esa-sceva/satcom-chunk-collection huggingface dataset:
# The SF12 Well in LoRaWAN: Problem and End-Device-Based Solutions
## 6 Conclusions  
In this paper, we identified and characterized by simulation a formerly unexplored LoRaWAN network condition, which we call the SF12 Well. This phenomenon may arise due to the presence of even a relatively low number of CEDs, which will tend to increase their SF and thus the number of collisions and packet drops due to duty cycle constraints. In consequence, the SF12 Well may significantly degrade network performance, leading to a very low PDR. The negative impact of the SF12 Well on the PDR increases with the number of CEDs and the EDL.  
We evaluated alternative ED-oriented SF management techniques which show that it is possible to avoid the SF12 Well, while achieving a significant PDR improvement compared with default LoRaWAN behavior by up to a factor of 4.7 in the scenarios considered. A crucial feature of alternative SF management techniques is their SF value distributions. Even SF distributions, as in SFM2, allow the exploitation of the orthogonality of different SF values and reduce the number of collisions. Approaches leading to uneven SF distributions, especially when low SF values are highly utilized, as tends to occur in SFM3, reduce the ToA to a greater extent. While decreasing ToA reduces the number of collisions and losses due to duty cycle limitations, it misses the opportunity to exploit the orthogonality of using different SF values.  
The best alternative SF management technique depends on the specific characteristics of each scenario, particularly in terms of the number of CEDs and the EDL. The above remarks will offer helpful guidance for engineers or researchers who may need to face the SF12 Well problem.  
Simulation work, L.C.; validation, L.C., C.G. and R.V.; writing--original draft preparation, L.C.; writing--review and editing, C.G. and R.V.; supervision, C.G. and R.V. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Spanish Government through project TEC2016-79988-P and project PID2019-106808 RA-I00, AEI/FEDER, EU.  
Figure 18: CED SF values distribution when SF mode is (**a,d,g**) SFM1; (**b,e,h**) SFM2; and (**c,f,i**) SFM3 and for (**a–c**) EDL equal to 0.9 packets/h, (**d–f**) 3.6 packets/h, and (**g–i**) 18 packets/h.
# Time and Energy Efficient Relay Transmission for Multi-Hop Wireless Sensor Networks
## 3 Proposed Scheme  
As shown in Figure 1, sensor nodes transmit the sensing data to the coordinator or the PAN coordinator after they obtain sensing data. In the proposed scheme, the coordinator relays data frames received from sensor nodes or low-level coordinators. The PAN coordinator configures its WPAN, manages its member nodes, and processes the received data. Figure 3 shows the protocol stack of the proposed relay transmission scheme.  
As shown in Figure 3, MAC and physical layers of all devices are based on the IEEE 802.15.4 standard, and their upper layers are based on the ZigBee standard. In the proposed scheme, the coordinator uses only the IEEE 802.15.4 standard to forward sensing data.  
In the current IEEE 802.15.4 cluster-tree topology, the PAN coordinator and intermediate coordinators must compete against their children devices to deliver data frames to the previous or next coordinator. Therefore, when the traffic load or the number of devices increases, the coordinator may take several BIs to deliver data frames to the next coordinator. If these phenomena continue for a long time, the network performance significantly decreases. Also, the IEEE 802.15.4 cluster-tree topology may not be able to provide sufficient bandwidth for the increased traffic load or the number of devices and may not deliver the additional information to manage the network successfully. The common drawback of the existing approaches is that they do not address the poor bandwidth utilization problem in IEEE 802.15.4 cluster-tree networks, so it is hard to improve the network performance. To address these problems, in this paper, we propose a new relay transmission scheme suitable for the IEEE 802.15.4 cluster-tree topology. The proposed scheme provides the RP to deliver data or control frames to the next coordinator without the contention for the channel access. Also, the proposed scheme includes various time offsets for the distributed beacon scheduling in each beacon frame. Thus, all devices in the network can easily synchronize with their parent coordinators and can easily adapt to the adjusted BI.  
Figure 3: The protocol stack of the proposed relay transmission scheme.
