esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cadaveric Study of Ulnar Nerve Movement and Strain around the Elbow Joint
## 1 Introduction  
Peripheral nerves need to glide and stretch to adjust to the changes in nerve travel associated with joint movement [1]. Fifteen percent elongation of a peripheral nerve reduces the nerve blood flow by 80% [2]. In addition, prolonged strain on the nerves causes irreversible nerve conduction disorders [3]. Flexion of the elbow joint increases the distance traveled by the ulnar nerve [1]. The ulnar nerve stretches during elbow flexion, and the overstrain of the nerve may be involved in the development of cubital tunnel syndrome [4; 5; 6]. Furthermore, amateur and professional baseball players frequently experience cubital tunnel syndrome [7]. There have been many studies on ulnar nerve sliding and strain using cadavers [1; 5; 6; 7; 8; 9; 10; 11; 12]. [PERSON] et al. measured ulnar nerve strain at the posterior medial epicordyle of the humerus during different daily activities and reported that daily activities did not cause permanent damage to the nerve [12]. However, ulnar neuropathy has similarly been reported to occur in the arcade of Struthers, which is far from the cubital canal [13]; nonetheless, there is a lack of data on how ulnar nerve strain varies according to the location around the elbow joint. The establishment of normal ulnar nerve movement is important for it to be compared with movements in painful conditions, such as cubital tunnel syndrome and traumatic nerve injury, that affect the ulnar nerve.  
We hypothesized that nerve strain would increase at a location closer to the elbow joint and that nerve movement would be larger in the proximal site of the elbow joint than in the distal site. In this study, we measured the longitudinal movement of the ulnar nerve around the elbow joint during elbow motion using fresh-frozen cadavers.
## 2 State of the Art  
### Fermentation Monitoring  
Few studies deal with the topic of monitoring the fermentation state of dough pieces. [PERSON] et al. propose a non-destructive method for real-time information gaining of changes in the structure of dough during the fermentation using low-intensity ultrasonic waves [9]. Utilizing that, a correlation between the fermentation time and the ultrasonic velocity and attenuation can be observed. Increasing fermentation time leads to an increasing attenuation due to the density change of the dough, and hence leads to a decreasing speed of the ultrasonic waves. [PERSON] et al. describe a sensor that generates low-frequency acoustic waves through an oscillating piezoelectric element to validate the kinetics of bread dough during fermentation [10]. An emitter and a receiver are brought into contact with a piece of dough, and the attenuation of the emitted acoustic signal that changes due to the formation and growth of gas bubbles during the fermentation is measured. By means of that method, the influence of different process parameters such as temperature and humidity can be observed. Both of these proposed methods have the disadvantage of being restricted to only one dough sample at a time and hence being inappropriate for the control of a whole batch of fermenting bread without human supervision. [PERSON] et al. use magnetic resonance microscopy for the continuous control of dough fermentation and bread baking [11]. The proposed method delivers good results for the monitoring of dough pore distribution and dough volume regarding one dough piece but can theoretically be scaled up to more dough pieces at a time laying in one plane. A significant disadvantage is the complex measurement setup and the missing possibility for being used to upgrade existing fermentation chambers. [PERSON] et al. propose an optical method of continuous fermentation state monitoring using a 3D vision system composed of a line laser and a camera and installed inside a fermentation chamber [4]. By means of this method, the height and transversal area of only one dough sample can be measured, and thus the fermentation state controlled. [PERSON] et al. use a digital imaging method to monitor the dynamic density of dough during fermentation [12]. The fermenting dough is taken out of the fermentation chamber to take a picture with a camera positioned orthogonal to the object. With that method, conclusions about the density by means of the calculated volume of the dough can be drawn. The technique is invasive and not practicable for multiple samples.  
A restriction of all existing methods is the limitation to be only applicable in one plane. Standard fermentation chambers consist of multiple layers of metal sheets containing many dough pieces. The monitoring of one sample and even of one layer representatively is not sufficient because parameters like temperature and humidity can vary at different areas within the fermentation environment, leading to a different fermentation mellowness.
