esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Study Design  
We used a retrospective study design. APS therapy was performed from December 2019 to November 2022 in patients diagnosed with knee OA at our clinic or another clinic. Of the 39 patients (13 male individuals, 16 knees; 26 female individuals, 35 knees) who completed the Knee Injury and Osteoarthritis Outcome Score (KOOS) questionnaire at 3 and 6 months, 24 with KL4 (totaling 33 knees) were included. The KL classification was used to determine the OA severity of the knee. The mean age of patients was 71.0 (52-89) years.  
After the APS injection, the A + E group that underwent ESWT after APS therapy had 26 knees, with 1, 5, and 20 knees scored as KL2, KL3, and KL4, respectively. Those who did not do ESWT after APS therapy (A-alone group) had 25 knees, with 6, 6, and 13 knees scored as KL2, KL3, and KL4, respectively. In the revision, KL2 and KL3 were excluded and only KL4 was included, which is more severe. As a result, the A + E group had 20 knees and the A-alone group had 13 knees. In the A + E group, an average of 4.5 ESWTs were performed post-APS therapy (Table 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **APS Only** & **APS + ESWT** & **All** \\ \hline n & 13 & 20 & 33 \\ Age & 74.0 \(\pm\) 6.1 & 69.6 \(\pm\) 9.4 & 71.0 \(\pm\) 8.1 \\ Male, n & 3 & 7 & 10 \\ Female, n & 10 & 13 & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material. Inclusion criteria: The main inclusion criteria were being aged 50–90 years; KL grade 4 medial KOA diagnosis on Rosenberg view radiographs; no improvement with conservative treatment covered by insurance for at least 2 or 3 months (e.g., physical therapy, oral analgesics, and hyaluronic acid intra-articular injection); and MRI findings of BML, synovitis, and intra-articular effusion. All patients agreed to pay for medical treatment not covered by public health insurance. Exclusion criteria: Patients with rheumatoid arthritis or arthritis following other inflammatory or metabolic diseases; history of cancer treatment, mental illness, other regenerative medical treatment (PRP and mesenchymal stem cell injection, among others); outer type or PF type OA; KL1–3 KOA; history of in vitro shock wave treatment before receiving APS; and history of intra-joint injection of HA after APS.  
All patients underwent an ESWT session before the APS infusion and tried to continue ESWT once every 2 weeks for the first three times after APS therapy and once every 4-8 weeks thereafter. However, there were cases when patients did not attend the clinic for social reasons. The group that underwent ESWT after APS therapy (A + E group) included 20 knees, averaging 4.5 ESWTs. The group that did not undergo ESWT after APS therapy (group A-alone) had 13 knees. There was no significant difference in age or number of cases between the two groups. This study adhered to the tenets of the Declaration of Helsinki. Written informed consent was obtained from all patients.
## 4 Experiments
### Comparison with the State-of-the-Art Methods
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Features** & **X-Sub** & **X-Setup** \\ \hline MST-GCN [38] & Joint & 82.8 & 84.5 \\ Skeletal [8] & Joint & 83.5 & 85.7 \\ DualHead-Net [21] & Joint & 84.6 & 85.9 \\ TransSkeleton [22] & Joint & 84.9 & 86.3 \\ HDOCN [43] & Joint & 85.7 & 87.3 \\ FR Head [39] & Joint & 85.5 & 87.3 \\ BlockGCN [10] & Joint & 86.9 & 88.2 \\ Ours (CTR-GC [9]) & Joint & 86.2 & **88.7** \\ Ours (Block-GC [10]) & Joint & **86.7** & 88.6 \\ Shift-GCN [14] & Fusion-4s & 85.9 & 87.6 \\ Dynamic GCN [5] & Fusion-4s & 87.3 & 88.6 \\ MST-GCN [38] & Fusion-4s & 87.5 & 88.8 \\ Skeletal [8] & Fusion-4s & 87.5 & 89.2 \\ CTR-GCN [9] & Fusion-4s & 88.9 & 90.6 \\ DualHead-Net [21] & Fusion-4s & 88.2 & 89.3 \\ Ta-CNN+ [41] & Fusion-4s & 85.7 & 87.3 \\ MCTM-Net [42] & Fusion-4s & 89.3 & 91.0 \\ FR Head [39] & Fusion-4s & 89.5 & 87.3 \\ BlockGCN [10] & Fusion-4s & 90.3 & 91.5 \\ Shift-GCN [14] & Fusion-4s & 85.9 & 87.6 \\ Ours (CTR-GC [9]) & Fusion-4s & 89.1 & 90.9 \\ Ours (Block-GC [10]) & Fusion-4s & **90.4** & **91.7** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with state-of-the-art for action recognition (accuracy, %) on the NTU RGB+D 120 dataset. The best results for each feature setting and split are indicated in bold. temporal graph nodes at the early stage of the entire data flow, while other methods focus on either spatial graph representation or the design of GNNs. Comparison results with methods that process frame-level representations, such as DualHead-Net [21], show the substantial competitiveness of our proposed MSKFS method.
