esa-sceva/satcom-chunk-collection huggingface dataset:
# Blind Interleaver Parameters Estimation Using Kolmogorov-Smirnov Test
## 1 Introduction
In addition, the new approach estimating interleaver periods without generating additional data has been proposed when the length of the received data is short [11]. They focused on the case that the data collected was so short that matrices could not be sufficiently constructed to calculate rank deficiencies. To solve this problem, the matrix created once from the received data is split into multiple submatrices to calculate the rank deficiency. In this paper, we do not consider the case of short received data and propose an improved approach that utilizes the difference between the random binomial distribution and the rank distribution of matrices composed of codewords. For a blind interleaver parameters estimation, we used the Kolmogorov-Smirnov (K-S) test. K-S statistics has been used in the automatic modulation classification (AMC) algorithm to measure the fitness whether two groups are extracted from the same distribution [12; 13]. We utilized the K-S test as a metric to find the final candidate of interleaver periods with the most different rank distribution from the rank distribution of the random signals. We utilize the K-S test as a metric to make decision whether two groups are extracted from the same distribution. The K-S test value is used to find the most different rank distribution. The K-S test does not include any logarithmic calculation and instead needs only the cumulative distribution function (CDF) of two distributions. Therefore, differences between the two distributions can be easily computed without requiring the complex logarithmic operation that is part of the KLD. Experimental results validated that our proposed algorithm has relatively low computational complexity and outperforms existing algorithms. Furthermore, we can finer control false alarm rates compared to other algorithms by using multinomial distribution. In a noisy channel environment, simulation results demonstrate that the K-S test estimates the interleaver parameter with high estimation accuracy. The rest of this paper is organized as follows. Related work is described in Section 2 In Section 3, the proposed algorithm is explained. The proposed algorithm is compared with conventional algorithms and analyzed based on experimental results in Section 4. Finally, Section 5 conclude the paper.
## 4 Discussion
### Effect of Skin Contact on Volatile Thiols and Esters Concentrations  
Skin contact is known to affect the volatile composition of white wines [2]. This study showed increased concentrations of volatile thiols (3 SH, 3 SHA, and 4 MSP) and some yeast-derived esters for the SC wines. Volatile thiols are present in the grapes as precursors, in their cysteinylated and glutathionylated versions [17]. These precursors are mostly found in the grape skins [18]. Therefore, the increase in thiol content in the wine is due to the greater extraction of precursors from the skins during SC [2; 27; 38].  
As for the ester compounds, the effect of SC was more pronounced for the acetate esters (Table 3). While all six acetate esters showed greater levels for SC, the effect for fatty acid ethyl esters (FAEEs) showed various results, with eight out of eighteen compounds showing higher levels for SC. The greater concentration of acetate esters may be due to the esterification of higher alcohols with carboxylic acids [39]. Higher alcohols, in turn, are a byproduct of amino acid metabolism [40]. Grape skins are rich in amino acids, which are consumed by yeast as a source of nitrogen during alcoholic fermentation and might have contributed to the observed increase in acetate esters in this study. Unfortunately, a more comprehensive analysis of amino acids in the juice was not conducted.  
The remaining ethyl esters showed statistical significance for SC. Ethyl 2-hydroxypropanoate, ethyl 3-hydroxybutanoate, ethyl 2-phenylacetate, and ethyl propanoate were found at greater concentrations with no SC. Ethyl esters have been found at lower concentrations in Chenin Blanc wine that underwent skin contact [41]. Finally, the fatty acids of methyl and isoamyl esters (methyl octanoate, 3-methylbutyl hexanoate, 3-methylbutyl butanoate, and 3-methylbutyl octanoate) were significantly higher for SC wine. Previous studies investigating the effect of SC have not included these compounds, although they have been found in Chardonnay wines [42].  
An important aspect to consider when comparing research investigating skin contact is higher temperatures and longer SC time, which might explain why a different result was observed in our study. Additionally, it was not possible to extend the conclusions from previous work to this study because confounding variables, such as grape vintage and variety, yeast strain, and specific winemaking processes, also affect the volatile composition of wine [43].