# Antioxidant and Antimicrobial Properties of Selected Phytogenics for Sustainable Poultry Production
## 4 Selected Plant Sources of Antimicrobial and Antioxidant Compounds
### Allium sativum L.  
Garlic (_Allium sativum_ L.) is a bulbous plant that belongs to the _Anaryllidaeeae_ family [79]. It is believed to have originated in Asia, although it is now utilized throughout the world [80]. Garlic is globally used as a nutraceutical, containing carbohydrates, protein, amino acids, lipids, fiber, vitamins, minerals, organic acids, saponins, phenolic compounds, and a large group of organo-sulfur compounds [81], along with phytochemicals (saponins, tannins, alkaloids, and flavonoids) [82]. Additionally, garlic has enzymes, minerals, vitamins [83], and moderate sulfur content but little B vitamin [84]. Garlic bioactive compounds are organosulfur compounds, which are responsible for its smell, flavor, and antioxidant and medicinal properties [85]. Sulfur compounds, such as trisulfides, thiosulfates, tetrathiol, sulfates, scordinine, pseudoscordinine, methyl sulfides, methionine, glutathione, disulfides, dimethyl sulfides, diallyl sulfides, cystine, cysteine sulfoxides, cysteine, cycloallin, allyl trisulfides, alkyl sulfides, allyl disulfides, allin, allicin (thiosulfonate), and ajoene, have all been reported in garlic [80,86].  
Other reported phytochemicals are flavonoids (flavones and quercetin) [87], phenolic compounds, saponins, and sapogenines [88]. These bioactive compounds have antibacterial, hypo-cholesterol, antioxidant, and growth-promoting qualities, which are beneficial to livestock and poultry [83]. For example, the addition of 0.5 kg/ton garlic meal in diets as an alternative to antibiotics promoted the high live weight in broilers [89]. According to [PERSON] et al. [90], supplementing a standard diet with 3% garlic powder promoted higher body weight gain, higher feed intake, and an improved feed conversion ratio, along with higher dressing and breast percentages in broilers.
## 2 Mission Overview  
The primary objective of the RSONAR II mission was to collect an improved SSA dataset compared to RSONAR. The stratospheric balloon platform, part of the STRATOCIENCE 2023 campaign, was utilized as a quicker and simpler approach compared to a space-based mission [12]. The project spanned 10 months from concept design to flight. This timeline facilitated the acquisition of a large RSO image dataset from ground observation, which was utilized for hardware testing and algorithm design, and to improve our SSA algorithms prior to a CubeSat launch to Low Earth Orbit (LEO). The camera acquired images in varying resolutions. The secondary objective of the mission was to demonstrate real-time RSO detection and attitude determination. A second camera system has been developed specifically for this functionality. The payload features two additional cameras that were developed for non-SSA purposes, which will be discussed in future publications. RSONAR II has multiple improvements over RSONAR. RSONAR only had one main image capturing system but RSONAR II contained two for SSA purposes. The first system in RSONAR II retained the same sensor and board used in RSONAR, but improvements to the image capturing algorithm were implemented to allow for more efficient image capturing. These improvements include altering detector resolution in-flight to collect images of different quality in addition to addressing an issue with image delays that caused RSO sequences captured from RSONAR cut-off mid-streak. Additionally, downlink communications were added to allow for health data and some images from the data collection camera to be viewed from the ground during the mission. Another major improvement to RSONAR II was adding a dual-purpose system for real-time onboard attitude determination and RSO detection extending our capabilities in the field of SSA.
# Intelligent End-Edge Computation Offloading Based on Lyapunov-Guided Deep Reinforcement Learning
## 4 End-Edge Computation Offloading Based on Lyapunov-Guided DRL
### Problem Formulation  
In order to achieve end-edge computation offloading for complex industrial tasks, we formulate the network computation rate maximization problem, assuming that each industrial terminal generates only one indivisible real-time task \(S_{n}\). The set of tasks is represented as \(S=\{S_{1},S_{2},\ldots,S_{N}\}\), where each task is denoted as \(S_{n}=\{d,\tau,f,e\}\). In detail, \(d_{m}=\{d_{n,1},d_{n,2},\ldots,d_{n,M}\}\), \(\tau=\{\tau_{1},\tau_{2},\ldots,\tau_{N}\}\), \(f=\{f_{1},f_{2},\ldots,f_{N}\}\), \(e=\{e_{1},e_{2},\ldots,e_{N}\}\).  
