esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In fact, postoperative physical therapy and gait training may lead to improved function and weight-bearing in patients who have undergone HV surgery [13]. In this sense, the abductor hallucis (AH) is considered a fundamental muscle to target in HV rehabilitation [14], which also focuses on proper activation of the intrinsic foot muscles globally and on balancing the activation ratio between AH and adductor hallucis. A variety of therapeutic exercises have been identified in the literature as beneficial for activating AH. Among the most commonly used exercises are Toe Spread Out (TSO), Short Foot (SF), Forefoot Adduction (FA), and Flexion of the Metatarsophalanges (FM) [15]. TSO is an essential exercise aimed at improving flexibility and strength of the intrinsic muscles of the foot, particularly those responsible for toe alignment. This movement encourages the active use of AH and helps to stop the toes from drifting toward the midline, which is a common problem in individuals suffering from HV. By practicing TSO regularly, the muscle strength and coordination necessary for proper toe positioning can be enhanced, potentially reducing discomfort and correcting alignment over time [16]. SF is a crucial exercise for foot rehabilitation, focusing on strengthening the intrinsic muscles that contribute to supporting the medial longitudinal arch. It has been shown that SF not only strengthens the foot muscles but could also enhance overall balance and control, which are beneficial for individuals suffering from HV [2]. FA is an exercise that involves a movement targeting the alignment of the forefoot. It emphasizes the engagement of the adductors and the intrinsic foot muscles, counteracting the lateral movement commonly observed in HV [17]. This exercise has been shown to be effective in enhancing foot stability, particularly during dynamic activities. The FM exercise focuses on increasing strength and flexibility in the muscles responsible for toe flexion. This exercise could be useful for counteracting the effects of HV, which makes the joint stiff and misaligned. By regularly practicing this movement, patients can improve joint mobility and muscle activation, contributing to foot function and stability [1]. However, it is not yet fully clear which therapeutic exercise is the most suitable and effective for stimulating AH. Clarifying this knowledge gap could be useful, as many approaches could consequently vary in terms of effectiveness, clinical applicability, and in relation to different degrees of deformity [18]. Therefore, this study aimed to compare four different therapeutic exercises with sEMG to assess their effectiveness in activating AH in patients suffering from HV, with the final goal of building new and more effective rehabilitation strategies.
## 2 Methodology of the Inversion
### Direct Problem
Temperature response at the surface depending on the used model for three configurations: a heat source located at \(z=L_{z}/4\) (**b**), \(z=L_{z}/2\) (**c**) or \(z=3L_{z}/4\) (**d**). \[\left( \begin{array}{ccc}\mathbf{I}_{\text{3D}}^{(1)}&\mathbf{I}_{\text{3D}}^{(2)} \cdots&\mathbf{I}_{\text{3D}}^{(n)}\end{array} \right)\left[\Omega\right]=\left[ \begin{array}{c}\theta_{(1)}\\ \theta_{(2)}\\ \vdots\\ \theta_{(m)}\end{array} \right]\Leftrightarrow\mathbf{I}\,\Omega=\theta, \tag{9}\]  
where \(\mathbf{I}\) is an \(m\) by \(n\) operator matrix and \(\theta\) is a vector of size \(m\), and \(m\) and \(n\) are, respectively, the number of time steps and the depth discretization of the sample. \(n\) can then be seen as the number of point heat sources present in the material. For the 1D problem, each point of the operator matrix \(\mathbf{I}\) described above must be calculated using formula (8) since each of its values is independent of the other. However, for the 3D problem, calculation of matrix \(\mathbf{I}\) can be optimized as it is composed of several sub-matrices that are repeated (see Figure 3). Thus, it is not necessary to calculate each value of \(\mathbf{I}\).x To illustrate this phenomenon, Figure 3 gives the profile of the matrix \(\mathbf{I}\), obtained for the case where \(n_{x}=3\), \(n_{y}=4\), \(n_{z}=5\) and \(n_{t}=10\). In the case of the 3D problem, the operator matrix can indeed be written in the form of a block symmetric Toeplitz matrix [11]:  
\[\mathbf{I}_{\text{3D}}= \begin{pmatrix}A_{1}&A_{2}&\cdots&A_{n_{x}}\\ A_{2}&A_{1}&\ddots&A_{n_{x}-1}\\ \vdots&\ddots&\ddots&\vdots\\ A_{n_{x}}&A_{n_{x}-1}&\cdots&A_{1}\end{pmatrix} , \tag{10}\]  
