esa-sceva/satcom-chunk-collection huggingface dataset:
# Single-Handed Gesture Recognition with RGB Camera for Drone Motion Control
## 4 Experiments and Results
### Drone Flight Performed in a Simulated Environment  
To assess the functionality of the proposed hand gesture vocabularies in controlling drone flight, we employ a Tello UAV simulator developed with Unity [40]. Specifically, we align the classified gesture signals with the control signals according to the remote control message format used in the simulator, corresponding to the four elements: roll, pitch, throttle, and yaw. For example, when a hand gesture is classified as the stop sign, zeros are assigned to all factors. If a user extends both the index finger and thumb, and the gesture is classified as'move left', only the throttle factor is assigned a zero value because the yaw and pitch factors are influenced by the hand distance and rotation. Additionally, we set the speed to a fixed value for simplified implementation. Figure 9 shows a sample scene from the drone simulator integrated with our hand gesture controller.  
Before commencing the hand gesture control test for the drone, we conducted a preliminary session with four participants to familiarize them with drone manipulation using keyboards across 81 distinct flight patterns. Subsequently, they were instructed to replicate the same drone control using hand gestures. Feedback from the participants indicated that controlling individual factors such as the roll, pitch, and yaw with hand gestures felt intuitive, with a difficulty level similar to that of keyboard control. Additionally, the participants perceived gesture control as more intuitive when simultaneously manipulating the roll and yaw. However, the participants noted initial challenges with throttle-related controls. Specifically, they reported confusion when attempting to move the drone downwards by folding the middle finger and extending the pinky finger and vice versa. This challenge arose from the less intuitive nature of the proposed throttle gestures, making them difficult for the users to execute accurately. While defining throttle reduction as extending the middle finger downward presents a more physically aligned gesture, it hinders the performance of other gestures and obscures other fingers in the head-mounted 2D RGB camera view, thereby complicating recognition. This challenge will be addressed in the future by finding an optimal camera installation location to reduce occlusion issues and exploring alternative gestures for throttle control.  
Figure 9: An illustrative scenario from the drone simulator demonstrates the application of the proposed hand gesture vocabulary in controlling a drone flight. In this example, the drone executes commands for ‘pitch forward’, ‘roll left’, and ‘yaw left’ simultaneously.
## 2 Materials and Methods
### Input Variables  
Three types of input variables were included in the ANNs (GPS-based accelerometer, anthropometric and running speed) due to the relationships previously found with running kinematics [26; 27; 28; 29; 30]. Stance time characteristics and peak accelerations during foot stance were selected as the GPS-based accelerometer input variables. Instances of initial foot contact (IFC), midstance (MS), and terminal foot contact (TFC) were identified in the vertical acceleration profile [26], and the subsequent timings (s) between these events were calculated (stance time, IFC-MS time, MS-TFC time). The vertical (VT), anterior/posterior (AP), medial/lateral (ML), and resultant (RES) peak accelerations during foot stance were then calculated by identifying the largest value on each axis during this time frame. The averages of each accelerometer variable were calculated across the ten gait cycles. Participant height (cm), body mass (kg), and leg length (cm) (distance from the anterior superior iliac spine to the medial malleolus) were recorded prior to testing and included as anthropometric input variables. Running speed (km/h) was derived from the treadmill speed within each trial and was the running speed input variable.