Then, we formulate the network computation rate maximization problem \(P_{1}\) as  
\[\max_{d,\tau,f,e}\lim_{K\to\infty}\frac{1}{\kappa}\sum_{t=1}^{K} \sum_{n=1}^{N}\sum_{m=1}^{M}\beta_{n}z_{n,m} \tag{8}\] \[\text{s.t.}\quad C_{1}:=\sum_{n=1}^{N}\tau_{n}\leq 1,\] \[\quad C_{2}:d_{n,m}\in\{0,1\},\] \[\quad C_{3}:f_{n}\leq f_{n}^{\max},\forall n,\] \[\quad C_{4}:P_{n}\leq P_{n}^{\max},\forall n,\] \[\quad C_{5}:e_{n}\leq P_{n}^{\max}\tau_{n},\forall n,\] \[\quad C_{6}:\sum_{m=1}^{N}\bigg{(}\frac{(1-d_{n,m})f_{n}\tau_{n} \tau_{n}}{\phi}+d_{n,m}W\text{log}_{2}\bigg{(}1+\frac{e_{n}h_{n,m}}{\tau_{n}N_ {0}}\bigg{)}\tau_{n}T\bigg{)}\leq Q_{n}(t),\forall n,\] \[\quad C_{7}:\lim_{K\to\infty}\frac{1}{\kappa}\sum_{t=1}^{K} \mathbb{E}[\mathcal{Q}_{n}(t)]<\infty,\forall n,\] \[\quad C_{8}:\lim_{K\to\infty}\frac{1}{\kappa}\sum_{t=1}^{K} \mathbb{E}[(1-d_{n,m})\kappa(f_{n})^{3}\tau_{n}+d_{n,m}e_{n}]<\xi_{n},\forall n,\]  
where \(\beta_{n}\) is the weight for \(n\)-th industrial terminal, \(\xi_{n}\) is power threshold. \(C_{1}\) is the computation offloading time ratio constraint, which means the ratio sum of computation offloading time should not exceed one. \(C_{2}\) is the binary computation offloading decision constraint. \(C_{3}\) is local CPU frequency constraint, which means there is a maximum value \(f_{n}^{\max}\) for the local CPU frequency. \(C_{4}\) is the transmit power constraint, which means there is a maximum value \(P_{n}^{\max}\) for the transmit power. \(C_{5}\) is the energy consumption constraint used for data offloading, which means there is a maximum value \(P_{n}^{\max}\tau_{n}\) for the energy consumption. \(C_{6}\) is the data causality constraint. \(C_{7}\) is the data queue stability constraint, which means time queue is strongly stable if the average queue length is limited. \(C_{8}\) is the average energy consumption constraint, which means there is a maximum value \(\xi_{n}\) for the average energy consumption.
# The Design and Deployment of an End-To-End IoT Infrastructure for the Natural Environment
## 3 Related Work
Sensor networks have also been used to detect and monitor river flooding [25], with the capability of integrating remote fixed-network grids for computationally intensive flood-modelling and also performing on-site grid computation. Also in the natural environment, the GreenOrbs [26] project is an extremely large deployment of sensor nodes in a forest environment to help understand canopy coverage over time. SensorScope was an outdoor wireless sensor network deployed on top of a rock glacier in Switzerland for environmental monitoring in harsh weather conditions [27]. The researchers deployed 16 sensing stations on a \(500\times 500\) m area for a couple of months to measure air temperature and humidity, surface temperature, incoming solar radiation, wind speed and direction, and precipitation. They opted for a Shockfish TinyNode sensor more platform composed of a Texas Instruments MSP430 microcontroller running at 8 MHz and a Semtech XE1205 radio transceiver. They designed and implemented the communication stack from scratch for their stations with TinyOS. SensorScope mainly aimed at providing a low-cost, efficient, and reliable WSN with an emphasis on improved data collection techniques and, to a great extent, deployed successfully in outdoor harsh environments. [PERSON] et al. [28] reported experiences learned from their wireless sensor networks deployed over six years to understand the natural and agricultural environments and the major challenges they pose in Australia. They developed and deployed sensor networks for different applications such as cattle monitoring, ground water and lake water quality monitoring, virtual fencing, and rainforest monitoring, highlighting the technological challenges they faced, which technology they used for each application, and what lessons they learned from each deployment. All of the above work suggests a strong need for Environmental Internet of Things developments, with high interest from multiple different domains of science. However, work to date remains relatively tightly focused on single dimensions of the environment, lacking a broader view that can fuse data across multiple scientific domains to build a holistic environmental perspective. Further to this, we note that a large proportion of the above works are technology-oriented and often fail to deeply involve scientists in the domain of study throughout the design process. In contrast to these two points, our work takes a multidimensional perspective on an environmental IoT deployment and has deeply involved soil scientists, hydrologists, and animal behaviour scientists from the first stages of design through to actual deployment and maintenance.