# A Comprehensive Analysis of Smart Grid Systems against Cyber-Physical Attacks
## 5 Bayesian Attack Graph for the Smart Grid Cyber Domain
### Simulation Results  
Figure 8 represents the BN using Bayes.jar file. This network is used to compute the PoC of all SG cyber functions (shown in Figure 7). We input all the PoC of all vulnerabilities corresponding to each cyber function into the Byes.jar file. Figure 9 represents the unconditional probabilities of all functions of the SG test network. Each functional node is a Bernoulli variable, where True (T) represents the probability of compromise and False (F) represents the likelihood of no compromise. The probabilities will change if any vulnerability is patched or discovered, and that will change the probability of compromise of other system components since they are connected in the Bayesian network using Bayes' rule. Figure 10 represents a scenario when engineers have patched the billing engine server, and its likelihood of compromise becomes zero. Figure 11 represents the scenario when remote code execution is discovered in the billing engine server and shows how the PoC of its children change. Furthermore, if we know OMS is compromised, we can compute the likelihood of MDM compromise using the posterior probability distribution based on Bayes' rule.  
Figure 8: BAGS simulation results: function nodes using the Bayes.jar tool. XSS, Cross Site Scripting; CSRF, Cross Site Request Frogery.  
Figure 10: Unconditional probability distributions when the billing engine’s SQL injection vulnerability is patched.  
Figure 9: Probability distributions when the probability of a remote attacker to attack is 0.70.  
BAGS quantifies the resilience of the cyber domain of the SG system based on the vulnerabilities of the cyber components. It accounts for the known vulnerabilities for which we compute CVSS scores. If there is any zero-day vulnerability (unknown to the system admin), it does not account for that. The system admin should implement an intrusion detection system on the gateways and install application layer firewall and anti-virus systems in order to detect zero-days. BAGS focuses on the first step of compromising the system, which is exploiting known vulnerabilities and protecting the system from them.
## 1 Introduction  
In South Africa, as in many regions of the world, the leading products used to control needs and invading alien plant species are glyphosate-based herbicides [1]. Eventually, both terrestrial and aquatic invertebrates are unintentionally exposed to glyphosate (GLY) residues after spraying events. [PERSON] [2] and [PERSON] et al. [3] both reported that high GLY concentrations were found in the Hex River Valley, an intensive grape-farming area in the Western Cape Province of South Africa.  
Among other species, the harmful effects of GLY have been documented in earthworms [4; 5; 6; 7], amphibians [8; 9; 10; 11], and freshwater shrimps [1; 12]. Glyphosate is also known to affect belowground interactions between earthworms and symbiotic mycorrhizal fungi [13].  
In temperate ecosystems, earthworms and arbuscular mycorrhizal fungi are very important for nutrient recycling and overall ecosystem functioning.  
The deleterious effects of GLY contrast with its known usefulness in agriculture. Yet, because of its widespread use and its documented harmful effects in several non-target species [1; 4; 5; 6; 7; 8; 9; 10; 11; 12], it is imperative to find means to diminish its environmental impact. Biochar amendment of agricultural soil can be such a means.  
Biochar results from biomass pyrolysis under the limited presence or complete absence of oxygen at temperatures greater than 250 \({}^{\circ}\)C. Because of its stability and absorbent properties, some view biochar amendment as a means to mitigate the effects of chemicals in soils [14; 15]. Such toxicity mitigating abilities have been shown after soil amendment in the cases of the spring onion _Allium cepa_ exposed to carbofuran and chlorpyrifos [16], the potworm _Enchytraeus albidus_ exposed to imidalcoprid and silver nanoparticles [17] and several earthworms species exposed to fomesafen, chlorantraniliprole, mesotrione, and imidalcoprid [18; 19; 20; 15]. Despite these beneficial attributes, some have found that biochar amendment reduces the efficacy of soil pest management efforts due to its absorbent capacities [21; 22] or worse causes direct harm to soil organisms such as earthworms [23; 24; 25; 26].  
The aim of the study was to investigate the effects of different rates of biochar soil amendment on the survival, biomass, neurotoxic and oxidative stress biomarkers in the earthworm _Eisenia fetida_ exposed to glyphosate.
