esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Contralateral Cervical Glide on the Suprascapular Nerve: An In Vitro and In Vivo Study
## 2 Materials and Methods
### In Vivo Study
#### 2.2.3 Data Processing  
The movement of the SNe was analyzed using the trajectory tracking function of the KINOVEA (Kinovea-2023.1.2) program, a reliable free software tool for linear measurements that digitalizes the x- and y-axis coordinates [46]. This program has shown usefulness in previous studies [41]. Three tracking points were selected for each analyzed video: two on the SNe (movement points) and one outside the nerve (control point). Before deeming the chosen points valid, the whole video was examined by two researchers to ensure there were no major tracking mistakes. If a monitored point shifted beyond the chosen structure, the selection procedure was repeated until suitable points were obtained. A total of 240 position records were obtained for each of the three points studied on the horizontal and vertical axis. The difference in mm of each position relative to its initial position was calculated. As it was not possible to completely align the ultrasound head longitudinally on the SNe course, small positional fluctuations situated consistently within the nerve outline were indeed detected. Possessing 240 position records for each point was deemed adequate to minimize the effects of these tracking errors. Also, to estimate the movement, a trimmed mean excluding the 5% of extreme values of all the displacements registered for each point was calculated.
## 2 Materials and Methods
### Study Sites and Soil Characteristics
Soil analyses were performed according to the standardized methods: pH was determined potentiometrically in aqueous suspension (10 g of soil/25 mL of bi-distillated water, with a combined pH electrode glass-calomel) [40,41]. Granulometric analysis was performed using the Kacinski method [42]. The content of carbonates was determined by [PERSON]'s method, according to the European Standard SR EN ISO 10693:2014 [43]. As shown in Table 1, at the H site a cambisol soil was identified, whereas the soil types from F and S sites were cambisol and luvisol, respectively. Among the main differences of the soils were the pH values, indicating a higher acidity in F and S, the moisture content which was slightly higher in H and the characteristics of the granulometric distribution. On average, the soils could be characterized by a pH value of 6.14 \(\pm\) 1.28, a moisture content value of 29.04 \(\pm\) 6.09%, a hygroscopicity value of 8.03 \(\pm\) 1.41% and shares of sand, silt and clay of 17.54 \(\pm\) 14.09%, 13.40 \(\pm\) 5.26% and 38.12 \(\pm\) 6.60%, respectively. Close to the soil profiles, rectangular testing areas were delimited by placing physical ground guiding marks (g) in the form of white plastic spheres supported by sticks inserted into the ground. These testing areas were perfectly straight, located on flat ground and about 20 m in length each. The ground guiding marks were placed in straight lines with the aim to guide the drivers when passing over the testing ground. Figure 1c shows the basic procedures used to place the ground guiding marks, Figure 1d shows a testing area after a couple of skidder passes and more details on the experimental design of the testing areas are given in Section 2.2. Figure 1: Study sites and description of the extraction means: **(a)**–location of the sites at national level, **(b)**–description of the extraction means, **(c)**—an example of preparing a sampling area, **(d)**—an example of the sampling area after a few passes, **(e)**—an example of soil sampling pit used to characterize the main properties of the soil in the sampling area. Note: graphical schemes used to describe the means shown in panel **(b)** are taken from the learning resources of the Technodiversity project [1].
# A Proposal of Printed Table Digitization Algorithm with Image Processing
## 3 Adopted Technologies
### Tesseract  
_Tesseract_ is open-source software to recognize the characters in an image file. It can be executed from the command-line interface. For this algorithm, _Tesseract_ is imported into _Python_ through the _pytesseract_ package. After using _OpenCV_ to divide the table into a collection of cells, _Tesseract_ will recognize the characters in each cell and return the digitalized result.