# Low-Cost Wireless Sensing System for Precision Agriculture Applications in Orchards
## 3 Results and Discussion  
The system was developed, programmed, and evaluated both in laboratory and field conditions. A set of indoor trials took place in order to assure the undisturbed and continuous operation of the system. The first test included energy consumption and operation under unfavorable solar energy harvesting conditions. According to literature, to enhance the lifetime of the wireless network, the energy consumption should be minimized by using energy harvesting and adjusting the duty cycle [1; 19]. In addition, following protocols according to which the data packets are transmitted to the Coordinator from distant nodes via intermediate nodes, requires smart routing protocols to define the best pathway to limit the number of transmissions, minimizing energy consumption [20; 21].  
In our network, a star communication topology was selected. Therefore, the network operation was simplified and the energy demands were limited. Solar energy harvesting panels were used to charge the batteries powering each node of the network. As in most Wireless Regional Area Networks (WRAN), the system consisted of a base station, referred as Coordinator, and peripheral devices wirelessly connected to the Coordinator, the End-nodes. According to this widely used protocol, the End-nodes only send their data to the Coordinator which is responsible for the management of the whole network's data management [2]. A dedicated API was developed to establish the communication of the wireless agricultural network with the cloud server. The resulting wireless sensors network is intended to become part of a decision support system (DSS) to assist farmers manage their fields in timely and effective manner. The API is designed to link the wireless sensor network with a central farm management information system (farmB; farmB Digital Agriculture P.C., Greece) that automatically receives and processes the data packets providing the results in the form of easily interpretable graphs and tables. The user-friendly interface will ensure intuitive operation of the system to all users. A similar approach was followed by [PERSON] and [PERSON] [22], who developed a software integrated with in-field sensor network using Bluetooth wireless radio communication for site-specific irrigation control.
## 3 WBAN Requirements
### Quality of Service  
QoS is accepted as a measure of the service quality that the network offers to applications and users. Network reliability and guarantees of successful transmission are vital in WBANs due to the criticality of the data [17]. WBANs share many of the QoS issues that are inherent in WSNs: _Resource constraints_: energy, bandwidth, memory, limited transmission power; _Unbalanced traffic_: Potential \(n\) number of nodes communicating with a sink; _Heterogeneous nodes_: variety of sensor node types; and _Packet criticality_: priority traffic based on node criticality and condition type.  
QoS levels within a WBAN environment take on new levels of criticality over and above those for WSNs due to the monitoring of key physiological life signs. Reliable transmission from sensor nodes to BCU and then potentially to remote healthcare professionals over HTTP or GPRS is vital and could be life threatening if not delivered with some guarantees. QoS requirements within a WBAN include high Packet Delivery Ratio (PDR), low levels of packet transmission delay, minimal collisions and retransmissions and efficient balancing of high energy efficiency and reliable transmission. Optimized energy efficiency requires an energy management process that efficiently balances QoS with energy efficiency, adapting to heterogeneous node constraints and wireless channel dynamics. QoS can only be evaluated at the higher layers of the protocol stack while energy consumption largely appears at the lower layers. The QoS for a WBAN solution can provide the core requirements guarantees for any network design where the various protocol stack parameters can be modified and adapted to deliver efficiencies as long as the QoS levels are still fulfilled.
## 4 The \"Call Me Maybe\" Framework  
The 'Call Me Maybe' framework is a design framework for VoIP networks that will provide the network with additional protection against DoS attacks. At its core, the framework is built around the addition of a'shadow' TCP network that can be used as a fallback when the regular network is suffering a (D)DoS attack. Supporting this core idea, 4 principles make up the framework to ensure maximum effectiveness and reproducibility. The 'Call Me Maybe' framework principles are as follows:  
1. The network should provide the capability for VoIP phones to operate over both TCP and UDP.
2. As part of the TCP network, a UDP-restricted firewall should be in place to aid in the mitigation of a UDP flooding attack.
3. The dynamic switch between TCP and UDP should be automatic and in response to latency rises caused by a denial-of-service attack.
4. To enable dynamic switching between TCP and UDP, the network should have in place a form of latency monitoring to capture changes in response times.  
Using the above principles, the 'Call Me Maybe' framework aims to provide an additional layer of security to VoIP networks that can be used in conjunction with any underlying protection mechanisms to mitigate distributed and non-distributed DoS attacks. While TCP and UDP VoIP networks were selected for this research, it is acknowledged that the design is not limited to alternating protocols, and can be used in other scenarios, for example, dynamically switching between VoIP and PSTN. Figure 4 displays a diagram of the processes involved in the framework.
