esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The use of a quadriceps tendon with an attached bone block (QTB) as a source of a graft for anterior cruciate ligament (ACL) reconstruction (ACLR) has gained popularity in recent years. Its advocates point out its greater cross-sectional area and higher failure load than a patellar tendon graft, making it a feasible option for an ACL graft choice [1, 2]. Additionally, it enables the bone-to-bone healing, which is faster and results in stronger incorporation than bone-to-tendon healing mode for all soft tissue grafts [3, 4]. These assumptions are being verified by recent systematic reviews that report that quadriceps tendon is equal to other graft options for ACLR in terms of stability, functional outcomes and complications [5, 6].  
The outcome of an ACLR is strongly influenced by the progress of graft remodeling. To enhance the progress of ligamentization, various modalities have been proposed, with the use of platelet rich plasma (PRP) being one of the most extensively used in a variety of clinical settings [7; 8; 9; 10; 11; 12]. PRP is a first-generation autologous platelet concentrate used to promote wound healing and tissue regeneration through the release of growth factors [13]. However, despite its clinical popularity, the utility of PRP in ACL reconstruction is still controversial [8]. Thus, the purpose of this study was to assess the impact of PRP on pain, range of motion (ROM) restoration, knee stability, tibial tunnel widening and functional results.
## 2 Materials and Methods
### Volatile Composition  
The volatile composition was determined using headspace solid phase micro-extraction (HS-SPME). After several preliminary tests to optimize the extraction system, accurately amounts between 20 and 40 mg of saffron stigmas and between 120 and 140 mg of saffron flowers were weighted and added into a 40 mL vial with polypropylene caps and PTFE/silicone septa, and isoamyl acetate (1000 mg/L, internal standard for semi quantification of compounds). Fresh, freeze-dried, and air oven-dried samples of saffron flowers were used in the analyses. Then, the vial was placed in an AOC-6000 Plus autosampler (Shimadzu Corporation, Kyoto, Japan), and after 5 min of equilibration time, a 50/30 \(\upmu\)m DVB/CAR/PDMS fiber (1 cm) was exposed to the sample headspace for 45 min at 40 \({}^{\circ}\)C (with agitation, 250 rpm).  
The separation and identification of compounds were performed by GC2030 (Shimadzu Scientific Instruments, Inc., Columbia, MD, USA) in a Sapiens X5 MS column (Teknokroma, Barcelona, Spain), 30 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m f.t., and coupled with a mass spectrometer detector (TQ8040 NX triple quadrupole mass spectrometer; Shimadzu Scientific Instruments, Inc., Columbia, MD, USA). Only the single quadrupole acquisition mode was used on the TQ8040 NX (Q3 Scan; scan speed 5000 amu/s; mass range 40-400 \(m/z\); event time 0.100 s). The oven temperature program was as follows: (i) initial temperature of 35 \({}^{\circ}\)C and hold 5 min; (ii), increment of 5 \({}^{\circ}\)C/min up to 150 \({}^{\circ}\)C/min, and hold 1 min; (iii) increment of 10 \({}^{\circ}\)C/min up to 280 \({}^{\circ}\)C and hold for 5 min. Helium column head pressure was 47.6 kPa (constant linear velocity mode of 36 cm/s). Injector, ion source, and interface were at 250, 230, and 280 \({}^{\circ}\)C, respectively. Helium was used as gas carrier, column flow 1 mL/min, with split ratio 1:50 and purge flow of 6 mL/min.  
Retention indexes of a commercial alkane standard mixture were used to identify the compounds, as well as the National Institute of Standards and Technology (NIST) 17 Mass Spectral and Retention Index Libraries. The identification was considered tentative when it was based only on mass spectral data, and only compounds with spectra similarity \(>\) 90% were considered correct hits. Linear retention similarity filter was set at \(\pm\)10 units. This volatile compound extraction method has been previously used for the analysis of different food matrices, according to [PERSON] et al. (2018).
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 2 Review of Edge Computing Architectures
### Fog Computing
#### 2.3.3 Video and Game Analytics  
Video analytics always involve intensive computation or large bandwidth demand. To address this issue, the authors in [53] proposed a system called Latency-Aware Video Edge Analytics (LAVEA) based on an edge computing platform. This system offloads computation between clients and edge nodes, fosters collaboration among neighboring edge nodes, and delivers low-latency video analytics to nearby users. Cloud gaming is an emerging technique, but it suffers from high response latency and bandwidth costs. [PERSON] et al. [54] proposed a fog computing system named CloudFog that can handle the challenges brought by massively multiplayer online gaming. CloudFog uses super nodes as \"fog\" to connect users and the cloud. The cloud is responsible for intensive computations and transmits updated data to supernodes. These super nodes are responsible for creating game videos to be streamed to players.