## 2 Materials and Methods
### Photocatalytic Degradation Experiments  
The degradation tests were carried out in a solar light simulator Sun Test CPS+ system. Further details of the applied system can be found in [23]. First, 60 mL of a DCF solution (\(c\) = 25 mg/L) was poured into the photoreactor. To this solution, 30 mg of catalyst was added, and the reactor was closed and placed in the exposure chamber. Before the xenon lamp of the Sun Test CPC+ was turned on, the suspension was stirred in the dark for 30 min to retain the adsorption-desorption equilibrium. After turning on the light, samples were taken from the reactor at 30 min intervals and analyzed using the HPLC method. After 120 min of irradiation, the experiment was terminated, and the reaction mixture was separated from the photocatalyst by means of centrifugation. The relative DCF concentration c\({}_{t}\)/c\({}_{0}\) was calculated from the HPLC area of DCF in the starting solution (\(A_{0}\)) and the DCF area obtained after a reaction time t (\(A_{t}\)) using Equation (2).  
\[\frac{c_{t}}{c_{0}}=\frac{A_{t}}{A_{0}} \tag{2}\]  
The total organic carbon (TOC) content of the reaction mixture was then determined by using a TOC analyzer (multi-N/C 3100, Analytic Jena, Jena, Germany). The degree of mineralization was obtained by using the total organic carbon (TOC) content in the final reaction mixture and in the starting solution. The photocatalytic mineralization efficiency (TOC removal) was calculated using Equation (3), whereas \(C_{0}\) and \(C\) are the initial and the organic carbon content after 120 min of irradiation (mg/L), respectively.  
\[\text{TOC removal}(\%)=\frac{(C_{0}-C)}{C_{0}}\cdot 100\% \tag{3}\]The reusability test was conducted under the same conditions as described above. After 120 min of irradiation, the TiO\({}_{2}\) was separated from the aqueous phase by means of centrifugation and then washed with water. This step was repeated four times. The TiO\({}_{2}\) photocatalyst was then dried in an oven at 80 \({}^{\circ}\)C for 17 h, ground, and used for a new experiment. The TOC content of the aqueous reaction solution was determined immediately after separation from the photocatalyst.
## 2 Materials and Methods
### Thyroid Cancer  
Thyroid cancer (TC) is one of the most common cancers in AYA, especially in 15-24-year-old patients [133]. In this age group, TC accounts for approximately 13% of all malignant neoplasms [230]. A significant increase in the incidence of TC has been observed in recent decades [231]. There are four histological types of TC: papillary, follicular, medullary, and anaplastic. They can develop de novo or in individuals previously treated for cancer. Medullary TC can also be present in hereditary syndromes like type 2 multiple endocrine neoplasia (MEN) syndromes MEN2A, MEN2B, and the related multigenerational familial MTC (FMTC) syndrome.  
The overall survival rate of TC is one of the highest among AYA cancers [233; 234], but in anaplastic TC it is still unsatisfactory [235]. For this reason, there is a continuing need to understand the biology of thyroid cancer, find clinically useful biomarkers and develop novel therapies.  
One of the promising options for therapeutic targets in TC are Hsps, which are overexpressed in thyroid malignancies. Many studies reported altered expression of various Hsps, including Hsp27 [236; 237], Hsp60 [238], Hsp70 [239; 240; 241; 242], and Hsp90 [243; 244; 245; 246]. There were studies that showed that KU711 and WGA-TA, inhibitors of Hsp90, decrease migration and invasion of anaplastic TC cells in vitro. Moreover, these molecules lead to downregulation of \(\beta\)-catenin, BRAF, Akt, and phospho-Akt, leading to induction of apoptosis [247; 248; 249]. [PERSON] et al. showed that apoptosis of anaplastic TC cells can be induced not only by inhibition of Hsp90, but also by Hsp70 inhibitors [248]. However, data on the role of exosomal Hsps in TC cells is scarce.  
To our knowledge there is only one study on exosomal Hsps in TC. [PERSON] et al. for the first time reported significantly higher levels of Hsp27, Hsp60, and Hsp90 in tissue of papillary TC than in peritumoral tissue and non-toxic goiter. Moreover, the same Hsps were increased in exosomes obtained from plasma in patients with papillary TC. Furthermore, it was noticed that exosomal levels of Hsp27, Hsp60, and Hsp90 were higher before ablative surgery than after this procedure. These results suggest that exosomal Hsps play a role in carcinogenesis and may have clinical implications for the prognostic analysis and treatment of patients with TC. However, in the same study, no differences in Hsp70 level were found in thyroid cancer tissue and benign goiter. This is why the evaluation of Hsp70 in exosomes was not done [101]. The results obtained by the authors are interesting, but further studies are needed.