## 3 Results and Discussion
### Reflectivity of Resonant Cavity  
The 3.5 \(\upmu\)m fiber laser utilized an output coupler (OC) or a fiber bragger grating (FBG) to control the reflectivity of the resonant cavity. In this simulation, OC reflectivity was used to represent the reflectivity of the non-high-reflective side of the cavity. Figure 6 shows how the output power changed with the OC reflectivity. The optimum point for the DWP scheme turned out to be lower than the DWCP scheme. Since the \(\alpha_{1976}\) was very high in the DWP scheme, a lower OC reflectivity was enough to make full use of the \(P_{1976}\). The optimum point of OC reflectivity was higher as the power of \(P_{1976}\) increased in the DWP  
Figure 5: Output power of a 3.5 \(\upmu\)m laser as a function of the fiber core diameter at 5 W of \(P_{976}\) and a different power of \(P_{1976}\) under (**a**) the DWP scheme and (**b**) the DWCP scheme. (**c**) \(\Gamma_{1976}\) as a function of the fiber core diameter at the DWP scheme and the DWCP scheme.  
scheme but lower in the DWCP scheme. Under the DWP scheme, for a certain length of fiber, a higher OC reflectivity led to a higher 3.5 \(\upmu\)m laser energy inside the fiber, which consumed excited state ions in level 4 and contributed to the absorption of \(P_{1976}\). Thus, a higher OC reflectivity means a higher \(\alpha_{1976}\), and for a higher power of \(P_{1976}\), it takes a higher OC reflectivity to deplete \(P_{1976}\). However, for the DWCP scheme, as what happened in the fiber length optimization, \(P_{976}\) was depleted before \(P_{1976}\). For a settled 5 W of \(P_{976}\), the higher the power of \(P_{1976}\) was, the easier \(P_{976}\) was depleted. So, the best OC reflectivity is lower as the power of \(P_{1976}\) increases.
# Optimizing Geospatial Data for ML/CV Applications:
## 3 Methods
### ULDK Service Integration
#### 3.2.2 Response Handling  
The service response provided the requested parcel information. New parcels were saved as GeoJSON files, as can be seen in Figure 6. Their WKB geometries were also included. This optimized processing in later stages by avoiding redundant requests. The 'loads' function from the'shapely.wkb' module converted WKB geometries into usable geometric objects.
# Agreement Between Tele- and Face-to-Face Assessment of Neuromotor Development in High-Risk Children
## 2 Materials and Methods
### Procedure  
Before conducting the assessment, clinical data such as gestational age, chronological age or corrected age, gender, and perinatal antecedents, including the presence of prematurity, multiple gestations, intrauterine growth restriction, plagioclase, muscle tone alterations, congenital muscular torticollis, and perinatal asphyxia/mild hypoxic-ischemic encephalopathy, were recorded.  
The participants underwent two assessments in two appointments: a traditional face-to-face and a real-time tele-assessment. Children were assessed face-to-face at the hospital service and at home through the tele-assessment. The assessments were conducted by two physical therapists, ABF and AIRL, each with more than 10 years of experience in the pediatric population. One therapist always conducted the face-to-face evaluation and the other the tele-assessment, and each was blinded to the assessments of the other. The two assessments were performed in the same week, and the order was randomized using a random sequence generator to counterbalance any testing-order effects.  
During the face-to-face assessment, the therapist observed the children's spontaneous movement, positioned them at the starting position, and encouraged the children to move as the evaluation required. The tele-assessment involved the participation of the caregiver, who received the following instructions: (1) to place the child on a flat surface that allows freedom of movement, (2) to use the front-facing camera with the smartphone situated in a place guaranteeing wide vision of the child when assistance was required, and (3) to encourage the child to move as much as needed and position him or her as instructed by the therapist.
## 4 Experiments
### The Dataset Used for Experimental Validation and Experimental Settings
#### 4.1.4 Implementation Details  
The model was developed using the PyTorch framework and trained on a GPU device using the Nvidia GTX-3090. We use the Adam optimizer to train the model on the dataset for 1000 epochs. The initial learning rate is set at 0.001, and it decreases by 0.5 every 10 epochs. The batch size remains constant at 64. In addition, LSTM architecture comprises a single-layer encoder and decoder, with an input dimension of 2 and a hidden state size of 32. Dropout regularization was used. Scene feature information is embedded in a 16-dimensional space, while the grid size for inputting scene features is established at 8. The intention feature of pedestrian behavior information is embedded in 22-dimensional space. For the attention layers, the first is configured to a 16 \(\times\) 16 dimension and the second to 16 \(\times\) 32. We introduce random noise as a 16-dimensional vector, obtained by sampling from a standard Gaussian distribution. The computational complexity of our model is \(O\big{(}T*\big{(}H^{2}+HD\big{)}\big{)}+O\big{(}EF^{2}\big{)}\).