## 4 Methodology
### De-Interfered Anomaly Principal Component Thresholding Technology  
Principal component analysis (PCA) is a widely used and mature technique for extracting anomalous information. This paper uses the \"De-interfered Anomaly Principal Component Thresholding Technology\", which has been determined by the China Geological Survey as a remote-sensing technology method that must be used in mineral survey work [96; 97]. In a pixel, as long as the vegetation does not exceed 50%, anomalous information can be extracted. After performing principal component transformation on the selected bands, the threshold of 2, 3, and 4 is set based on the variance \(\sigma\) of the anomaly intensity as the scale to achieve density segmentation of the anomaly principal components. To present the anomaly image more intuitively, the density segmentation result also needs to be processed with a 5 \(\times\) 5 Gaussian low-pass filter.
# Investigation of THz Absorptive Signatures in Opioids
## 5 Discussion  
The experimental results clearly establish that lactose monohydrate was used as the \"filler\" material for the oxycodone tablets. These results also confirmed our assumption that the mixing of materials does not change their individual spectroscopic lines.  
Oxycodone likely has a line just below 1.6 THz. This is consistent qualitatively with the Gaussian modeling (Figure 3). However, for oxycodone, the \(\sim\)1.358 THz signature is difficult to discern because of its proximity to the 1.370 THz line of lactose. We notice that the 1.370 line in Figure 7 spans from \(\sim\)1.355 to 1.397 THz, which is about 7-GHz shift to the left and is narrower than the 1.370 THz line spanning from \(\sim\)1.362 to 1.420 THz in Figure 6 for pure lactose samples. More investigations need to be performed to confirm the signatures by measuring \"pure\" samples through collaboration with pharmaceutical companies or chemists.  
Another effect that needs to be considered is background scattering from grains of powder samples, which can lead to substantial attenuation. This can be seen from the \(\sim\)10 dB falloff of transmittance in Figure 6 as frequency (\(f\)) increases despite the high-pressure pressing for sample preparation. Such scattering has a power-law dependence on frequency, i.e., \(f^{n}\)[25; 26]. Hence, the scattering may have less impact on fentanyl detection, as the predicted 0.337 THz line is positioned at the lower end of the THz regime.
# Multi-Agent-Supported Tracking Based on Hidden Markov Model with Weighted Entropy
## 3 Multi-Agent-Supported Tracking Based on Hidden Markov Model with Weighted Entropy  
### Overview  
Unlike traditional TBD methods that perform global optimization and track updates centrally, we address the MOT problem using the multi-agent mechanism, assigning independent agents to each track when a new target enters the scene. The agents' role is to manage a separate Kalman filter and take actions to modify the track under its responsibility. As shown in Figure 1, in each frame the agent obtains detections from the detector and makes its decision according to the Kalman filter and weighted entropy mechanism. This framework can be conducted in both a decentralized way and a multi-thread way. The agents compete with each other to obtain the detections under the communication protocol proposed by [PERSON] et al. [13] and adjust their strategies through trust-region policy optimization (TRPO) based on rewards determined by the joint tracking performance relative to ground truth object tracks. The reward is denoted by  
\[r_{i}=\omega dist^{m}(\tau_{i},d_{j})+(1-\omega)dist^{a}(\tau_{i},d_{j}) \tag{1}\]  
\[dist^{m}(\tau_{i},d_{j})=\frac{\tau_{i}\cap d_{j}}{\tau_{i}\cup d_{j}} \tag{2}\]  
\[dist^{a}(\tau_{i},d_{j})=1-\frac{\tau_{i}\cdot d_{j}}{\|\tau_{i}\|\|d_{j}\|} \tag{3}\]  
where \(dist^{m}(\tau_{i},d_{j})\) represents the motion similarity between track \(\tau_{i}\) and the selected detection \(d_{j}\), while the appearance similarity is denoted as \(dist^{a}(\tau_{i},d_{j})\), and \(\omega\) is a weight parameter.