## 5 Open Issue
To address this, future research could focus on lightweight clusteringalternatives or intermittent GAN training cycles. This approach could balance computational demands and energy preservation, enabling the deployment of GAN-ACO in resource-constrained environments. **Scalability in Large-Scale and Dense Network Deployments:** As WSNs expand in size, congestion control strategies must maintain performance and scalability. Current results show that GAN-ACO performs effectively in medium-sized networks; however, in larger deployments, the clustering and pheromone update processes may introduce delays. Implementing a hierarchical clustering framework using GANs, where clusters operate semi-autonomously and aggregate data to a central unit only when necessary, could help in managing high data loads. This hierarchy would reduce the overhead on central nodes and allow for more efficient data processing across large networks. **Security and Robustness Against Adversarial Attacks:** WSNs deployed in exposed environments are often susceptible to attacks that compromise data integrity. The GAN-ACO strategy, reliant on clustering and pheromone-based routing, may be vulnerable to attacks such as forged pheromone updates or adversarial clustering. Ensuring robust, secure data paths could be achieved by integrating blockchain for data validation or introducing anomaly detection layers to prevent malicious rerouting or clustering manipulation. A future criterion for this enhancement would be to evaluate robustness by simulating adversarial conditions and measuring performance. **Criteria for Future Directions and Evaluation**  
Evaluating the future potential of GAN-ACO in WSNs requires a set of well-defined criteria:  
**Energy Efficiency:** Evaluate total network energy consumption under various traffic loads and conditions, with special attention to prolonging network lifetime in low-energy environments. **Latency:** Testing latency across dynamic network topologies would provide valuable insights into GAN-ACO's ability to adapt to increased data flow without introducing delays. Evaluating this technology within 5G and 6G networks could further demonstrate its performance in high-speed, high-capacity environments, showcasing its suitability for modern, demanding network standards. **Scalability:** Evaluating the method's performance in larger WSN deployments with multiple BSs can provide deeper insights into its scalability. Implementing hierarchical clustering across these BSs would help balance the network load, minimizing congestion and enhancing data transmission efficiency as the network grows. This setup allows for more resilient and efficient data handling, particularly in expansive WSN environments. **Security Robustness:** Develop metrics to assess resilience against network attacks, including data integrity and reliability under adversarial conditions.
# Vehicle Lateral Control Based on Augmented Lagrangian DDPG Algorithm
## 3 Problem Formulation
### The Vehicle Trajectory Error System  
The vehicle dynamics model can convert the vehicle's underlying motion characteristics into a quantitative problem of high-level control objectives, i.e., vehicle trajectory tracking accuracy problem. In the control architecture of a four-wheel steering vehicle, control commands and trajectory tracking performance are dynamically coupled through a multi-level state transmission chain.  
Specifically, the four-wheel steering angles \(\delta_{i}\) serve as control inputs, first acting on the vehicle's physical system through the 6 DOF dynamic model: the lateral force of the tires varies with the steering angle, driving the lateral acceleration \(\dot{v}_{y}\) and yaw acceleration \(\dot{w}\), which in turn update the vehicle's motion state \((v_{y},w)\). Therefore, establishing a vehicle trajectory tracking model based on the vehicle dynamic model effectively addresses the trajectory tracking problem, as demonstrated in Figure 2.  
The trajectory tracking error model under single preview-point configuration is derived through kinematic analysis and dynamic coupling. Let \(L\) denote the preview distance ahead of the vehicle's center of gravity. The lateral position error \(y_{L}\), defined as the perpendicular deviation between the vehicle's Center Of Gravity and the reference path, evolves according to three contributions: (1) longitudinal velocity \(v_{x}\) projected laterally due to  
Figure 1: 6 DOF model of a four-wheel vehicle.  