# Improvement of Perception Layer Routing Protocol with Static Nodes in IoT-Based Microgrids
## 3 System Models
### Algorithm Improvement under Static Nodes  
In this section, the cluster radius, cluster head ratio and cluster head selection threshold methods are improved on the basis of the disadvantages of the LEACH protocol.
# Recognition of American Sign Language Gestures in a Virtual Reality Using Leap Motion
## 3 Experiment and Results  
### Settings and Data  
Participants in the pilot study were twelve (12) people, aged 20 to 48 (mean: 27.2), with a differing range of experience in the use of computer equipment. All subjects were healthy (with no known problems of vision or VR sickness) and inexperienced users of ASL, therefore, the subjects were given about 1 h of prior training to learn the signs of ASL as well as to familiarize with the developed system and the VR device used. For experiments, we used a conventional desktop computer with Microsoft Windows 10 and the Leap Motion device placed on the table at a normal room lighting conditions. Before the study, the participants were asked to remove the rings, watches, because they could affect the results. The output of the Leap Motion controller representing a three dimensional spatial model of subjects' hands was displayed to subjects using the Oculus Rift DK2 device.  
The participants were asked to perform 24 gestures analogous to the letters of the ASL [45] (see Figure 5). The gesture of each letter was performed ten times resulting in a total of 2880 data samples. We recorded the gestures of the participants' hands in the Leap Motion environment and took pictures of real gestures shown by hand. Subsequently, the data from this study were analysed.  
To evaluate the accuracy of the results, we divide the collected dataset in a train and a test sets by using the LOPO (leave-one-person-out) subject-independent cross-validation strategy. The results are averaged to obtain the resulting accuracy.  
Processing and visualization of results presented in this paper was done using MATLAB R2013a (The Mathworks, Inc., Natick, MA, USA).  
For the text input experiments using the ASL, we used 18 pangrams (i.e., sentences using every letter of an alphabet at least once) as follows:  
_A quick movement of the enemy will jeopardize six gunbats._  
_All questions asked by five watched experts amaze the judge._  
_Amazingly few disotheques provide jukeboxes._  
_Back in June we delivered oxygen equipment of the same size._  
_Few black taxis drive up major roads on quiet hazy nights._  
_Five quacking zephyrs jolt my wax bed._  
_Fix problem quickly with galvanized jets._  
_Glip jocks quiz nymph to vex dwarf._  
_How vexingly quick daft zebras jump._  
_Jackdaws love my big sphinx of quartz._  
_Jived fox nymph grabs quick waltz._  
_My girl wove six dozen plaid jackets before she quit._  
_Pack my box with five dozen liquor jigs._  
_Sphinx of black quartz, judge my vow._  
_The five boxing wizards jump quickly._  
_The quick brown fox jumps over a lazy dog._  
_The wizard quickly jinxed the gnomes before they vaporized._  
_Woven silk pyjamas exchanged for blue quartz._  
The pangrams have been used for typing using gesture recognition by Leap Motion before [46].  
Figure 5: Alphabet Gestures for American Sign Language Alphabet.
## 4 Case Study: Damage Identification of a Jacket Platform  
### Finite Element Modeling  
The paper selects a representative jacket platform as shown in Figure 4. The jacket structure is composed of four main tubes connected by horizontal and diagonal members. There are two platforms upon the mean water level, and their sizes are both 30 \(\times\) 20 m in plane view. The vertical dimension between the waterline and the top deck is 15 m. The overall height of the structure is 65 m. Detailed dimensions are shown in Figure 4. During the finite element calculations, the ANSYS software was used. Deck beams are square steel tubes, and all the other members of the platform are circular steel tubes. The section parameters and finite element types of the structural members are listed in Table 1. During the damage identification, 12 sensors are used to measure the local structural deformation. The sensors are located at important nodal positions close to the water surface; see Figure 4.
## 4 Software Tools
### Replaying the Network Traffic  
The final testing stage in the laboratory infrastructure is to recreate the previously modified network traffic and conduct tests for the test scenarios. The following two software tools have been used during our study: Tcreplay (ver. 4.4.4) and Caldera (ver. 4.2.0).  