## 3 New Link Adaption Technique in Maritime Environment  
### Link Adaptation  
The VDES provides additional support for adaptive modulation and coding (AMC) techniques. In addition, the LA technique involves the selection of appropriate modulation and coding rates based on the prevailing channel conditions using AMC algorithms. For instance, communication errors will occur in scenarios where a ship is located a considerable distance from the coastline and consequently experiences weakened signal reception from a coastal station. When communication errors occur, the LA technique in the VDES should change the AMC profile to guarantee reliable communication. However, if a ship is close to a coastal station, the AMC profile must be changed to increase channel efficiency. In other words, proper selection of the AMC profile in the LA technique is crucial for increasing channel efficiency based on appropriate channel estimation.  
The LA technique should be operated based on appropriate channel estimation. Therefore, a channel estimation technology that can represent the channel conditions under which the LA technique operates is vital. The representative channel estimation method for the LA technique utilizes a channel reference metric, such as the received signal strength indicator (RSSI) [28]. However, it cannot accurately predict the channel capacity for imminent transmission in maritime communication environments. This is because the maritime communication environment, unlike the general land communication environment, is a channel environment in which periodic RSSI detection is difficult, and special phenomena (deep nulls) occur. Figure 3 shows the modeling of the maritime communication channel environment based on the distance. As shown in Figure 3, the signal is attenuated with distance, but it can be observed that the signal is rapidly lowered owing to rays reflected from the sea surface and extra reactions by the evaporation duct. In other words, in a situation where signal attenuation occurs rapidly, it is difficult to acquire the RSSI in a short period; therefore, a new LA technique is needed.
## 4 Discussion  
Only few studies have evaluated the cephalometric changes on the lateral cephalogram after RME and SME, and to date, only one study [21] has compared the cephalometric parameters following RME and a Ni-Ti memory screw palatal expander. As reported by previous long-term longitudinal investigations, there are contradictory data in the literature on the effects of RME on maxillary anteroposterior positioning, [PERSON] [22] was one of the first authors to report the presence of anterior displacement of the maxilla after maxillary expansion treatment. On the contrary, other authors did not report such displacement, instead showing the not clinically significant sagittal behavior of the maxillary bone [23, 24]. The present research is in accordance with the aforementioned research, as no significant difference was noted in the three groups on the anteroposterior position of the maxilla. Concerning the SNB angle, no significant difference was found in both treated and untreated subjects, concluding that neither the RME nor the Leaf expander affected the mandibular anteroposterior positioning. These findings are in accordance with those previously published by [PERSON] et al. [25] and other previous longitudinal studies [26, 27, 28]. Regarding the palatal plane inclination (S-N \(\wedge\) SNP-SNA), it remained unchanged in both treated and untreated groups during the entire study period. The literature shows several responses of the palatal plane secondary to maxillary expansion, from anterior-superior to anterior-inferior rotation [29, 30, 31]. Considering the lack of difference between the treated and untreated groups, it does not seem reasonable to attribute any influence in the inclination of the palatal plane to the RME. [PERSON] and [PERSON] [21] evaluated the effects of a conventional Hyrax-type RME and a Ni-Ti memory screw with similar properties to the leaf expander to compare their sagittal and vertical dentrofacial effects. All treated subjects obtained the midpalatal sutural opening and subsequent important skeletal and dental expansions. The maxilla moved anteriorly and inferiorly in both groups, while the mandible rotated inferiorly and posteriorly much more for the Ni-Ti-screw group. The Ni-Ti screw produced maxillary expansion using lighter forces over a shorter time. The RME using the memory screw developed similar sagittal and vertical changes to those produced by the Hyrax screw. The present study showed after RME and Leaf expander treatment a statistically significant spontaneous retraction of the upper incisors with a decreased in the U1 \(\wedge\) S-N and U1 \(\wedge\) SNP-SNA angles. These findings are in accordance with [PERSON] et al. and [PERSON] et al., who demonstrated a significant posterior movement of the upper incisors following RME therapy. This effect is probably due to the deciduous anchorage that