# Traffic-Predictive Routing Strategy for Satellite Networks
## 2 Related Research
Simulation results show that compared with single-path TCP, MPTCP significantly improves throughput performance and prevents data transmission interruption caused by satellite switching. [PERSON] et al. designed a mutually exclusive multipath routing algorithm [19], which sets track spokespersons through which real-time link state is collected and exchanged. Then, according to the topology of the whole network, multiple optimal paths of the local satellite and the cooperative satellite are calculated, so that the data can achieve traffic balance in the transmission process, so as to enhance the real-time downlink data transmission capability of the satellite. To improve the throughput of satellite networks, [PERSON] et al. proposed a multi-path cooperative routing algorithm based on source and destination nodes, which dynamically and cooperatively transmits different parts of the data stream along the disjoint paths of multiple links. In addition, an efficient No-Stop-Wait ACK mechanism is designed to accelerate data transmission [20]. Alternative path routing protocol is an alternate routing protocol that is often used to achieve redundancy and high availability. This type of protocol provides multiple alternate paths. When the primary path fails, you can switch to the alternate path to ensure connectivity. [PERSON] et al. proposed an alternate link-based routing and forwarding policy [21], which diverts traffic from the shortest path to the alternate shortest path to achieve load balancing. Based on the above discussion, it can be seen that table-driven routing protocols usually require more network resources and bandwidth to maintain routing tables, while on-demand routing protocols may introduce a certain delay when routing updates. Load fluctuations and unevenness in satellite communications can lead to degraded network performance, such as increased latency, packet loss, and congestion. However, traffic prediction can be used to achieve load balancing by redirecting traffic to a path with more available bandwidth [22]. At the same time, it can also be used for fault tolerance, ensuring that even if some paths fail, packets will still be transmitted according to the optimal path. At present, there is some research on satellite network routing based on traffic prediction. The literature [5] captures the spatiotemporal correlation of satellite network traffic to predict the future network state and designs routing strategies based on the load levels of neighboring satellites. To improve the accuracy of traffic prediction, the literature [23] improved the Gated Recurrent Unit (GRU) neural network to more effectively analyze the self-similarity and long correlation between data series by introducing an attention mechanism, to improve the prediction accuracy. In addition, the Particle Swarm Optimization (PSO) algorithm is introduced to automatically obtain the best neural network model hyperparameters, thus improving the prediction efficiency.
## 2 Cable-Driven Parallel Manipulators Model  
### Sagging Cable Model  
In this study, the realistic sagging cable model introduced by [PERSON] [17] will be used as the standard model in order to derive others. [PERSON]'s sagging cable model is widely used from small-scale to macro applications such as cable-stayed bridge, improving the analysis of CDPM [18], ship anchors analysis, etc. because of the validation for elastic and deformable cable whose mass is considerable. Let consider the cable that has an unstrained length \(L\) and one extremity is fixed in the origin of frame \(\{A\}\), illustrated in Figure 1. The other extremity **B** was influenced by external force \(\textbf{T}=\left[\begin{array}{cc}T_{x}&T_{z}\end{array}\right]^{T}\), and we can treat this force as the tension of the cable at free extremity. The reaction force at **A** is denoted as \(\textbf{T}^{\prime}=\left[\begin{array}{cc}T^{\prime}_{x}&T^{\prime}_{z} \end{array}\right]\). Let \(\rho\), \(E\), \(A\) be the linear density, Young's modulus, and unstrained cross-section area of the considering cable, respectively. The Cartesian coordinates of a certain point on the cable with respect to \(\{A\}\) at the Lagrangian coordinates s is given by Equations (1) and (2).  
\[x^{A}(s)=\frac{T^{A}_{x}s}{EA}+\frac{\left|T^{A}_{x}\right|}{\rho g}\left[ \sinh^{-1}\!\left(\frac{T^{A}_{z}-\rho g(L-s)}{T^{A}_{x}}\right)-\sinh^{-1}\! \left(\frac{T^{A}_{z}-\rho gL}{T^{A}_{x}}\right)\right] \tag{1}\]  
\[z^{A}(s)=\frac{T^{A}_{z}s}{EA}+\frac{\rho g}{EA}\!\left(\!\frac{s^{2}}{2}-Ls \!\right)+\frac{1}{\rho g}\!\left[\sqrt{\left(T^{A}_{x}\right)^{2}+\left(T^{A} _{z}-\rho g(L-s)\right)^{2}}-\sqrt{\left(T^{A}_{x}\right)^{2}+\left(T^{A}_{z} -\rho gL\right)^{2}}\right] \tag{2}\]  
The Cartesian coordinates of extremities of cable respect to \(\{A\}\) can be expressed as in (3) and (4).  