## 2 Materials and Methods
### Site and Dataset Descriptions
The procedure of quality control included the test for physically not possible values and spikes, the stationarity test, and the integral turbulence characteristics (ITC) test [24]. Furthermore, all of the instruments installed at the SACOL site were calibrated every year in order to ensure the reliability and continuity of the dataset [13]. More details regarding the profile of the SACOL station and the in situ measurements have been provided by [PERSON] et al. [24] and [PERSON] et al. [23]. The NDVI was derived from the MOD09 GA version 6.0 product and had passed phase 3 verification ([[https://doi.org/10.5067/MODIS/MOD09](https://doi.org/10.5067/MODIS/MOD09) GA.006]([https://doi.org/10.5067/MODIS/MOD09](https://doi.org/10.5067/MODIS/MOD09) GA.006), (accessed on 20 June 2023). The gross primary productivity (GPP) represents the growth of the terrestrial vegetation and was derived from the Global OCO-2 based SIF product (GOSIF). This is a new global product of solar-induced chlorophyll fluorescence [27]. The LAI was derived from the MOD15A2H version 6.1 moderate-resolution imaging spectroradiometer (MODIS). The dataset consisted of eight-day composite data with a pixel size of 500 m ([[https://lpdaac.usgs.gov/products/mod15a2](https://lpdaac.usgs.gov/products/mod15a2) hv061/]([https://lpdaac.usgs.gov/products/mod15a2](https://lpdaac.usgs.gov/products/mod15a2) hv061/), (accessed on 20 June 2023). Using the MODIS L1B as the primary input and performing corrections for the effect of gaseous absorption, molecules and aerosol scattering, coupling between atmospheric and surface bidirectional reflectance function (BRDF) and adjacency effect (atmospheric point spread function), surface reflectance was produced for high-level NDVI and LAI. NDVI (Level2G) and LAI (level 4) were radiometrically calibrated and atmospherically corrected [28].
# Development of a New Eye Movement Measurement Device Using Eye-Tracking Analysis Technology
## 3 Results
### Degree of Agreement between the Visual Target and Eye Position  
Rho max between the eye position data and visual target positions was used as an index to evaluate eye movement accuracy. The average rho max values of the horizontal and vertical eye movements were plotted against the visual target speeds (Figure 5). A multi-way ANOVA was performed, with the main effect indicating that rho max tended to decrease with increasing visual target speed (F [4; 60] = 32.50, \(p\) < 0.0001). Rho max was higher for horizontal eye movements than vertical eye movements (F [1; 15] = 22.59, \(p\) = 0.0003). There was no significant difference in rho max between the right and left eyes (F [1; 15] = 0.84, \(p\) = 0.3749). There was no significant interaction between visual target speed, direction of eye movement, and right and left eyes (\(p\) = 0.5832 to 0.9060). [PERSON]'s post hoc analysis revealed no significant difference (\(p\) = 0.0839) in rho max between visual velocities of 20 deg/s and 40 deg/s; however, a significant difference was observed (\(p\) = 0.0117 to <0.0001) between the other groups. These results indicate that the accuracy of eye movements decreased as visual target speed increased.
Chromotography_**2014**, \(1\), 120-140; doi:10.3390/chromotography1030120  
_chromotography_  
**ISSN 2227-9075**  
www.mdpi.com/journal/chromatopraphy  
_Article_  
A Longitudinal Study of Decomposition Odour in Soil Using Sorbent Tubes and Solid Phase Microextraction  
**[PERSON] *, [PERSON] and [PERSON]**  
Centre for Forensic Science, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007, Australia; E-Mails: [PERSON](@uts.edu.au (B.H.S.); [PERSON]SS] (S.L.F.)  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]Tel.: +61-2-9514-2000.  
_Received: 27 May 2014; in revised form: 14 July 2014 / Accepted: 25 July 2014 / Published: 30 July 2014_  
**Abstract:** Odour profiling of decomposed remains is important for understanding the mechanisms that cadaver dogs and forensically-relevant insects use to locate decomposed remains. The decomposition odour profile is complex and has been documented in outdoor terrestrial environments. The purpose of this study was to perform longitudinal analysis of the volatile organic compound (VOC) profile in soils associated with decomposed remains across all stages of decomposition. Two VOC collection techniques (sorbent tubes and solid phase microextraction) were used to collect a wider analyte range and to investigate differences in collection techniques. Pig carcasses were placed in an outdoor research facility in Australia to model the decomposition process and VOCs were collected intermittently over two months. VOCs of interest were identified over the duration of the trial, showing distinct trends in compound evolution and disappearance. The collection techniques were complementary, representing different subsets of VOCs from the overall profile. Sorbent tubes collected more decomposition-specific VOCs and these compounds were more effective at characterising the matrix over an extended period. Using both collection techniques improves the likelihood of identifying the complete VOC profile of decomposition odour. Such information is important for the search and recovery of victim remains in various stages of decomposition.  
