esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Therefore, in this study the main focus is set on the development of linear and non-linear models to predict Tensile Modulus, Nominal Stress, and Elongation at break by using different machine learning techniques. Machine learning techniques have already been used for prediction of geometrical deviations, and have shown a great potential for the datasets collected from more than 100 samples [27]. However, the application of the classical approaches like DOE for smaller batches of data has shown great potential and has been reported in [22; 26; 28]. While the structured DOE is beneficial for the optimization of individual manufacturing parameters of AM systems and allows developing a systematic experimental approach to simultaneously analyzing multiple production requirements [26], the structured DOE also has drawbacks. For example, [PERSON] et al. [22] have reported that \"The presented DOE was incapable of replicating this phenomenon, as the range energy density was limited to a narrower window due to excessive geometrical distortion\", which means that the investigation of different combinations of AM-related parameters depends on the understanding of the process, and in the case where some of the process conditions are not satisfied, the data from the experiment can be lost. Therefore, there is a need for an additional data analysis technique, which will be able to learn the correlations between parameters that cannot be seen with a \"human eye\" from previous studies. Machine learning techniques have an ability to find hidden patterns in the data, and then accumulated information can be transformed to the new unknown problems [9]. In addition, machine learning techniques do not require a structured way of data collection, and thus they have a benefit over other classical methods when it comes to the conditions similar to real-time manufacturing. Data collected using a different design of experiments can be used together (joined in one data set) for model development, and with every next use of AM machines, collected data contributes to modification of previously developed models, resulting in higher prediction accuracy and better precision for the future use of AM. Nowadays, these methods are used in different field of studies [29; 30; 31], and have also been widely used in traditional manufacturing [32; 33; 34; 35]. Specifically, the author aims to adress the following issues (Section 3):  
* Estimation of mechanical properties of AM-manufactured parts without prior knowledge about the material. * Understanding how the mechanical properties depend on the part positioning in the build chamber. * Compare performance of Linear regression models and machine learning proposed models, and choose the best models for the prediction of mechanical properties. * Discuss which of the investigated features are the most significant and can be used to predict the mechanical properties, and how the mechanical properties can be controlled and managed based on the obtained results.
## 1 Introduction
### Vehicle-to-Ground (V2G) Communication  
V2G conflict detection employs data received by a ground station to detect the conflict between UAVs. Most V2G-based conflict detections used automatic dependence surveillance-broadcast (ADS-B)-like technology as their communication combined with other communication, such as telemetry, radar, and a cellular network [11; 12; 13]. However, other research used only telemetry for transmitting data to a ground station [14; 15].  
National Aeronautics and Space Administration (NASA) used an ADS-B module from uAvionix hardware attached to Da-Jiang Innovations (DJI) Phantom 4 UAVs combined with DJI telemetry to demonstrate the UTM conflict detection application. The demonstration parameters, such as position, speed, direction, altitude, and produced resolution advice, were evaluated [11]. Similarly, Oklahoma State University conducted V2G conflict detection with Vigilant's FlightHorizon software using ADS-B from intruder Cessna aircraft combined with telemetry from several UAVs (DJI Phantom 4, DJI M600, MakeFlyEasy Believer, Foxtech Nimbus, STRIX Stratosurfer) and ground radar. This test focused on the range of detection [12]. Another ADS-B-based UTM conflict detection combined with cellular network communication was conducted by Universitat der Bundeswehr Munchen using two DJI Matrice UAVs in simulated controlled airspace. This test measured the cellular network performance including the data trip time, signal-to-noise ratio (SNR), and bit rate [13].  