heading misalignment \(\varepsilon_{\mathrm{L}}\), (2) direct lateral velocity \(-v_{y}\), and (3) yaw-induced displacement \(-\omega L\). This yields the lateral error dynamics:  
\[\dot{y}_{\mathrm{L}}=v_{x}\varepsilon_{\mathrm{L}}-v_{y}-\omega L.\]  
Concurrently, the heading error \(\varepsilon_{\mathrm{L}}\), representing the angular deviation from the path tangent, is governed by the discrepancy between the reference curvature \(\rho\) and the vehicle's yaw rate \(\omega\):  
\[\dot{\varepsilon}_{\mathrm{L}}=v_{x}\rho-\omega.\]  
The dynamic equation of the vehicle trajectory tracking model with preview distance is as follows:  
\[\begin{bmatrix}\dot{y}_{\mathrm{L}}\\ \dot{\varepsilon}_{\mathrm{L}}\end{bmatrix}=\underbrace{\begin{bmatrix}0&v_{x} \\ 0&0\end{bmatrix}}_{A}\begin{bmatrix}y_{\mathrm{L}}\\ \varepsilon_{\mathrm{L}}\end{bmatrix}+\underbrace{\begin{bmatrix}-1&-L&0\\ 0&-1&v_{y}\end{bmatrix}}_{\mathsf{B}}\begin{bmatrix}v_{y}\\ w\\ \rho\end{bmatrix}. \tag{5}\]  
The vehicle trajectory tracking model introduces preview distance, which introduces the curvature information of the vehicle trajectory through the preview distance, solving the dynamic tracking problem of the vehicle trajectory.
## 2 Materials and Methods
### Identification of Agricultural Drought Years
#### 2.7.1 Spain  
In Spain, the critical month for crop yield was determined by performing a Pearson correlation analysis between wheat and barley yields and the agricultural drought index from 2001 to 2020, thus obtaining a monthly R value during the phenological cycle of wheat and barley. The analysis was performed at the provincial level, and subsequently, the most frequent critical month in each region was extracted, considering the month with the highest correlation as critical.  
Once the critical month for CL and CM was obtained, it was used to identify the drought years. One of the most commonly used methods for drought detection is based on the definition of a threshold level below which drought is said to have occurred [77]. This approach was applied in the present work, using a percentile as a threshold. A percentile is defined as the value that divides a linearly ordered dataset, so that it indicates the value below which a percentage of the dataset is equal to or less than that value [78]. A common value adopted in the literature to detect agricultural drought is the 20 th percentile [79; 80; 81]. Accordingly, the 20 th percentile was used as the threshold level, below which the onset of agricultural drought was defined.  
In addition, three approaches were studied for wheat and barley with different time periods as the criteria to identify drought years. The critical month (M) and two (2M, the critical month plus the next or the previous month) or three (3M, the critical month plus the previous and next months) months around the critical month were used. Thus, for the identification of drought years for wheat and barley crops in each Spanish region and for each year of the study period, the threshold level was applied to the three approaches.
# New Compact Wearable Metamaterials Circular Patch Antennas for IoT, Medical and 5G Applications
## 1 Introduction
### Main Applications of Wearable Medical Systems  
* Wearable Medical devices may help to monitor hospital activities;
* Wearable devices may help to operate and monitor home accessories to help diabetes patients, asthma patients, epilepsy patients and assist in solve cardiovascular diseases;
* Wearable devices may help to operate and monitor IoT devices.  
Moreover, wearable antennas may be used in IoT devices. The internet of things (IoT) is a system of interrelated computing devices, mechanical and digital machines, personal devices, objects, animals, peoples, healthcare devices and wireless communication devices that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human to human or human to computer interaction. The internet of things helps people everyday life, work smarter and get complete control over daily services and procedures. IoT provides smart devices to automate homes, companies and health care centers. IoT is essential to business. IoT provides businesses with a real time observation and inspection how their companies' systems really work. IoT enables companies to automate processes and reduce labor costs. An IoT ecosystem consists of web-enabled smart devices that use embedded processors, sensors and communication hardware to collect, send and act on data they acquire from their environments. IoT devices share the sensor data they collect by connecting to an IoT gateway or other edge device where data is analyzed locally or sent to the cloud to be analyzed.  
