deprotonation process occurs and inhibits the process of forming hydrogen bonds [25]. From an economic point of view, the use of acidic solvents is cheaper [26]. For 100 mL, the price of 1 N HCl is lower than CH\({}_{3}\)COOH and H\({}_{2}\)SO\({}_{4}\).
# Combining InSAR and GNSS to Track Magma Transport at Bastic Volcano
## 6 Discussion  
### Discrepancies between Independent Datasets Reveal Hidden Processes  
#### 6.1.1 Discrepancies in the Covered Time Periods Reveal Pre-Eruptive Displacement  
The recent increase in the frequency of SAR acquisition now makes it necessary to evaluate the selection of interferograms that span an eruptive event. Because of the difference in time scale and amplitude between pre-eruptive and co-eruptive deformation, the whole deformation pattern is generally assumed to reflect co-eruptive processes. An important question is whether it is more relevant to select interferograms that cover the same time periods or interferograms that are as close as possible to the eruption onset. In the present study, interferograms (CSK A, CSK D, S1 D and S1 A) that cover months before the eruption and captured the pre-eruptive period were available along with an interferogram (S1 A2) that only covered the few hours before the eruption. A systematically better fit on S1 A than S1 A2 reveals an inconsistency between S1 A2 and the other interferograms. During the 10 days preceding the eruption, a very slight inflation was noticed on the continuous summit GNSS stations (Figure 2). However, GNSS displacement being less than 1 cm, no fringes in interferogram S1 A1 can be conclusively associated with this inflation. Yet, the less good fit of S1 A2 than S1 A could come from the best model preferentially fitting the majority of the data, that is, the three interferograms  
Figure 12: Location of the overpressure source determined from the temporal inversion (colors) for 14 time steps out of the 29 time steps inverted between 20:40 on 25 May and 04:00 the next day. The pressurized area is assumed to be a circular disk projected on the a priori mesh (in gray) of family F1 (**A**) and family F2; (**B**) or a subgraph of the a priori mesh of family F2; (**C**). Source geometries for F1 and F2 are non unique solutions determined from the inversion of the four sets of InSAR data that cover the whole eruption.  
that recorded the pre-eruptive deformation and fitting the fourth interferogram, which which started after this deformation, less well. Therefore, the best model should be considered with caution as it might in reality represent a compromise between several displacement sources. Here we performed inversions with data from one time period and others incorporating data that covered a different time period to the rest of the data, showing that the latter approach can be an asset and may be used to highlight processes which are otherwise too subtle to appear clearly in the longer time-period data.