## 5 Numerical Examples
### A Nonlinear RC Ladder Circuit
The three output signals are illustrated in the left plot of Figure 6, i.e., that of the original nonlinear system in (61), and the outputs of the reduced-order BTI and QBTI models  
Figure 5: The singular value decay of the Loewner matrices of the BTI and QBTI reduced systems (**left**) and the poles of the reduced-order BTI and QBTI models (**right**). computed via the Loewner approach. As can be seen, both of the latter two are in good accordance with the original output. Additionally, in the right plot of Figure 6, we also depict the magnitude of the absolute approximation errors between the output of the original nonlinear model and the outputs of the two reduced-order systems. As can be clearly observed, the QBTI model provides a far better approximation quality that that of the BTI. This is indeed to be expected, since the QB reformulation of analytic nonlinear systems is exact, while Carleman's linearization introduces errors in the enforced truncation.
# An Overview of Intelligent Transportation Systems in Europe
## 1 Introduction
### Structure of the Paper  
The main issues addressed in this article, which represent the aims of the review, are presented in Figure 2.  
The objectives of this study are as follows:  
* To examine the historical evolution of Intelligent Transport Systems (ITSs) and assess the impact of Directive 2010/40/EU on the European legislative framework, with a focus on how regulations and policies have supported ITS implementation;
* To identify the essential components of ITSs and analyze their role in optimizing transport, particularly technologies such as automation, artificial intelligence (AI) and vehicle-to-infrastructure (V2I) communications;
* To analyze the main European initiatives that support ITS development and evaluate their influence on road safety, urban mobility and environmental sustainability;
* To investigate the obstacles and challenges in ITS implementation, including technical, legislative and economic issues, based on relevant case studies that highlight successful practices and lessons learned;
* To explore future trends and development plans for ITSs in Europe, and to propose recommendations for improving the efficiency and interoperability of systems to support digitalization and sustainability goals.  
A qualitative document analysis was employed to identify and synthesize relevant regulations, strategies and academic sources related to Intelligent Transportation Systems (ITSs) in Europe. Key legal documents--such as EU directives, communications and funding frameworks--were retrieved primarily from EUR-Lex and official European Com  
Figure 2: The main areas related to ITSs addressed in the present paper.  
mission repositories. The relevant scientific literature was selected through searches in Scopus, Google Scholar and Web of Science, using keywords such as \"Intelligent Transport Systems\", \"ITS regulation Europe\", \"C-ITS\", \"V2X policy\" and \"sustainable mobility EU\".  
The selection criteria focused on sources published between 2010 and 2024, prioritizing materials that addressed regulatory developments, cross-border implementation challenges and real-world ITS applications in EU Member States. Documents and articles were included if they provided substantial insights into governance, technological architecture, interoperability or deployment outcomes.  
The proposed structured approach ensures that the findings reflect both the legislative evolution and the practical integration of ITSs across diverse European contexts.  
This study provides additional value due to its in-depth and comprehensive analysis of the evolution, impacts and challenges of ITSs in Europe. It represents an integrated perspective on how technological developments, legislative frameworks and sustainability objectives interact with each other, with strategic implications for public policies and the future development of intelligent mobility. The study therefore offers useful information for researchers, transport authorities, policymakers and industry to help create a more efficient and sustainable transport environment.