In this paper, meta-materials are employed to develop circular patch antennas with high efficiency for medical, IoT and 5G communication systems. Detail design of several types of metamaterial antennas with Circular Split-Ring Resonators (CSRRs) was presented by the author in [2; 3; 4]. Circular patch meta-material antennas were not presented in journals. However, circular patch meta-material antennas have several advantages over regular square and rectangular patches. Such as symmetry, ease to generate circular polarization and propagation mode. The computed and measured bandwidth of the stacked circular patch wearable antenna with SRR for IoT and medical applications is around 12%, for VSWR, Voltage Standing Wave Ratio, better than 3:1. The gain of the stacked circular patch with CSRR is around 8.5 dB and with 95% efficiency.  
The progress in development of small antennas is presented in Figure 1. Figure 1a presents the gain of several small antennas. Figure 1b presents the bandwidth of several small antennas. The values presented in Figure 1 are based on data and results presented in [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40].
## 7 Discussion  
Fog computing can be seen as a supplement to cloud computing. By extending the cloud and capitalizing on it, we were able to reduce the download time by relying on the segments existing on the fog nodes to stream videos and only using the cloud when the required segments are missing. Computation is done on multiple fog nodes, reducing the processing effort and allowing the placement of the segments in closer locations, therefore decreasing the latency. The DASH protocol used in the fog node breaks up the videos into small segments available in different qualities and allocates them to the fog nodes, allowing a better quality and boosting the performance of the system. The results show reduced latency, improved quality and enhanced system performance by using fog-computing-assisted DASH following different algorithms. The static allocation algorithm distributes the segments of the videos over the fog nodes in an arbitrary way and was used as a basis for the comparison with other algorithms. The marginal gain maximization algorithm rearranges the assignment of the segments of the videos through the fog nodes using the segments' marginal gain and ranking them. This has improved the QoE by ensuring less buffering and less download time. The downgrade algorithm moves the segments to a lower quality if they cannot meet the requirements to avoid buffering by grouping segments of similar qualities and inspecting them, which has improved the QoE by eliminating buffering and reducing the download time. The optimal buffering algorithm allows us to allocate the segments so that the under-buffering is minimized while always respecting the required quality by the users.  
Conceptualization, D.T.; methodology, D.T.; software, M.N. and D.T; validation, M.N., A.B. and D.T.; investigation, M.N., A.B. and D.T.; data curation, M.N. and D.T.; writing--original draft preparation, M.N.; writing--review and editing, A.B. and D.T.; supervision, D.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 4 Findings
### Management Systems  
In any IoT ecosystem, it is crucial to compute the assets (operations, users, devices, etc.) and their provenance. Thus, a secured ID management system based on blockchain technology is proposed to tackle current security and privacy issues [131]. The authors developed a proof-of-concept prototype in a business case scenario. This prototype consists of three stages; the first stage discusses how the data can be managed and modeled to be stored securely on the blockchain. The second stage explains different rules developed using smart contracts for any agreement and monitoring these rules while the core transactions are also being processed. The third stage manages identity verification to address security and privacy aspects. Four types of smart contracts' rules are used: computer ID management, software ID management, user ID management, and data backup ID management. The proposed prototype can be further explored for large-scale businesses in terms of adaptability and extendability.  
SmartDID [132] is a distributed identity management system proposed due to a lack of a systematic proof system and issues in IoT, such as privacy, security, and resource limitations. The authors constructed a distributed identity system to support certain features, for example, supervisability, unlinkability, and Sybil attacks resistance. The authors considered the following: to hide privacy information, cryptographic credentials and plaintext are used to create a dual-credential model. In addition, a zero-knowledge scheme is used as a verification mechanism, and a commitment scheme is used for encryption to secure cryptographic credentials. However, there is a possibility of a Sybil attack, and such methods are unable to hide attribute linkage. Thus, a distributed system with multiple pseudonymous userIDs and one unique masterID is designed to address the mentioned issues. The system model is comprised of supervisors, verifiers, issuers, holders, and a consortium blockchain. IoT devices can act as verifiers to verify credentials and publish access policies, while the committee acts as a credential issuer to sign and issue credentials. SmartDID was evaluated and compared with two other approaches in terms of proof generation and credential generation times, and SmartDID achieved the best performance.