Tcreplay replays recorded network traffic from.pcap files. It is frequently used for performance evaluation, security analysis, and the testing of network applications and hardware [65]. Tcreplay replays the network traffic exactly the way it was recorded. As a result, we can test the network by considering their response to specific traffic patterns.  
Tcreplay can replay packets to help test the performance and effectiveness of firewalls and IDS/IPS systems. As a result, we may replay traffic containing known attacks to see if the implemented detection algorithms work.  
Tcreplay can replay large.pcap files with a large number of packets in a short period of time. It allows us to evaluate the network's reaction to high traffic and helps us to simulate denial-of-service (DoS) or DDoS attacks.  
Tcreplay offers the tcprewrite utility, which lets the user modify.pcap files before replaying them. Different settings can be modified, e.g., the IP and MAC addresses, source and destination ports, and other fields in packets, which is useful if the replayed network traffic is redirected to a specific machine or the device under test requires specific addresses.  
Caldera was developed by MITRE (McLean, VA, USA) [66]. It is an open-source platform for simulating cyberattacks and automating security testing based on techniques listed in the ATT&CK framework [67]. It can perform simulations of realistic attack scenarios in a controlled environment. As a result, we can evaluate and assess the systems' ability to detect and respond to threats. Although Caldera does not replay the recorded network traffic like Tcreplay, it uses various offensive techniques to replicate adversary actions, e.g., advanced attacks. Caldera emulates the entire attack lifecycle and simulates techniques dynamically to reflect real-world threat scenarios.
## 3 Materials and Methods
### Grid-Side Converter Control System  
The block diagram of the grid-side converter control system is shown in Figure 8. This reveals many similarities with Figure 6, especially on the current loops, although some differences occur. In this regard, the same symbols used for M/G voltage and current space vectors, i.e., \(v_{dq}\) and \(i_{dq}\), are employed also for the corresponding grid-side quantities for the sake of simplicity, although they correspond to different variables.  
First of all, grid phase voltages (\(e_{rst}\)) are processed by a phase-locked loop (PLL) to compute both magnitude (\(E\)) and displacement (\(\varphi\)) of the fundamental voltage space vector. Subsequently, \(E\) is employed for limiting grid complex power \(S_{g}\) in accordance with  
\[\left|\overline{S}_{g}^{*}\right|\leq\frac{3}{2}E\ \hat{l}_{g}\,\ S_{g}=\frac{3}{2}E \cdot\ conj\left\{i_{dq}\right\} \tag{12}\]  
in which \(\hat{l}_{g}\) is the maximum allowable grid current, while \(conj\) denotes the complex conjugate operator. The grid space vector displacement is instead employed for converting grid phase currents (\(i_{rst}\)) into the corresponding \(dq\) space vector. The two PI regulators can be thus designed in accordance with the following voltage relationship:  
\[\widetilde{v}_{dq}=r_{g}\ i_{dq}+L_{g}\frac{di_{dq}}{dt}\,\ \widetilde{v}_{dq}=v_{dq}-j\ \omega L\ i_{dq}-E \tag{13}\]  
in which \(\omega\) is the grid pulsation, while \(r_{g}\) and \(L_{g}\) are line resistance and inductance, which also account for the grid filter. Once \(\widetilde{v}_{dq}^{*}\) has been determined, the same procedure already described for the M/G control system applies, except that rotor position is replaced by grid space vector displacement.
## 4 Results
### Performance of Several Window Length  
Figure 4 shows RMSE, obtained by different-window-length SWA ranging from 50 to 2077 samples at 50 sample intervals and averaging across all long channels. Estimation errors, which were estimated by SWA with shorter than 350-sample closing to the single-trial sample length (i.e., \(L<350\) samples), were substantially increased. In contrast, RMSE decreased gradually when we employed longer-sample-wide sliding-window than single-trial-samples window (i.e., \(L\geq 350\) samples). We also checked that the means of RMSE across activated- or non-activated-channels showed similar results shown in Figure 4.  
Figure 3: Comparisons of performance indicators: root-mean-squared error (RMSE) and adjusted \(R^{2}\). (**A**) RMSE and (**B**) adjusted \(R^{2}\) of rt-ShortPCA GLM were compared with on-line methods and with (**C**) off-line. _Open circles_ show means of each subject across all channels. _Filled circles_ and _error bars_ represent the mean and standard deviation across subjects, respectively.