## 2 Results
### Static Adsorption and Desorption
The model has the following form:  
\[\log^{\mathrm{q_{e}-q_{t}}}=\log^{\mathrm{q_{e}}}-\mathrm{k_{1}t} \tag{3}\]  
where \(\mathrm{k_{1}}\), \(\mathrm{q_{e}}\) (mg/g), and \(\mathrm{q_{t}}\) (mg/g) represent the rate constant of first-order adsorption (min\({}^{-1}\)), the amounts of adsorption at equilibrium and at time t (min), respectively. The pseudo-second-order model [26] is expressed as:  
\[\frac{1}{\mathrm{q_{t}}}=\frac{1}{\mathrm{k_{2}q_{e}^{2}t}}+\frac{1}{\mathrm{ q_{e}}} \tag{4}\]  
where \(\mathrm{k_{2}}\) represent the rate constant of pseudo-second-order adsorption (g/mg/min). The rate constant (\(\mathrm{k_{2}}\)) and adsorption at equilibrium (\(\mathrm{q_{e}}\)) can be obtained from the intercept and slope, respectively [27]. The parameters used for the pseudo-first-order and pseudo-second-order equations are shown in Table 3. The results showed that the theoretical \(\mathrm{q_{e}}\) value estimated from the pseudo-second-order equation was close to the experimental value (R\({}^{2}>\) 0.99). The desorption solvent was selected according to the ionization of the adsorbent and the solubility of glucoerucin in the desorption solution. As shown in Figure 7, the desor \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Temperature (\({}^{\circ}\)C)** & \multicolumn{3}{c}{**Langmiur Model**} & \multicolumn{3}{c}{**Freundlich Model**} \\ \hline  & \(\mathrm{q_{e}}\) (mg/g) & \(\mathrm{K}\) (mg/mL) & \(\mathrm{R^{2}}\) & \(\mathrm{K_{f}}\) (mg/g) & \(\mathrm{1/n}\) & \(\mathrm{R^{2}}\) \\ \hline
20 & 52.91 & 14.54 & 0.9916 & 52.69 & 0.1855 & 0.9924 \\
30 & 64.94 & 7.333 & 0.9879 & 59.85 & 0.2737 & 0.9952 \\
40 & 69.93 & 6.217 & 0.9880 & 63.28 & 0.2974 & 0.9893 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Langmuir and Freundlich adsorption parameters of glucoerucin on PGMA-III adsorbent at different temperatures. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Experimental** & \multicolumn{3}{c}{**Pseudo-First-Order Kinetic**} & \multicolumn{3}{c}{**Pseudo-Second-Order Kinetic**} \\ \hline \(\mathrm{q_{exp}}\) (mg/g) & \(\mathrm{k_{1}}\) (min\({}^{-1}\)) & \(\mathrm{q_{e}}\) (mg/g) & \(\mathrm{R^{2}}\) & \(\mathrm{k_{2}}\) (g/mg/min) & \(\mathrm{q_{e}}\) (mg/g) & \(\mathrm{R^{2}}\) \\ \hline
60.24 & 0.0312 & 103.4 & 0.8803 & 0.0003922 & 71.43 & 0.9920 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The pseudo-first-order kinetic model and pseudo-second-order kinetic model adsorption parameters of glucoerucin on PGMA-III adsorbent at 30 \({}^{\circ}\)C.  
Figure 6: Adsorption kinetics for glucoerucin on PGMA-III adsorbent at 30 \({}^{\circ}\)C.  
increased with increasing concentrations of aqueous ammonia or KCl. However, there were no significant differences between the maximum desorption ratios obtained using aqueous ammonia (82.08%) or KCl (83.12%) as the desorption solvents; either could be used to elute glucoerucin from the adsorbent.
# Fast and Improved Real-Time Vehicle Anti-Tracking System
## 3 Implementation and Results
### Detection System  
Figure 17 shows the rate of false negatives (FN), false positives (FP), true negatives (TN), and true positives (TP) in the system's vehicle detection success rate. The proposed method has a high success rate in both positive and negative results, which means high reliability. The success rate is calculated by the following formulas:  
\begin{tabular}{l} True-Positive Rate = TP/TP + FN \\ False-Positive Rate = FP/FP + TN \\ True-Negative Rate = TN/TN + FP \\ False-Negative Rate = FN/FN + TP \\ \end{tabular}  
Figure 16: Tracked vehicle.  
Figure 17: Detection success rate and comparison with other methods: Simple adaboost, Canny edge [7], TBMOD [8] and Support vector machine [15].  
Figure 15: Detected vehicles.  
For good classifiers, TPR and TNR both should be near to 100%. On the contrary, FPR and FNR both should be as close to 0% as possible.  