# Integrating BIM and GIS for an Existing Infrastructure
## 2 Current State of BIM and GIS
### 2.2. Gis
#### 2.2.1. Regulations and Standardization of GIS  
Regulations surrounding the use of GIS depend on the legal framework in a given country. They are essentially based on the management and privacy of spatial data. At the European level, the INSPIRE Directive (Infrastructure for Spatial Information in Europe) establishes a framework for the exchange of geospatial data between EU member countries [47]. This directive encourages the establishment and maintenance of thematic datasets, defining their metadata (detailed information on their content, quality, and conditions of use) and facilitating their open access [48].  
At the international level, ISO has published some standards related to geospatial data and technologies that are relevant to the use and implementation of GIS in its ISO/TC 211 committee [49]. Some of these standards are ISO 19107 [50] for geographic data models, ISO 19111 [51] for the specification of coordinate representation and geospatial reference systems, ISO 19115 [52] for the specification of metadata, and ISO 19125 [53] for the encoding of geographic entities. Therefore, there are many public administrations and other organisations that are working together towards the collaboration and interoperability of data [3]. Many of these organisations are partners in the Open Geospatial Consortium (OGC). This is an international non-profit organisation committed to creating open and interoperable standards for the global geospatial community within the framework of GIS and the World Wide Web.
# Real-Time Water Level Monitoring Based on GNSS Dual-Antenna Attitude Measurement
## 2 Methodology
### Baseline Vector Calculation  
GNSS provides various observables including pseudorange, carrier phase and Doppler [37; 38]. Pseudorange and carrier phase are commonly used in GNSS attitude measurement systems. Equations (2) and (3) represent the pseudorange and carrier phase observation equations, respectively. \[\mathrm{P_{m}^{j}}=\mathrm{\rho_{m}^{j}}+\mathrm{c}\cdot\left(\delta\mathrm{t _{u,m}}-\delta\mathrm{t}^{j}\right)+\mathrm{T_{m}}+\mathrm{I_{m}}+\varepsilon \mathrm{r_{m}} \tag{2}\]  
\[\mathrm{\phi_{m}^{j}}=\lambda\cdot\mathrm{\varphi_{m}^{j}}=\mathrm{\rho_{m}^{j }}+\mathrm{c}\cdot\left(\delta\mathrm{t_{u,m}}-\delta\mathrm{t}^{j}\right)+ \mathrm{T_{m}}-\mathrm{I_{m}}+\lambda\cdot\mathrm{N_{m}}+\varepsilon_{\mathrm{ L_{m}}} \tag{3}\]  
where, \(\mathrm{P_{m}^{j}}\) is the pseudorange observation between antenna m and satellite j, \(\mathrm{\phi_{m}^{j}}\) is the carrier phase observation between antenna m and satellite j, \(\mathrm{\phi_{m}^{j}}\) is the geometric distance between antennas m and satellite j, \(\mathrm{\delta_{u,m}}\) is the receiver clock error, \(\mathrm{\delta t^{j}}\) is the satellite clock error, \(\mathrm{T_{m}}\) is the tropospheric delay, \(\mathrm{I_{m}}\) is the ionospheric delay, \(\mathrm{N_{m}}\) is the carrier-phase integer ambiguity, \(\mathrm{c}\) is the speed of light, \(\lambda\) is the signal carrier wavelength, \(\varepsilon\mathrm{r_{m}}\) and \(\varepsilon\mathrm{t_{m}}\) are the observation noise of pseudorange and carrier, respectively. To reduce various errors in positioning, we use a double-difference observation combination. The expressions are as follows:  
\[\
abla\Delta\mathrm{P_{sm}^{jk}}=\
abla\Delta\mathrm{\rho_{sm}^{jk}}+\
abla \Delta\varepsilon_{\mathrm{r_{sm}^{jk}}} \tag{4}\]  
\[\
abla\Delta\mathrm{\phi_{sm}^{jk}}=\
abla\Delta\mathrm{\rho_{sm}^{jk}}+ \lambda\cdot\
abla\Delta\mathrm{N_{sm}^{jk}}+\
abla\Delta\varepsilon_{ \mathrm{t_{sm}^{jk}}} \tag{5}\]  
where, s represents antenna s, m represents antenna m, j represents satellite j, and k represents satellite k, \(\
abla\Delta\mathrm{P_{sm}^{jk}}\) is the pseudorange double difference, \(\
abla\Delta\mathrm{\rho_{sm}^{jk}}\) is the double difference of the geometric distance between the antenna and the satellite, \(\
abla\Delta\mathrm{\phi_{ms}^{jk}}\) is the carrier phase double difference, \(\
abla\Delta\mathrm{N_{sm}^{jk}}\) is the integer ambiguity double difference, \(\
abla\Delta\varepsilon_{\mathrm{r_{sm}^{jk}}}\) and \(\
abla\Delta\varepsilon_{\mathrm{t_{sm}^{jk}}}\) are the observed noise double differences of pseudorange and carrier, respectively. Under short baseline conditions, most errors in observed quantities such as satellite orbit and clock differences, ionospheric and tropospheric delays, and receiver clock differences are eliminated after applying the double-difference combination.
# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 4 IberSpeech-RTVE 2022 Submitted Systems
### Speaker Diarization and Identity Assignment Challenge  
Only 2 teams have participated in the Speaker Diarization challenge and only one of them submitted results with Identity Assignment.  
* **IRIT**[25]. Universite de Toulouse (France) The IRIT submission was based on the pyannote.audio diarization system. pyannote.audio ([[https://github.com/pyannote/pyannote-audio](https://github.com/pyannote/pyannote-audio)]([https://github.com/pyannote/pyannote-audio](https://github.com/pyannote/pyannote-audio))) (accessed on 24 July 2023) is an open-source toolkit written in Python for speaker diarization. Version 2.1 introduces a major overhaul of pyannote.audio default speaker diarization pipeline, made of three main stages: speaker segmentation applied to a short sliding window, neural speaker embedding of each (local) speaker, and (global) agglomerative clustering. The IRIT submission focused on Speaker Diarization.
* **IV**[26]. Intelligent Voice (UK) The Intelligent Voice submission was based on a Variational Bayes x-vector Voice Print Extraction system. The proposed system is capable of capturing vocal variations using multiple x-vector representations with two-stage clustering and outlier detection refinement. It implements the Deep-Encoder Convolution Autoencoder Denoiser network for denoising segments with noise or music on files identified by a signal-to-noise ratio classifier. Intelligent Voice submitted systems for both Speaker Diarization and Identity Assignment.
# Enhanced Effect of Phytoextraction on Arsenic-Contaminated Soil by Microbial Reduction
###### Abstract
[19] demonstrated that arsenate-reducing bacterial inoculants and composite microbial inoculants increased dissolved organic carbon and available arsenic in the soil, stimulating the growth of _Pteris vittata_ and arsenic accumulation. Unlike the aforementioned study, this research not only focuses on the release and reduction processes of arsenic, but also examines the influence of microorganisms on sulfur and iron elements in the soil, as well as their counteractive effects on the transformation of arsenic. Numerous studies have shown that the release, migration and transformation of arsenic are closely related to organic matter [20; 21] and iron-sulfur geochemical cycling processes in anaerobic environments involving soil and groundwater [22]. For example, bacterial-accelerated oxidative acidification of sulfides leads to the release of arsenic by mineral dissolution [23]. And bacterial-induced reductive dissolution of iron oxides causes the release and reduction of arsenic in the adsorbed state [24]. Therefore, in this work, functional iron-sulfur-reducing flora was enriched. The advantage of microbial flora over single strains in soil remediation lies in their ability to synergistically facilitate the translocation of pollutants from the soil to the aboveground parts of plants, as well as the accumulation, transformation and utilization of these pollutants within the plant body, thereby effectively facilitating the remediation of environmental pollution. Additionally, plant organic matter from straw fermentation was utilized as a carbon source for microorganisms and as nutrients for _Pteris vittata_. Ultimately, a novel microbial-_Pteris vittata_ remediation mechanism centered around biocatalytic iron-sulfur reduction and arsenic speciation transformation was proposed.
# Research on the Flight Performance of Biomimetic Moth Based on Flapping Function Control
## 4 Flight Experiment and Result Analysis of Biomimetic Flapping-Wing Aircraft  
### Design and Manufacture of Biomimetic Flapping-Wing Aircraft  
Referring to the results of the simulation calculation, a pair of wings with a unilateral wingspan of about 330 mm and a maximum chord length of about 360 mm can generate about 0.6 N to 0.8 N of lift, which means it can drive a flapping-wing aircraft with a weight of about 60 g to 80 g to fly. Therefore, the flapping-wing aircraft needs to be designed as lightweight as possible.  