\[\textbf{A}^{A}=\left[\begin{array}{cc}x^{A}(0)&z^{A}(0)\end{array}\right]^{T} \tag{3}\]  
\[\textbf{B}^{A}=\left[\begin{array}{cc}x^{A}(L)&z^{A}(L)\end{array}\right]^{T} \tag{4}\]
## 4 Overview of GNSSs
### Gps
#### 4.1.1 Gps System Architecture  
The GPS architecture is divided into three segments: space, ground, and user segments. The GPS uses the distance method of measuring the navigation parameter. The idea of this method is to measure the distance to at least three known measurement points--satellites--whose locations are known to the user at any time. The condition for the implementation of this method is to maintain the straightness of the propagation of radio waves and the constant speed of their propagation in the electromagnetic environment. The navigation parameter is measured by measuring the propagation time of radio signals of known structure, transmitted by the navigation satellite, and received by the user's receiver. The prerequisite for the correct operation of a navigation system using the distance method is a follows [79; 80]:  
* Elimination of the resulting time scale error in the receiver;  
Figure 10: Positioning using GNSS. Prepared on the basis of [72].  
* Accurate synchronization of the time scale of the user's receiver with the time scale of the GPS system.
## 2 Study Area and Data
### Data  
#### 2.2.1 TM Images  
To test the performance of the improved model under different illumination conditions, two Landsat-5 TM scenes (cloud cover less than 5%) acquired on 18 September 2007 and 18 March 2010 were downloaded from U.S. Geological Survey website ([[http://glovis.usgs.gov/](http://glovis.usgs.gov/)]([http://glovis.usgs.gov/](http://glovis.usgs.gov/))). It would be better to choose summer and winter images to make larger sun elevation difference. However, restricted by the  
Figure 1: **(a)** Location of study area in China. **(b)** Land cover types in study area. **(c)** DEM of study area.  
overall image quality (mainly cloud coverage), only clear images in spring and autumn were available. Landsat TM imagery of path 130, row 38 (WRS 2) was chosen as our basic experiment data (covering area indicated by red quadrangle in Figure 1c). The sun elevation angles for the scene center during the image acquisition are 48 degrees (spring scene) and 54 degrees (autumn scene), respectively, and their corresponding sun azimuth angles are 137 degrees and 141 degrees. The obtained spring and autumn images have already been orthorectified, with a geometric error less than 50 m [22].  
All the TM bands were used in our correction experiment except for band 6 thermal data. Digital Numbers of each band were firstly converted to the top-of-atmosphere radiance with the corresponding gains and offsets found in attached header file [23]. Solar zenith/azimuth angle and view zenith/azimuth angle of the image corners were calculated, and then interpolated for each pixel of the image using bi-linear interpolation method [24]. These auxiliary angle files will be used as input parameters in the subsequent topographic correction procedure.
## 2 Study Area and Data Sources
### Data Sources  
This study integrates multi-source data encompassing Landsat images, high-resolution remote sensing images, net primary productivity (NPP), soil conservation data, precipitation records, grain output, and price statistics (Table 1).  
Figure 1: Study area.
## 4 OSM as a Source for Crowdsourced (Indoor) Geodata
By tagging the different OSM-features with the key _height_, it is furthermore possible to provide 3D information, such as the height of a room. Information about doors is added by using OSM-_nodes_ and the width or height of the door can be provided with the corresponding key-value pairs. The basic ideas of the _IndoorOSM_ mapping schema are also depicted in Figure 1, whereby Figure 1(a) depicts the basic hierarchical principle of a building and Figure 1(b) shows the composition of a detailed floorplan for an exemplary building level (in this case level 0, _i.e._, the ground level). Since it is only possible to map two-dimensional geometries in OSM, vertical connections, such as stairways, escalators or elevators, need to be expressed semantically with keys and values. In general, every vertical passage is mapped as a polygon (_i.e._, a closed way) and tagged with _buildingpart_\(=\)_verticalpassage_. That is, for each elevator funnel, stairway area or escalator platform, there is a polygon on the corresponding floor. Furthermore, _IndoorOSM_ provides the key _buildingpart:verticalpassage_ for refining the corresponding type. Exemplary values are _stairway_, _escalator_, _elevator_, _ramp_, _etc._ The general floor range of a vertical passage is provided by using _buildingpart:verticalpassage:floorrange_, so a user can describe for example that an elevator ranges from the ground level to the second level (value 0-2). Since all of these vertical passage polygons are accessible via another building part (e.g., a corridor next to the elevator), the _IndoorOSM_ model provides a corresponding door (or opening) node between the two adjacent elements. By adding the key _connector:ids_ to these nodes, it is possible to semantically describe the connectivity between vertically connected elements. The value of this key is the OSM-ID of the element to which a vertical connection exists. For a one-way connector (e.g., an escalator), only the starting node is tagged with _connector:ids_, whereas for multi-way connectors (e.g., stairways or escalators), both adjacent _nodes_ are tagged accordingly. However, the key _connector:ids_ only contains information about the directly adjacent neighbors. For more information on _IndoorOSM_ please refer to the underlying research publication [14] or the _IndoorOSM_ wiki page [13].