NASA also conducted non-ADS-B V2G conflict detection using telemetry and dedicated short-range radio channel (DSRC) communication. This test aimed to evaluate the detection range and to enable a transition from the extended visual line of sight (EVLOS) to BVLOS operations using a commercial oc
## 2 Results and Discussion  
### Potential Degradation Products  
Numerous studies have demonstrated the rich terpene profiles of citrus fruits [18]. The terpene profile of kaffir lime juice has been studied previously [4]. Based on the changes in the content of the selected compounds, we decided to determine the changes that occurred in the juice as a result of storage and then to determine the possible decomposition/degradation pathways and compare them with previous findings from the literature. In the first step, chemical compounds were selected based on the literature as potential products of terpene degradation (Table S1). A literature review showed that terpenes degrade into various products. Chemical compounds with molecular masses of 150.22 Da (eucarvone, thymol, carvone, verbenone, myrtrenal, carvacrol), 152.23 Da (verbenol, pinocarveol, pineol, myrtenol, perillyl alcohol), 154.25 Da (\(\alpha\)-terpineol, 1,4-cineole, eucalypt, \(\beta\)-terpineol, terpinene-4-ol), and 172.26 Da (trans-1,\(\beta\)-p-menthanediol, cis-1.8-p-menthanediol) are formed. Conversion reactions of bicyclic monoterepenes to monocyclic monoterepenes also occur, resulting in compounds with masses of 132.20 Da (m-cymenene, \(p\)-cymenene), 134.21 Da (\(p\)-cymenene, \(p\)-mentha-1.5.8-triene), and 136.23 Da (y-terpinene, \(\alpha\)-terpinene, limonene, \(\alpha\)-phellandrene, terpinolene, \(\beta\)-phellandrene, camphene, \(\beta\)-myrcene, cis-ocimene, trans-ocimene) [13].
## 1 Introduction  
Treatment of patients with Class III disharmony has been discussed in the literature since [PERSON] [1], who described the appearance of patients with protruded chins in 1797. The etiology of this malocclusion can be associated with various combinations of skeletal and denotalveolar disharmony in one or both jaws [1; 2; 3]. Favorable dentoskeletal changes induced by combined rapid maxillary expansion and face-mask (RME/FM) treatment in young patients with midface deficiency have been reported by various investigators and have become the standard protocol in young children of different races [3; 4; 5; 6]. However, a lack of stability after early treatment is still a concern for clinicians [3; 7; 8; 9]. The growth potential of the mandible during and after pubertal growth peak influences the long-term success of early treatment [10]. Several studies reported that 20-33% of treated patients have unpredictable, excessive mandibular growth that eventually required orthognathic surgery [3; 6; 8]. This is also contributed by a longer period of mandibular growth (1 year and 5 months in normodivergent Class III subjects and 2 years 8 months in typical, high-angleClass III subjects) after the cessation of forward growth of the maxilla, especially in boys, compared to Class I individuals [1; 2; 3; 10; 11; 12; 13]. Due to the unknown period of individual mandibular growth and unpredictable growth potential in Class III, most clinicians wait for growth completion before treating these patients with orthognathic surgery [3; 14]. Nevertheless, according to recent scientific literature, orthognathic surgery is associated with patients' preoperative anxiety, post-operative pain and swelling, nutritional restrictions and challenges [15]. Among the complications, there is abnormal bleeding, bad splinting, iatrogenic soft tissue injury, temporary sensory impairment, infection, alteration of the planned occlusion, nasal septum deviation, TMJ disfunction with muscular pain and a skeletal relapse [16].  
Moreover, delaying the treatment until adulthood can aggravate problems related to psychosocial, esthetics and functional factors; waiting until growth cessation before orthodontic/surgical intervention is still regarded as a proper solution for patients with severe skeletal Class III disharmony [17].  
However, there are concerns related to the surgical correction of Class III subjects and the stability of movements \(>\)5 mm of the maxilla and/or \(>\)7 mm of the mandible [18; 19]. Similarly, another surgical procedure, i.e., surgical-assisted maxillary protraction showed less stability than standard, nonsurgical maxillary face-mask protraction in long-term observations [20].  
Moreover, there is still a scarcity of literature on the nonsurgical treatment of skeletal Class III malocclusion in pubertal and postpubertal patients [21; 22; 23; 24; 25; 26; 27; 28]. The present article aims to report orthopedic/orthodontic treatment protocols for Class III adolescent (pubertal and postpubertal patients that has been used in our clinic since 2005.