## 5 Numerical Experiments and Analysis
### Computational Results and Analysis
#### 5.2.1 Comparison Between NHALNS and Exact Optimization Method  
We first make a comparison between the results obtained by NHALNS and the optimal (or near-optimal) solution achieved by the exact method via GUROBI 9.1.1. Due to the limited capacity for exact algorithms in processing large-scale problems with high computation complexity, GUROBI cannot even produce a feasible solution within the specified time limit for the instances with more than 15 customers. Hence, we focus only on the small-scale instances for this comparison, specifically the instances of no more than 15 customers. The results are presented in Table 4. The TC of the best-found solution by GUROBI and NHALNS, as well as the corresponding running time (in seconds), are reported. For NHALNS, all the instances are run with 10 independent replications; the best, average, and standard deviation values are noted as Best, Avg, and SD, respectively. The maximum computation time for GUROBI is set to 1000 s, and the maximum number of iterations for NHALNS is set to 200.  
It is noted that NHALNS can optimally solve all the small-scale instances (with five customers) and some moderate-scale instances (with ten or fifteen customers) in a very efficient manner. For those instances for which GUROBI cannot provide the optimal solution, NHALNS can also obtain the solution which achieves the best upper bound found by GUROBI only in seconds. Obviously, NHALNS is able to solve the small- or moderate-scale instances in both an effective and efficient manner.
# Matrix Metalloproteinases and Temporomandibular Joint Disorder: A Review of the Literature
## 1 Introduction
These enzymes are seen in both physiological and pathological processes, including embryogenesis, apoptosis, bone remodeling, inflammation, arthritis, and cancer [7; 8]. However, the role of MMPs is not limited to the ECM, as they have also been shown to play a role in regulating inflammatory response, namely by processing chemokines, growth factors, receptors, proteases, and other molecules and proteins [9]. They are a family of 26 endopeptidase that degrade collagen, gelatin, proteoglycans, and other ECM components, and they are regulated at the level of their gene expression (cytokines, growth factors, hormones, and others), posttranslational activation, and endogenous inhibition (tissue inhibitors of metalloproteinases [TIMP]) [8; 10]. As inflammation occurs, however, processes involving these enzymes shift from physiological to pathological, and MMP activity results in excess tissue breakdown and damage [11]. Some MMPs have been suggested as being involved in angiogenesis, apoptosis, and osteoarthritis, processes which are seen in temporomandibular joint disorders [7]. Angiogenesis, found mostly in the synovial membrane in TMD, allows for new vessel formation to combat the hypoxia associated with an increase in the intra-articular pressure seen in TMD [12]. Chondrocytes undergo apoptosis in the early stages of TMD as the disc begins to remodel, and eventual cartilage degradation occurs, leading to osteoarthritis changes [4]. With evidence of the involvement of MMPs in these processes, and with the processes playing a crucial role in the progression of TMD, it would seem that MMP activity would be increased in joints of patients with the disorder. This paper aims to review the current scientific literature in order to investigate potential links between various MMPs and TMD.
# Satellite Climate Data Records: Development, Applications, and Societal Benefits
## 4 Societal Benefit
### Tourism  
The tourism industry is still in the early stages of using satellite observations, and in some aspects the usage is more often in GIS applications [136; 137]. Due to the specialty of tourism, the planning and management may overlap with other sectors, such as urban setting, transportation, water quality [138; 139], and water level. For example, the issue of forest conservation in protected areas, including national parks [140; 141], overlaps with remote sensing applications in the forestry industry.  
Some tourism may relate to weather or climate, which need only small efforts from satellite observation community. For example, fall foliage coloration can now be monitored by means of temporally normalized brownness derived from MODIS data [142], the precise position and timing is appreciated by leaf-lovers. Long-term satellite CDR can be beneficial by providing the best coloring time in a statistical sense so that tourists can be better prepared for their travel plan. Similarly, snow in ski areas is valuable information to skiers.  
The needs of each public sector may spur the improvement of the observation system and the production chain; new improvements in the technology would in turn expand the coverage and intensity that can apply to the societal benefits. This feedback loop would go endlessly through the life span of the technology.