where each element \(A_{i}\) is a matrix of size \(n_{t}n_{y}\) by \(n_{z}n_{y}\), \(n_{z}\) and \(n_{y}\) corresponding to the number of discretization points in \(z\) and \(y\), and \(n_{t}\) to the number of time steps. All the matrices \(A_{i}\) are themselves symmetrical in blocks, defined by:  
\[A_{i}= \begin{pmatrix}A_{i1}&A_{i2}&\cdots&A_{in_{y}}\\ A_{i2}&A_{i1}&\ddots&A_{in_{y}-1}\\ \vdots&\ddots&\ddots&\vdots\\ A_{in_{y}}&\cdots&\cdots&A_{i1}\end{pmatrix} , \tag{11}\]  
where each block \(A_{ij}\) is of size \(n_{t}\) by \(n_{z}\). The global matrix \(\mathbf{I}\) is therefore \(n_{x}n_{y}n_{t}\) by \(n_{z}n_{y}n_{x}\). Thus, instead of calculating all the points separately, just calculate the first row (by block) of each matrix \(A_{i}\)  
Figure 3: Typical profile of the operator matrix \(\mathbf{I}\) in 3D: test case for \(n_{x}=3\), \(n_{y}=4\), \(n_{z}=5\) and \(n_{t}=10\). using the formula (7) to reconstruct the entire matrix. The time savings by doing so are considerable. Indeed, for the 3D problem, the number of computations is \((n_{x}n_{y})^{2}n_{z}\).
# Deep Reinforcement Learning for Energy-Efficient 6G V2X Networks
###### Abstract  
The deployment of 6G vehicle-to-everything (V2X) networks is a challenging task given the 6G requirements of ultra-high data rates along with ultra-low latency levels. For intelligent transportation systems (ITSs), V2X communications involve a high density of user equipment (UE), vehicles, and next-generation Node-B (gNB). Therefore, optimal management of the current network infrastructure plays a key role in minimizing energy and latency. Optimal resource allocation using linear programming methods cannot be scaled to the required scenarios of 6G V2X communications and is not suitable for online allocation. To overcome these limitations, deep reinforcement learning (DRL) is a promising approach given its properties of direct integration with online allocation models. In this work, we investigate the problem of optimal resource allocation in 6G V2X networks, where ITSs are deployed to execute tasks offloaded by vehicles subject to data rate and latency requirements. We apply a policy optimization-based DRL to jointly reduce the number of active gNBs and the latency concerning the offloaded tasks going on in the vehicle. The model is analyzed for several ITS scenarios to investigate the performance observed and the advantages of the proposed policy optimization allocation for 6G V2X networks. Our evaluation results illustrate that the proposed DRL-based algorithm produces dynamic solutions that approximate the optimal ones at reasonable rates of energy consumption. Our numerical results indicate that DRL-based solutions are distinguished by equivalently balanced energy consumption under different scenarios.  
5G/6G; artificial intelligence; deep reinforcement learning; intelligent transportation systems; machine learning; multi-access edge computing; optimization; vehicle-to-everything Article
## 5 Conclusions  
This work explores an extension of the theoretical background of EOF. By assuming that feedback couples into the laser cavity from both sides, new dynamic equations are found for the lasing frequency, the threshold gain and the intrinsic linewidth. These are numerically evaluated to analyze laser stability. Solutions with linewidth reduction are observed, where tuning of the feedback parameters yielded a 74% and a 42% reduction with respect of the free running laser and the single feedback case, respectively. Results also show the existence of a stable solution, with feedback-insensitive lasing frequency and intrinsic linewidth, regardless of the lasing frequency. This case is obtained by tuning the phase of the feedback field, for external cavities with equal lengths and coupling factors. Furthermore, the feedback-insensitive case exists regardless of the feedback strength, within a weak feedback approximation, which is an major improvement with respect to the single feedback case. Additionally, the stability conditions show good tolerances with respect to all feedback parameters, albeit they become stricter as the feedback strenght increases. Choosing feedback parameters close to the feedback-insensitive conditions ensures stable solutions that are feedback tolerant. Finally, the proposed system relies on few components in straight-forward configurations, and the stable conditions can be met with mature components available in generic foundry platforms. This enables close to market, low cost, feedback-tolerant semiconductor lasers which have direct applications in multiple fields that rely on stable laser sources, such as coherent communications and spectroscopy.  