# Multiple Pedestrian Tracking in Dense Crowds Combined with Head Tracking
## 1 Introduction
Furthermore, compared to the pedestrian's entire body, the head has a smaller size, which indicates that even if some heads are occluded in some frames, they are more likely to reappear soon due to the fact that they only occupy small areas in the entire frame. Fortunately, the trackers tend to recover the tracklets of short-term occluded targets. In short, the head has become an ideal object to track. Therefore, using the head to track instead of body tracking in dense crowds can reduce the negative effects of severe occlusions to a considerable extent. In order to solve the problem of poor performance of multiple pedestrian trackers in dense crowd, and considering that the head is more suitable as a tracking object for MOT tasks in dense crowds, we proposed a novel approach for multiple pedestrian tracking in dense crowds combined with head tracking, and we named it as Tracking Pedestrians with Head Tracker (TraPeHat). Our method matched the head bounding boxes with the whole-body bounding boxes on the basis of obtaining the head movement tracklets, and  
Figure 1: On the premise of inputting the same picture, the head detector used in our approach detects 64 head bounding boxes, whereas a general full-body detector (which is an original implementation of Faster-RCNN) can only detect 46 of the 71 targets. replaced the head bounding boxes in the head tracklets with the whole-body bounding boxes according to the matching results to generate the final full-body trajectories. On the basis of ensuring the tracking accuracy, our method effectively reduced the number of false negatives and false positives caused by occlusions, and improved the actual performance of the multiobject tracker in dense crowds. It demonstrated certain practical values because it can be placed in many venues, such as airports, stations, gymnasiums, shopping centers, crossroads, etc. An official implementation of our paper can be found in [[https://github.com/TUT103/THT.git](https://github.com/TUT103/THT.git)]([https://github.com/TUT103/THT.git](https://github.com/TUT103/THT.git)) (accessed on 23 December 2022). Our paper has the following contributions. * Inheriting the work of [7], which only tracks the pedestrians' heads, we extended the tracked objects to the whole bodies of pedestrians, which are more common in the field of multiobject tracking. * To accomplish the task of matching pedestrians' head and body bounding boxes, we proposed a novel bounding box similarity calculation method, Intersection over Containment (IoC), by which, with the help of the Hungarian algorithm, we can efficiently complete the matching work of the head bounding box and the whole-body bounding box belonging to the same pedestrian. * We used the MOT20 [8], SCUT-Head [9], HT21 [7], and CrowdHuman [10] datasets to conduct a series of related experiments to demonstrate the feasibility and effectiveness of the above methods.
# A High Efficiency Multistage Coder for Lossless Audio Compression using OLS+ and CDCCR Method
## 7 Adaptive Golomb Code and Context Adaptive Binary Arithmetic Coder
### Analysis of Practical Aspects of Prediction Error Encoder Implementation  
Table 3 presents a comparison of the results for several different encoder settings, using the example of a database containing 16 test files (the average of the whole database was bold in the last row of Table 3). The second column contains the values of \(H(S_{\mathrm{DMS}})\) unconditional entropy here referred to as zero order entropy. The third column contain entropy values of the first-order \(H(S|C^{(1)})\) for prediction errors (we are talking about conditional entropy in which according to the first-order Markov model process the context is the predicted value which was calculated using cascade prediction (see Formula (23)). There is a significant decrease (bit average for the whole database of 16 files) from 13.813 to 9.172 bits per sample.  
The fourth column takes into account the actual bit average after using our proposed adaptive Golomb; a code described in Section 7.2. This allowed to obtain a bit average lower than \(H(S|C^{(1)})\) by 0.407 bits per sample, which shows that predictive modelling is not able to fully remove the mutual information.  
On the other hand, the introduction of a block in the form of an context-free arithmetic encoder (with active default long-term adaptation, see Section 7.3), which additionally encodes the stream of bits coming out of the Golomb encoder, allowed for a further decrease of the bit average by another 0.14 bits per sample.  