Referring to the previous biomimetic analysis, the wings of the biomimetic flapping-wing aircraft are designed as a lightweight biomimetic flexible membrane wing structure. The veins are carbon fiber rods of different diameters bonded according to a certain venation, and the wing membranes are lightweight film materials adhered to the carbon fiber skeleton. The two wings are directly driven by the left and right servos fixed on the servo frame. The servo bracket, control board bracket, hind-wing bracket, and battery bracket are installed on the main carbon fiber square rod, as shown in Figure 10. The wing shape and wingspan of the flapping-wing aircraft are the same as those of the model used in the simulation, as shown in Figures 3 and 4. The flapping-wing aircraft, including the control board, battery, and wires, weighs 66.5 g.  
For flight control, a custom Arduino control board is selected, the 8-channel pulse position modulation (PPM) signal is received by the A8S signal receiver (FLYSKY, Guangdong, China), and the control signal is provided by the FS-i6X panel flight controller (FLYSKY, Guangdong, China). The left and right servos output torque periodically according to the flapping function and directly drive the left and right wings, as shown in Figure 11. The selected servo motor is the GDW1906B servo (Dongguan City Gliding World Electronic Technology Co., Ltd, Dongguan City, China), which can generate approximately 2.6 kg \(\times\) cm of torque when driven by a 7 V DC power supply. The flight controller changes the key parameters of the flapping function through different joysticks or buttons, thereby realizing the control of the flapping-wing aircraft.  
Figure 10: Overall Top View of Biomimetic Flapping-wing Aircraft.
## 5 Simulation Results and Analysis  
In this section, our proposed routing protocol was implemented, verified, and analyzed by comparing it with GPSR [9], QNGPSR [19], and TQNGPSR [20]. We tested the network performance under different speeds, packet-sending rates, and node densities to comprehensively prove the superiority of our algorithm.  
The simulation experiments of all the protocols were conducted based on the Python coding language. All the UAVs were moving in a random direction model. The UAVs were distributed in a rectangular area of 2 km \(\times\) 2 km. The communication radius was 350 m. Hello packets were broadcast periodically at fixed intervals of 0.5 s. The maximum speed of the UAVs ranged from 5 to 25 m/s. The input features were processed by the min-max normalization method, and SELU was used as the activation function. In the training process, [PERSON] was used as the optimizer, and the learning rate was set to 0.001. The minibatch gradient descent was used to update the network parameters, and the batch size was 32. The neuron network was 7 \(\times 16\times 8\times 1\). The main parameters of the simulation experiment are shown in Table 1.  
The convergence rate, the average end-to-end (E2E) delay, and the packet-delivery ratio (PDR) were used as evaluation metrics to compare the network performance of the routing protocols. The specific definitions of these evaluation metrics were as follows.  
* Convergence rate: The convergence rate was the limit of the ratio of two consecutive errors. In our scenario, the convergence rate was evaluated by training 100 maps, according to the training results, to determine whether the algorithm converged and to analyze the convergence speed and the network performance at convergence.
* Average end-to-end delay: The average E2E delay was measured by calculating the average delay of the data packets from the source node to the destination node.
* Packet-delivery ratio: The PDR was measured by calculating the ratio of successfully received data packets of the destination node to the number of total transmission packets of the source node.
# Evolution of Access Network Sharing and Its Role in 5G Networks
## 2 Access Sharing Models
### Local Loop Unbundling (LLU)
#### 2.1.3 Legacy Bitstream Access  
Bitstream access was proposed in 2002 in order to enhance Internet access offers [9]. Bitstream provides OLOs with a new approach to utilize the incumbent's network through a new model that allows wholesale of DSL products.  
The European Regulators Group (ERG) defines bitstream as \"a high-speed access link to the customer premises with transmission capacity for broadband data in both directions [10]\".  
In bitstream, shown in Figure 1c, the point of access for an OLO (traffic handover point) determines both the level of control over technical parameters and usage of OLO's own network instead of the incumbents'. In principle, this provides the OLO with the choice on the amount of infrastructure to be deployed in the field, keeping in mind that this implies a trade-off with the level of control over the network performance.  
As a consequence, the main drawback of the bitstream is that OLOs can only offer a small number of different services (i.e., those defined by the incumbent). In addition, typically these are the same for all OLOs sharing the same infrastructure. This means that on the one hand, OLOs can only compete over price, as they cannot differentiate their service offerings among them. On the other, incumbents have little incentives to install new technology as there is no competition at the physical layer level with any of the OLOs.