**Author Contributions:** Formal analysis, M.F.B.; Funding acquisition, N.V.; Project administration, N.V.; Resources, N.V.; Supervision, N.V.; Writing--original draft, M.F.B.; Writing--review & editing, M.F.B. and N.V. All authors have read and agreed to the published version of the manuscript.  
**Funding:** We acknowledge support from Independent Research Fund Denmark.  
**Institutional Review Board Statement:** Not Applicable.  
**Informed Consent Statement:** Not Applicable.  
**Data Availability Statement:** Not Applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Materials and Methods
### Parameter Extraction
#### 4.3.8 Hjorth Descriptors  
A Hjorth descriptor represents a digital signal processing method that offers the statistical parameter values in the time domain [61].  
Signal Activity (SiA)  
The SiA is the first Hjorth descriptor and can be defined as a measure of the squared standard deviation of the amplitude or the variance of a signal (Equation (10)) [62].  
\[\text{SiA}=\sum_{i=1}^{N}\frac{x_{i}-\mu}{N} \tag{10}\]  
where \(x_{i}\) and \(\mu\) represent the amplitude value of the signal and the mean of the signal, respectively. \(N\) represents the number of samples of the signal.  
Hjorth Mobility (HjM)  
The HjM represents the second Hjorth descriptor that provides information about the mean frequency of the signal. It can be expressed using Equation (11) [63].  
\[\text{HjC}=\frac{\sigma_{x}^{\prime}}{\sigma_{x}} \tag{11}\]  
where \(\sigma_{x}\) represents the variance of the signal and \(\sigma_{x}^{\prime}\) represents the 1 st derivative of the variance, respectively.  
Hjorth Complexity (HjC)  
The HjC is the third Hjorth descriptor, which provides an estimation of the bandwidth of a signal. Here, the bandwidth is represented by the ratio of peak value to the harmonic content of the signal. Mathematically, it is given by Equation (12) [62].  
\[\text{HjC}=\frac{\frac{\sigma_{x}^{\prime\prime}}{\sigma_{x}^{\prime}}}{\frac {\sigma_{x}^{\prime}}{\sigma_{x}^{\prime}}} \tag{12}\]  
where \(\sigma_{x}\) represents the variance of the signal, and \(\sigma_{x}^{\prime}\) and \(\sigma_{x}^{\prime\prime}\) represent the 1 st and the 2 nd derivative of the variance, respectively.
## 2 Materials and Methods
### Remote Sensing Data  
For RS data, we utilized a monthly time series of freely available Sentinel-2 (S2) images, ensuring the closest possible alignment with the field data collection period (2020). Sentinel-2 imagery (European Space Agency, ESA, Paris, France) was obtained from the Copernicus Data Space repository ([[https://browser.dataspace.copernicus.eu](https://browser.dataspace.copernicus.eu)]([https://browser.dataspace.copernicus.eu](https://browser.dataspace.copernicus.eu)), accessed on 14 December 2024). The Sentinel-2 constellation, comprising Sentinel-2A (S2A) and Sentinel-2B (S2B), is equipped with a multispectral instrument (MSI) that captures data in 13 spectral bands across the visible, near-infrared, and shortwave infrared regions of the electromagnetic spectrum. Sentinel-2 satellites provide high and medium spatial resolutions (10 m, 20 m, and 60 m) and have a high revisit frequency of five days at the equator [60].  
Since the study area spans two Sentinel-2 grid tiles (T20 JKL, T20 HKK), we downloaded monthly S2 images for each tile (12 images per tile, totaling 24 images) at processing level 2A (Bottom-Of-Atmosphere, BOA). These images are geometrically orthorectified, co-registered, and atmospherically corrected to surface reflectance by ESA using the Sen2 cor processor (version 2.11.0, European Space Agency, Paris, France) [61]. This processor applies atmospheric correction, terrain and cirrus correction, and scene classification to Sentinel-2 Level 1C (Top-Of-Atmosphere, TOA) imagery [61].  
Only S2 images with low cloud coverage (<10%) were selected (S1, Table S1.1), and none of the 49 forest sample sites were affected by cloud cover in any month. All selected images were acquired at the same time (14:16-14:17), with sun zenith angles ranging from 24.361\({}^{\circ}\) to 61.536\({}^{\circ}\). The Sen2 cor processor applies bilinear interpolation using Lambert's reflectance law to minimize errors caused by shadows affecting spectral responses (S1, Table S1.1) [61]. Temporal alignment between RS and field data was ensured, as the measured variables remain stable over short periods in the Arid Chaco [62]. These variables typically do not change significantly within a few years unless extreme events such as wildfires or deforestation occur, none of which were recorded during the study period.  