After introducing context rules (medium term adaptation) to the encoder CABAC, the bit average was reduced by 0.082 bits per sample, and after including additional contexts in CABAC (in the short term adaptation, see Section 7.1), the result was improved by reducing the bit average by 0.004 to the target level of 8.539 bits per sample.
## 3 Results and Discussion
### Governor System  
The final significant system identifiable within the invention is the speed control system (Figure 20). This task is carried out by the governor, which ensures that the steam  
Figure 19: Cross-section showing the toothed section of the flywheel.  
Figure 18: Cross-section showing the connection of crankshaft and governor by the drive belt.  
engine maintains a constant speed, regardless of variations in engine load. This is achieved through a mechanism that automatically adjusts the input steam supply to the cylinder.  
Although the concept of a speed governor is straightforward, its implementation and operation can be complex due to various technical aspects. These include precision in terms of speed changes, the dynamic adjustment of steam supply in real time and continuously, the synchronization of the rotation of the counterweights with the steam valves, balance, and durability, among others.  
As previously explained, the crankshaft transmits the rotational speed through pulleys and a drive belt (Figure 21). Once rotational motion is received on the governor shaft, it is then transmitted to the spindle via a system of bevel gears (Figure 22).  
Figure 21: Axonometric view of the ensemble of the crankshaft and control speed system.  
Figure 20: Front view of the elements forming the governor system.  
In addition to the small level gear housed in the spindle by means of a pin, it also consists of two flyweights and a movable sleeve enclosing the pressure spring. Furthermore, the regulation system extends from the lower part of the movable sleeve where the lever is fixed to a glut collar (Figure 23).  
Figure 23: Cross-section of the components of the governor spindle.  
Figure 22: Right view of the connection of shaft and spindle by level gears.  
Therefore, the control system is determined by the dependence on the rotational speed of the crankshaft, or the linear motion of the piston, translating into an increase or decrease in the centrifugal force generated at the upper part of the spindle by the flyweights.  
An increase in centrifugal force causes the position of the flyweights to change, such that the compressed dashpot spring contracts, raising the position of the movable sleeve (Figure 24).  
Conversely, a reduction in speed results in a closer position of the flyweights and a decompression of the dashpot spring, causing the movable sleeve to descend (Figure 25). Thus, these elevation changes cause the movement of the cam levers that guide the trip blades through a system of mechanical linkages.  
Figure 24: Cross-section of the governor at maximum speed (**left**) and influence on the cam lever position (**right**).  
Figure 25: Cross-section of the governor at minimum speed (**left**) and influence on the cam lever position (**right**).
# A Low-Complexity Start-Stop True Random Number Generator for FPGAs
## 4 Conclusions  
This paper introduces a novel approach to generating true random number sequences utilizing two-ring oscillators as primary sources of entropy. Phase detectors complement these oscillators by exploiting phase jitter from both sources, enhancing randomness extraction. The proposed TRNG operates on-demand, seamlessly integrating into a single FPGA alongside any digital system requiring random bits. Additionally, raw bit sequences undergo real-time post-processing using established software implementing the SHA-1 algorithm.  
This study evaluates performance across various FPGA technologies, specifically Virtex 5, Spartan-3, Spartan-6, Artix-7, Cyclone V, and Max-10. As described, WT2D finds application across systems necessitating authentic random bit generation, with particular relevance to cryptographic contexts.  
Enhancing resilience against attacks, particularly frequency injection attacks, is a promising area for exploration in the field of security. One method involves deploying multiple ring oscillators with significantly distinct nominal frequencies. Another approach entails integrating health tests within the FPGA structure to promptly detect deficiencies in randomness and potential interference in the random number generation process caused by attackers, thereby enhancing overall security. The implementation of multiple WTD2 generators, which includes the random selection of the current source of random bits within the same FPGA, appears to be another promising approach to bolster robustness against cryptographic attacks.  
Significantly, the proposed TRNG demonstrates exceptional efficiency metrics. It utilizes less than 1% of resources in the smallest FPGA device and consumes under 260 mW for all sampling frequencies up to 10 MHz. These attributes underscore its suitability for resource-constrained environments and highlight its potential impact in cryptographic applications where robust random number generation is critical.  