**Keywords:** volatile organic compounds; decomposition chemistry; gas chromatography-mass spectrometry; GC-MS; human remains search; VOC collection
# Immigration as a Divisive Topic: Clusters and Content Diffusion in the Italian Twitter Debate
## 3 Methods and Results
### Where Are the Users Located? Geo-Spatial Analysis
#### 3.1.2 Correlation with Institutional Spatial Data  
We now correlate Twitter locations with official data, gathered from _Immigratio.Stat_ ([[http://stra-dati.istat.it/](http://stra-dati.istat.it/)]([http://stra-dati.istat.it/](http://stra-dati.istat.it/))), the ISTAT online archive specialised on data about migrants and foreigners in Italy. All data were available at different levels of granularity, corresponding to the official administrative levels (regions, provinces and municipalities) and were usually detailed per gender, age cohort, country of origin. Studying this data can also help in putting into context the whole situation about migrants in Italy. From the website of the Ministry of Interior, we learned that the number of migrants illegally landed in Italy by boat in 2019 (until the 4 October) is \(<\)8000, following a strongly decreasing trend that already started in 2017 (precisely: 108,876 in 2017, 21,119 in 2018 and 7896 to 4 October 2019). The distribution of the total number of residence permits, normalised over the number of residents in the area, can be seen in Figure 2. From this map, it is clearly visible how newcomers try to settle mostly in northern Italy, with particular hotspots in the province of Milan, Rome and in different provinces between Emilia-Romagna, Tuscany and Lombardy.  
Figure 2: Spatial distribution of the geolocated tweets (**b**) compared to the distribution of residence permits per province (**a**). Both are normalised over the number of residents per province. We can observe that the provinces that display the highest activity are Rome, Milan and Genoa. It is also clearly visible the gap in terms of Twitter activity—at least on this specific topic—between northern and southern Italy. The correlation with residence permits appears to be low ([PERSON]’s correlation coefficient \(=0.37\), with \(p\)-value \(=8.08\times 10^{-5}\)).  
Keeping in mind that all data about the territorial distribution refers to those migrants whose situation is somehow regularised and acknowledged by the State (i.e., irregular migrants without residence permits are not included), we can see in Figure 2 that the correlation between number of tweets and resident permits looks quite low, maybe with the only exception of the province of Milan. [PERSON]'s correlation confirms this sensation, with data being only mildly positively correlated (\(0.37,p\)-value = \(8.08\times 10^{-5}\)). Therefore, the debate seems to be driven by other mechanisms rather than the actual, local presence of migrants.
# Associating Images with Sentences Using Recurrent Canonical Correlation Analysis
## 4 Experimental Results
### Image and Sentence Matching  
To systematically study the modelling of contextual attention mechanism, as shown in Figure 3, we propose four variants of RCCA: (a) RCCA-nc does not use any context information at all the time steps in the contextual attention-based LSTM-RNN, (b) RCCA-fc uses the context information only at the first time step, (c) RCCA-lc uses the context information only at the last time step, and (d) RCCA-na dose not use the attention mechanism but only the context information. We denote RCCA-ac as the original model in Figure 1 which uses the context information at all the time steps. We also develop an ensemble model RCCA\({}^{*}\) in a similar way as [8], by summing five cross-modal similarity matrices generated from the five RCCAs together,  
For all the RCCAs, we use the 19-layer VGG Network [21] to extract 512 feature maps (with a size of 14 \(\times\) 14) in \"conv5-4\" layer as multiple local features, and a feature vector in \"fc7\" layer as the context feature. Therefore, the dimensions of local and context features are \(F=512\) and \(D=4096\), respectively,and the total number of local regions is \(L=196\). Some hyperparameters of RCCAs are illustrated as follows: \(D_{x}=1024\), \(D_{y}=1024\), \(\lambda_{x}=0.1\), \(\lambda_{y}=0.1\), \(\lambda=10\), \(K=100\), \(U=300\) and \(m=0.2\). For the pretraining, we find that using 3 pretraining epoches can always achieve satisfactory performance. The number of time steps in the contextual attention-based LSTM-RNN is \(T\)=3 (more details in Section 4.4). We compare RCCAs with several previous state-of-the-arts on the three datasets in Tables 1-3, respectively. All the compared methods can be classified into two classes: (1) global matching and (2) local matching. The global matching methods [1; 3; 4; 6; 7; 45; 47; 48] directly learn global representations for image and sentence, and then associate them by computing the global similarity in an one-to-one manner. While the local matching methods [2; 5; 8; 9; 10] first detect local objects and words in the image and sentence, and then summarize their local similarities to finally obtain the global similarity.