## 2 Materials and Methods  
### Study Area and the Experimental Procedure  
This study was conducted on three carrot fields irrigated with a center-pivot system (Fields' IDs: N1, N4, and N6), each with an area of 27 hectares, belonging to the Tawdeehiya Farms located between the cities of Al-Kharj and Haradh in Saudi Arabia. This farm has an area of approximately 7000 ha and is located within an arid climatic zone between the latitudes of 24\({}^{\circ}\)10\({}^{\prime}\)22.77\({}^{\prime\prime}\) and 24\({}^{\circ}\)12\({}^{\prime}\)37.25\({}^{\prime\prime}\) N and the longitudes of 47\({}^{\circ}\)56\({}^{\prime}\)14.60\({}^{\prime\prime}\) and 48\({}^{\circ}\)05\({}^{\prime}\)08.56\({}^{\prime\prime}\) E (Figure 1). Sandy loam soil was the dominant soil type in the farm, where the main crops grown during the study period included a group of vegetables, the most important of which was the carrot crop. The mean annual temperatures in the experimental site ranged between 12 \({}^{\circ}\)C and 42 \({}^{\circ}\)C in the winter and summer seasons, respectively. The mean annual rainfall was about 98 mm, distributed mainly in the period between November and February. Tawdeehiya Farm produces carrots commercially and has facilities for data collection and measurements for continuous monitoring and yield assessment.  
Figure 1: Location map of Tawdeehiya Farms (**A**); experimental fields (**B**) and sampling strategy used for field data collection (**C**).
# Interference Avoidance through Periodic UAV Scheduling in RIS-Aided UAV Cluster Communications
## 3 UAV Scheduling to Avoid Interference
### LIM-Based Rollout Scheme
On the other hand, the RIM scheme will apply the LIM algorithm based on both nodes \(\hat{\mathbf{s}}_{k,1}\) and \(\hat{\mathbf{s}}_{k,2}\), and find their corresponding destination nodes \(\hat{\mathbf{s}}_{MN_{c},LIM}\) and \(\hat{\mathbf{s}}_{MN_{c},RIM}\), which are represented by the red arrow in the figure. The interference levels of these nodes are denoted as \(\gamma_{MN_{c},LIM}\) and \(\gamma_{MN_{c},RIM}\). Because the LIM scheme satisfies the sequential consistency condition, applying LIM from \(\hat{\mathbf{s}}_{k-1}\) will result in the same destination as applying LIM on \(\hat{\mathbf{s}}_{k,1}\), and we have  
\[ \begin{split}\gamma_{MN_{c},LIM}&=\underset{\hat{ \mathbf{s}}_{k,1}\dots\hat{\mathbf{s}}_{MN_{c}}}{\min}\gamma_{MN_{c}}(\bar{\mathbf{s}}_{ MN_{c}})\ (w/\ \text{LIM})\\ &=\underset{\hat{\mathbf{s}}_{k+1}\dots\hat{\mathbf{s}}_{MN_{c}}}{\min} \gamma_{MN_{c}}(\bar{\mathbf{s}}_{MN_{c}})\ (w/\ \text{LIM}).\end{split}  \tag{17}\]  
Assuming the RIM algorithm selects node \(\hat{\mathbf{s}}_{k,2}\) as indicated by the blue arrows in the figure, we have  
\[ \begin{split}\gamma_{MN_{c},RIM}&=\underset{\hat{ \mathbf{s}}_{k+1}\dots\hat{\mathbf{s}}_{MN_{c}}}{\min}\gamma_{MN_{c}}(\bar{\mathbf{s}}_{ MN_{c}})\ (w/\ \text{LIM}).\end{split}  \tag{18}\]  
The RIM algorithm selects \(\bar{\mathbf{s}}_{k}\), which leads to the minimum interference, indicating that \(\gamma_{MN_{c},RIM}\leqslant\gamma_{MN_{c},LIM}\). This demonstrates that applying RIM at stage \(k\) from \(\hat{\mathbf{s}}_{k-1}\) can reduce the interference level.