We used a subset of Sentinel-2 bands to calculate spectral indices, including bands at 10 m spatial resolution (e.g., blue: 490 nm, green: 560 nm, red: 665 nm, and near-infrared [NIR]: 842 nm) and bands at 20 m spatial resolution (e.g., red edge [RE]: 705 nm, Shortwave Infrared [SWIR]: 1610 nm and 2190 nm). All bands were resampled to a 20 m spatial resolution using the nearest neighbor method.
## 3 Data-Driven Solutions Applied for the Detection of NTLs
### Unsupervised Methods
#### 3.2.3 Self-Organizing Map  
Self-Organizing Map (SOM) is an unsupervised algorithm similar to the ANN used for data classification. It generates a two-dimensional representation of the dataset and, in some cases, is used to reduce the dimensionality of the attributes. Another advantage of SOM is its visual presentation, which is easy for users to understand. The final result of the algorithm is groupings (malicious customers or normal customers), which will need to be evaluated by an expert system [4; 16; 66].
# A Survey of GIS and IoT Integration: Applications and Architecture
## 4 Integrated IoT and GIS Architectures
### IoT Architecture and IoT Elements  
Internet of Things has tremendously evolved in the last years and different architectures have been proposed by researchers [1; 159; 160]. There is a three-layer architecture developed in the early stages of these studies [1; 161; 162] which consists of the Perception layer, Network layer, and Application layer. A five-layer architecture is also used by [163; 164; 165]. Perception, Transport, Processing, Application, and Business layers are proposed in this type of architecture. This study considers a five-layer architecture for IoT which is shown in Figure 2.  
The Perception layer consists of sensors for sensing and gathering information about the environment [159]. Depending on the purpose of the system, the type of sensors including RFID, IR sensors, or other network sensors and their collected data may differ. Providing identification and information storage like RFID tags, information collection like sensor networks, and information processing like embedded edge processors is the aim of using these hardware elements [165]. The collected data is passed to the Transport layer also known as the Network layer. This layer acts as the human nerves [166] and is responsible for connecting to the processing system to transmit the information gathered from sensor devices. A wide range of communication networks like wired or wireless ones, the Internet, etc. can be brought into the perception layer. 3G/4G, ZigBee, Wi-Fi, Bluetooth are some of the communication mediums used in this layer. According to the similarity of the Network layer to human nerves, the Processing layer can act as a brain [166]. This Layer is designed to store, analyze, and process the information received from the Perception layer through the Network layer. Direct access to a database to store the information is provided using technologies like cloud and fog computing. The application layer plays the role of an interface between the system and the user. Due to the processed data and the goal of the designed system, this layer is responsible for delivering the appropriate application to the user [165]. The application covers a wide range of cases. The Business layer is responsible for managing the whole IoT system activities by generating different business models [1] and is out of the scope of this study.  
According to Reference [1], the elements of IoT are Identification, Sensing, Communication, Computing, Services, and Semantics. The first element deals with identifying objects uniquely to satisfy their demands. Sensing is responsible for collecting data using various sensors and transferring it to a database often in the cloud. The third element, Communication, consists of technologies that are capable of providing a heterogeneous connection between objects. Computing, comprising of devices and software, processes the gathered data. Services and Semantics deal with acquiring suitable knowledge to present appropriate services.  
Figure 2: The architecture of IoT.
# Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios
###### Abstract  
The advancement of computer vision technology has allowed for the easy detection of weeds and other stressors in turfgrasses and agriculture. This study aimed to evaluate the feasibility of single shot object detectors for weed detection in lawns, which represents a difficult task. In this study, four different YOLO (You Only Look Once) object detectors version, along with all their various scales, were trained on a public 'Weeds' dataset with 4203 digital images of weeds growing in lawns with a total of 11,385 annotations and tested for weed detection in turfgrasses. Different weed species were considered as one class ('Weeds'). Trained models were tested on the test subset of the 'Weeds' dataset and three additional test datasets. Precision (P), recall (R), and mean average precision (mAP_0.5 and mAP_0.5:0.95) were used to evaluate the different model scales. YOLOv8l obtained the overall highest performance in the 'Weeds' test subset resulting in a P (0.9476), mAP_0.5 (0.9795), and mAP_0.5:0.95 (0.8123), while best R was obtained from YOLOv5m (0.9663). Despite YOLOv8l high performances, the outcomes obtained on the additional test datasets have underscored the necessity for further enhancements to address the challenges impeding accurate weed detection.  