## 5 Thinking modules
### Network Control and Access Selection Module  
The Network Control and Access Selection Module is in charge of _jointly_ dynamically deciding:  
1. at each application set-up attempt, the most appropriate QoS, mobility and security requirements to meet the target user expectations (including the target user Quality of Experience, the cost the user is ready to pay, _etc._) (intelligent requirement identification);
2. the most appropriate available access network(s) (if any) able to support the application satisfying its requirements identified in issue (1), as well as the most appropriate supporting flows (intelligent access and flow selection);
3. during the connection lifetime, the (dynamically varying) most proper access network(s) and relevant flows which has/have to support the connection, thus triggering possible traffic/quality/content/service driven vertical handovers (inter-network mobility);
4. the most appropriate virtual paths which have to support the in progress flows in the selected access network(s) (load balancing).  
The proposed approach will make possible to _jointly_ perform the four above-mentioned tasks which, in current implementations and works in the literature, are generally dealt with in separate, uncoordinated fashion by different procedures (namely, inter-domain selection/reselection, connection admission control, routing/load balancing).  
The algorithms embedded in this thinking module, basing on a knowledge of the present underlying access network status which could include the present performance of the various virtual paths, as well as of the type of contents/services to be carried (clearly, the information actually available depends to the deployed sensing functionalities), jointly decide, in a dynamic way, the connections-to-accessnetwork assignments, the connections-to-flows assignments and the flows-to-virtual-paths assignments, also deciding the possible triggering of vertical handovers. It is fundamental stressing that these decisions have be taken by using proper multi-object optimization methodologies using the most advanced concepts available in the literature and even developing ad hoc optimization theories.
## 2 Materials and Methods
### Measurements  
#### 2.4.1 Functional Performance Assessment  
Functional performance was evaluated through the assessment of dynamic balance, functional mobility, walking speed, and lower body muscle strength, which were measured with the standard valid and reliable tests for women aged over 50 years old, such as the Functional Reach Test (FRT) [46], Timed Up and Go (TUG) test [47], 10-Meter Walk Test (10 MWT) [48] and the 30S Chair Stand Test [49], respectively. These selected tests are relevant and appropriate for our target population, recognizing that older adults may have unique functional needs and limitations. Moreover, we chose tests that required minimal space and equipment and could be implemented in multiple field settings such as a general practitioner's office.  
To evaluate the lower body muscle strength, the 30S Chair Stand Test was performed [50]. During this test, participants were instructed to rise to a full stand from a seated position for 30 s, maintaining a straight back and feet flat on the floor, without using their arms for support (Figure 2). The number of repetitions during the 30 s timeframe was recorded as the final score.  
Functional mobility was evaluated using the TUG test [51]. During this test, participants were asked to stand up from a chair, walk a distance of 3 m, turn around, and return to a seated position as quickly as possible. The duration taken to complete the test was recorded as the score (Figure 2).  
Dynamic balance was assessed using the FRT. Functional reach is the maximum forward-reaching distance (cm) that individuals can reach in the forward direction from a comfortable standing posture while fixing their base of support on the floor, without balance loss [52]. The distance between the starting position of their third metacarpal bone and the maximal reach position was measured (Figure 2).  
Figure 2: Functional performance assessments.  
Walking speed performance was measured using the 10 MWT [53]. During this test, participants were instructed to walk at their usual pace for a distance of 10 m. Timing commenced when they started walking, and the time taken to complete the walk was recorded using a digital stopwatch (Figure 2). No verbal encouragement was provided during the test.