The proposed system is also compared to other methods that give the best results. We applied their concepts and algorithms to our system under the same circumstances and compared the overall results. The total system computation time of the proposed multi-vehicle detection system is up to 51 ms, and it is possible to operate perfectly at a frame rate of up to 30 frames per second.
# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 2 JSC Network Configurations
### Bistatic Deployment
#### 2.2.1 The Blind Zone Problem  
In bistatic configurations, blind zones can occur in the area surrounding the baseline between the transmitter and receiver due to limited delay and angle resolution. This creates a minimum ellipse within which the transmitter-target-receiver path becomes indistinguishable from the direct path. In vehicular applications, these blind zones can jeopardize collision avoidance and object detection, particularly when vehicles are traveling at high speeds. The extent of the blind zone is influenced by the signal bandwidth and bistatic range resolution [24]. However, cooperation among sensors in the multistatic setup described below can help mitigate this issue.
## 3 Results and Discussion
### HS-SPME/GC-MS Analysis Results
#### 3.2.1 Esters and Lactones  
Ester compounds play an important role in the aroma of coconut products. Esters are formed by the enzymatic condensation of organic acids and alcohols [30]. In fresh coconut water, the main esters are ethyl hexanoate, ethyl octanoate, ethyl decanoate and ethyl dodecanoate. In particular, ethyl decanoate and ethyl dodecanoate are present at the highest concentrations and represent approximately half of all the esters present [23; 31]. Ethyl decanoate, ethyl dodecanoate and hexyl formate represented about 82%-92% of the total ester content of the coconut jam samples. As shown in Table 1, coconut jam has the same ester aroma compounds as fresh coconut water, with only a few differences, indicating that the coconut jam basically retains the original flavor of the coconut.  
Lactones are cyclic esters with a fruity aroma. In this study, two lactones (delta-nonalactone and delta-dodecalactone) were detected. Delta-nonalactone has fruit and dairy odor characteristics [32]. Delta-dodecalactone has a sweet and fruity scent [33]. It is worth noting that the content of delta-nonalactone kept rising during the heating process and reached the highest value after sterilization, which enhanced the fruit aroma of coconut jam and made its flavor more unique.
# Web Mapping for Farm Management Information Systems: A Review and Australian Orchard Case Study
## 3 Case Study Materials and Methods
### Adjustment of Misalignment  
A 2D conformal transformation was devised to align GNSS data and GEI. If \(X_{GEI}\) represents coordinates in GE imagery and \(X_{GNSS}\) represents observed GNSS coordinates over the checkpoints of a given area, a 2D dimensional similarity transformation [64] can be presented as:  
\[X_{GEI}=sRX_{GNSS}+\Delta X \tag{7}\]  
where \(s\) is the scale factor, \(R\) is the rotation matrix of the x-axis cosine angle (\(\theta\)) in a counterclockwise direction, and \(\Delta X\) is the translational vector. The rotation matrix can be described as:  
\[R=\begin{vmatrix}Cos\theta&-Sin\theta\\ Sin\theta&Cos\theta\end{vmatrix} \tag{8}\]  
If \(a=sCos\theta\) and \(b=sSin\theta\), the transformation equation can be represented in matrix form as:  
\[\begin{vmatrix}E_{i}\\ N_{i}\end{vmatrix}_{GEI}=\begin{vmatrix}E_{i}&-N_{i}1&0\\ N_{i}&E_{i}&0&1\end{vmatrix}_{GNSS}\begin{vmatrix}a\\ b\\ \Delta E\\ \Delta N\end{vmatrix} \tag{9}\]  
where:  
\[s=\sqrt{a^{2}+b^{2}} \tag{10}\]  
\[\theta=\tan^{-1}\frac{b}{a} \tag{11}\]  
In the special case of when the scale factor is unity and there is no rotation, the matrix equation is represented as:  
\[\begin{vmatrix}E_{i}\\ N_{i}\end{vmatrix}_{GEI}=\begin{vmatrix}E_{i}\\ N_{i}\end{vmatrix}_{GNSS}+\begin{vmatrix}\Delta E\\ \Delta N\end{vmatrix} \tag{12}\]  
A 2D conformal transformation may not present a perfect solution for a set of control points. A least squares solution was, therefore, used to compute transformation parameters to minimize error on coordinate pairs.