digital image analysis; manilagrass; bermudagrass; ryegrass; YOLO +
Footnote †: journal: _Applied Sciences_
## 3 Methodology
### Determination of Landscape Composition with Land Cover Classification  
In this study, the freely available global LULC map produced by Impact Observatory and Esri was used [69]. Sentinel-2 satellites are very useful for LULC mapping due to their high spatial, spectral, and temporal resolutions. Advances in deep learning and scalable cloud-based computing provide the analytical capability needed to realize the value of global satellite imagery observations. The developed LULC map is based on deep learning segmentation with a new and very large dataset of Sentinel-2 pixels. This model is a 10 m resolution global LULC map that enables automatic LULC mapping from time series observations and achieves state-of-the-art accuracy [70].  
After obtaining the LULC digital map of the region from the [[https://livingatlas.arcgis.com/landcoverexplorer](https://livingatlas.arcgis.com/landcoverexplorer)]([https://livingatlas.arcgis.com/landcoverexplorer](https://livingatlas.arcgis.com/landcoverexplorer)) (accessed on 15 September 2024) website, subset operations were applied according to the sub-basin boundaries using ArcGIS Pro 3.2.0 Then, the stage of determining the landscape composition, which can be defined as the integrity formed by the combination of natural and artificial elements, was started. Landscape composition refers to the diversity of patch types within a landscape, usually measured using the proportions of different land cover types [71]. In determining the landscape composition, natural elements (water resources, vegetation, bare land, etc.) interact with cultural elements (agricultural areas, settlements, etc.) to create integrity [72]. Therefore, six classes of land cover types (water surface, settlement patch, green patch, agricultural patch, bare land, and sand/gravel) belonging to natural and artificial elements were identified for the study area, and landscape composition was defined (Figure 3).  
Figure 3: Landscape composition of Bogacay sub-basin.  
A natural lake with an area of approximately 35 ha is located on a tributary in the northwest of the sub-basin and is classified as a water surface. River and sea are excluded from the water surface class. The areas defined as green patches in the landscape composition are mostly forests and maquis. Also, urban green areas within the city are defined as green patches. The sand/gravel class, mostly bounding the riverbed, was formed by the deposition of sediments carried by floods in the past. The settlement patch is mostly made up of multi-story buildings, while bare land indicates barren terrain where no vegetation grows. Agricultural patches are areas around the riverbed where perennial crops (usually _Citrus sp._) are grown.
## 4 Results and Discussion
### Axial Dynamic Mechanical Characteristics of the Air Spring
#### 4.1.1 Influence of Frequency Excitation on Axial Load Characteristics of Air Spring  
The axial load behavior of an air spring under a constant initial pressure of 0.3 MPa was evaluated in response to sine amplitude excitations of 0.5 mm, under varying frequency conditions. The results of the analysis, covering a minimum of 10 cycles selected across different frequency excitations, are presented in Figure 7.  
The results of the axial load characteristics of the air spring under an initial pressure of 0.3 MPa and a 0.5 mm sine amplitude excitation are depicted in Figure 7. The figure demonstrates that changes in frequency can have a notable effect on the axial load of the air spring, particularly at frequencies below 1.0 Hz, with the most pronounced effect at 0.5 Hz and 0.25 Hz. With the progression of vibration time, a linear decrease in the maximum peak value of the axial load of the air spring was observed, implying that the axial load at low frequencies decreases over the vibration process. A load peak value increase followed by a decrease with increasing time was observed for the axial load of the air spring when the vibration frequency was 0.75 Hz. On the other hand, when the excitation frequency increased to 1.0 Hz, the axial load of the air spring remained consistent with no significant  
Figure 7: (**a**) Load change when the vibration frequency is 0.25 Hz; (**b**) load change when the vibration frequency is 0.25 Hz; (**c**) load change when the vibration frequency is 0.75 Hz; (**d**) load change when the vibration frequency is 1.0 Hz.  
variations, signifying that lower frequency vibrations can have a significant impact on the axial load characteristics of air springs.