## 2 Background of Sensors and Sensor Networks in the Ag-IoT  
### Sensors and Sensor Networks  
Smart agriculture requires the deployment of various sensors on farmland, crops, animals, and agricultural equipment. High-quality, high-resolution, high-reliability professional agricultural sensors must be designed and developed to sense the physiological markers of agricultural production environments, animals, and plants. According to the different detection objects, sensors can be divided into many types, such as gas sensors, temperature and humidity sensors, illuminance sensors, nutrient element sensors, and so on.  
Sensor networks are broadly classified into two types: wired sensor networks and wireless sensor networks. Wired sensor networks do not meet the requirements of remote deployment areas, wide monitoring areas, and low cost due to the shortcomings of complex wiring and the high cost of wired sensors. In comparison, wireless sensor networks have the advantages of flexible installation and low cost, and they are being used in an increasing number of agricultural fields. The self-organization of a large number of wireless sensors forms a Wireless Sensor Network (WSN). A WSN can remotely transmit data from various detection objects in the monitoring area to terminal users in real time and quickly. It can be deployed in remote areas to overcome wired limitations. A WSN consists of sensor modules, processor modules, wireless communication modules, and energy supply modules. The sensor module collects environmental information and converts the collected signal to digital analog; the processor module filters and processes the data; the wireless communication module sends the collected information to the sink node or base station; and the energy supply module is in charge of the entire wireless sensor's energy supply, including power supply, solar power, and wind power. Wireless sensors are typically dispersed at random throughout the monitoring area. The collected data are transmitted multi-hop between nodes before being wirelessly transmitted to the base station [31]. With the advances in communication technology, many different types of wireless networks have emerged, as shown in Table 1. The deployment nodes of agricultural sensors can be adjusted to meet the various service requirements of agricultural applications.
## 2 Background and Related Work
### Geometric Path Planning Methods  
A geometric PP method that assists in determining a good-quality path in a 3D environment of relatively higher complexity was given in [54]. The environment is represented by using a height reduction strategy to solve the trade-off between path-finding efficiency and accuracy in environment modeling. Unfortunately, it does not reduce the searches in the left-over parts of the area, and, thereby, computing time can be higher in most cases. An incremental PP algorithm considering both local and global constraints for good quality pathfinding was designed by [PERSON] et al. [55]. It is fast, and it reduces the set of good-quality path candidates to only four, with minimal computing time. However, the study ignores space reduction to efficiently find a candidate solution. A geometric PP method considering the minimum turn radius of a UAV for optimal paths in a 3D space was designed by [PERSON] et al. [56]. The suggested concept is reliable and assists in determining a path with the least complexity. An enhanced heuristic-based PP method to find a good-quality path efficiently by considering UAV flight limits was designed by [PERSON] et al. [57]. A new over-segmentation-based method to determine the free-space overlay of a connected region set was suggested by [PERSON] et al. [58]. This method quickly finds a safe and good-quality path. However, the approach does not take into account information about sharp turns, narrow passages, and other environmental constraints, which may degrade the suggested method's utility. Furthermore, to augment both efficiency and accuracy, it is extremely important to find irrelevant areas that can be discarded if they cannot help to find an optimal/quasi-optimal path in an environment [59]. Several studies have designed closely related PP methods with undoubtedly reduced time cost, such as the Approximation with Visibility Line (ApVL) method [60]. The ApVL PP method [60] is an improvement of the Base Line-Oriented Visibility Line (BLOVL) algorithm [50], and it is regarded as a highly suitable algorithm for finding an approximate shortest path in 3D urban environments. It reduces the obstacle count significantly (e.g., it processes obstacles that are on a straight line only), and constructs visibility graphs from the chosen obstacles' corners only to incrementally find a low-cost path. Meanwhile, it has relatively higher time complexity. In addition, it either yields longer paths or requires more processing to find a working path. In some scenarios, it is even unable to find a flyable path owing to connected obstacles with straight-line obstacles.