## 2 Materials and Methods
### The Architecture of PMLD Matrices  
As mentioned previously, the architecture of PMLD matrices were introduced in [18]. This architecture is based on six requirements:  
1. Each element within the matrix is distinct.
2. Zero-order differences are positioned along the main diagonal.
3. First-order differences are positioned along the secondary diagonal.
4. The indices \(x\) and \(y\) can assume one of three possible values as explained in [18].
5. The ideal matrix of Lagrange differences maintains lexicographical balance, ensuring that the number of symbols \(x\) and \(y\) in the expressions of all matrix elements is equal.
6. The ideal matrix of Lagrange differences maintains temporal balance, ensuring that the number of indices with subscripts minus delta and plus delta in the expressions of all matrix elements is equal.  
There are only eighteen different matrices satisfying these six requirements [18]. Without loss of generality, we will consider PMLD matrix #1 in this paper. Given two synchronously recorded time series \((x_{n}:n=0,1,2,3\ldots)\) and \((y_{n}:n=0,1,2,3\ldots)\), PMLD matrix #1 reads [18]:  
\[PMLD_{n}=\left[\begin{array}{cc}x_{n}&y_{n+\delta}-x_{n+\delta}\\ y_{n-\delta}-x_{n-\delta}&y_{n}\end{array}\right] \tag{1}\]  
where the index \(n\) represents the current moment and \(\delta\) is used to represent the time lag (\(\delta\ \epsilon\ \mathbb{N}\)). In Equation (1), the elements on the principal diagonal represent the zero-order derivatives. The elements on the secondary diagonal represent the differences betweenthe elements of time series \(x_{n}\) and \(y_{n}\). Those differences could be interpreted as first-order cross derivatives.  
It has also been demonstrated that PMLD matrices can be extended to higher orders [20]. For example, given three synchronously recorded time series \((x_{n}:n=0,1,2,3\ldots)\), \((y_{n}:n=0,1,2,3\ldots)\), and \((z_{n}:n=0,1,2,3\ldots)\), the third-order (PMLD) matrix #1 reads [20]:  
\[PMLD_{n}=\begin{bmatrix}x_{n}&y_{n+\delta}-x_{n+\delta}&z_{n+\delta}-x_{n+ \delta}\\ y_{n-\delta}-x_{n-\delta}&y_{n}&z_{n+\delta}-y_{n+\delta}\\ z_{n-\delta}-x_{n-\delta}&z_{n-\delta}-y_{n-\delta}&z_{n}\end{bmatrix} \tag{2}\]  
Note that this extension to the third order matrix keeps all six requirements in force. In other words, the extended third-order matrix in Equation (2) is also a PMLD matrix.
## 3 Results and Discussion
### Machine Learning-Based Prediction of Compressive Strength
(a) SVR; (b) LMR; (c) RF; (d) DTR; (e) KNNR; (f) RR; (g) GPR; (h) NNR. The evident discrepancies observed may indicate the presence of overfitting. To address this issue and improve model generalization, several strategies can be considered. First, increasing the dataset size or incorporating additional samples can help reflect more underlying patterns [103; 104]. Second, simplifying model configuration to reduce complexity is a viable approach, as overly complex models are prone to memorize noise artifacts rather than meaningful patterns [105]. A more detailed examination of Figures 5-7 reveals distinct prediction behaviors across models. NNR consistently achieves high accuracy across the entire compressive strength range, with minimal over- or underestimation. DTR and RF perform well in the mid-strength region (20-50 MPa) but tend to overfit, as indicated by reduced accuracy on  
Figure 6: Comparison of experimental and predicted compressive strength. (**a**) SVR; (**b**) LMR; (**c**) RF; (**d**) DTR; (**e**) KNNR; (**f**) RR; (**g**) GPR; (**h**) NNR. Figure 7: Absolute error of compressive strength prediction. (**a**) SVR; (**b**) LMR; (**c**) RF; (**d**) DTR; (**e**) KNNR; (**f**) RR; (**g**) GPR; (**h**) NNR. the test set. SVR underestimates high-strength values (>50 MPa), while GPR struggles with low-strength predictions (<20 MPa), though it improves in the mid-range. KNNR shows erratic predictions in low-strength regions due to sensitivity to sparse data, and LMR displays widespread error across all ranges, confirming its limited suitability for capturing nonlinear trends. These patterns underscore the varying strengths and weaknesses of each model depending on the data distribution.