Not applicable.  
The data presented in this study are available in the article. The authors declare no conflicts of interest.
## 4 Analytical Model For Optimal Cache Deployment
### Analytical Model  
#### 4.2.1 Notation and an Example  
We summarize the parameters and variables considered in our study in Table 1. \(N\) represents the total number of items in the catalog, whereas \(T\) is the total amount of storage capacity (GB) that can be deployed in caches at the various network levels. \(h_{access}\), \(h_{metro}\) and \(h_{server}\) represent the average hop-distances from caches at AMENs, MCENs and the video server, respectively. Note that \(h_{metro}\) defines how distant the MCEN cache is from end-users, and thus it is a decisive when planning cache deployment. A low value of \(h_{metro}\) means the MCEN is close to the access edge nodes, whereas a high value signifies that a request needs to traverse the metro transport network to reach end-users. \(n_{access}\) represents the number of caches utilized at the metro-access level. Note that it is not necessary that all AMENs are equipped with storage capacities. For example, it is possible that 16 AMEN caches are utilized having \(n_{access}=16\) while the topology consists of 32 AMENs. In such a case, caches at AMENs are placed such that the maximum number of hops between an end-user and an AMEN cache is minimized. \(k\) is the index of the last item stored in the access caches. Equivalently, \(k\) also represents the number of items stored in the access caches, as the caching is popularity-based. Note that we assume the same item cannot be cached at caches of two network levels simultaneously while all caches of the same level are assumed to store the same items. For example, if the last item stored in the access caches has rank 100 (\(k=100\)), it means that all items with rank less than 100 are stored in the access caches. Then, for a given value of \(k\) and according to the remaining storage capacity, the number of items to be stored in the cache located in metro level (i.e., at MCEN) is simply calculated given the average video size \(S_{a}\) as follows:  
\[\frac{T}{S_{a}}-n_{access}\cdot k \tag{3}\]  
For example, given \(T=160\),000 GB and \(S_{a}=8\), if \(k=400\) and \(n_{access}=32\) (400 items are stored in each of the 32 caches at AMENs), the number of items to be stored in the cache located at the MCEN is \(7200\left(\frac{160,000}{8}-32\cdot 400\right)\).  
Figure 3: Example of two random storage capacity distribution approaches, each leading to a cache deployment.
# Experimental Study on LTE Mobile Network Performance Parameters for Controlled Drone Flights
###### Abstract  
This paper analyzes the quantitative quality parameters of a mobile communication network in a controlled drone logistic use-case scenario. Based on the analysis of standards and recommendations, the values of key performance indicators (KPIs) are set. As the main network-impacting parameters, reference signal received power (RSRP), reference signal received quality (RSRQ), and signal to interference and noise ratio (SINR) were selected. Uplink (UL), downlink (DL), and ping parameters were chosen as the secondary ones, as they indicate the quality of the link depending on primary parameters. The analysis is based on experimental measurements performed using a Latvian mobile operator's \"LMT\" ISC infrastructure in a real-life scenario. To evaluate the altitude impact on the selected network parameters, the measurements were performed using a drone as transport for the following altitude values: 40, 60, 90, and 110 m. Network parameter measurements were implemented in automatic mode, allowing switching between LTE4-LTE2 standards, providing the opportunity for more complex analysis. Based on the analysis made, the recommendations for the future mobile networks employed in controlled drone flights should correspond to the following KPI and their values: \(-\)100 dBm for RSRP, \(-\)16 dB for RSRQ, \(-\)5 dB for SINR, 4096 kbps for downlink, 4096 kbps for uplink, and 50 ms for ping. Lastly, recommendations for a network coverage digital twin (DT) model with integrated KPIs are also provided.  
unmanned aerial vehicle; drone; mobile network; LTE; reference signal received power; reference signal received quality; signal to interference and noise ratio; uplink; downlink; ping +
Footnote †: journal: _Sensors_