esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Positioning of the Edge Cloud Model _wrt._ Existing Solutions
\begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline
**Criteria** & **Cloud Computing/** & **CDN** & **Cloud-based CDN** & **Proposed Model** \\  & **SaaS (Amazon EC2)** & **(Akamai)** & **(MetaCDN)** & \\ \hline Virtual & No notion of client & No notion of client & No notion of client & Virtualize user’s client \\ client & virtualization, however & virtualization. & virtualization. & through computing \\  & has provision to & & & support at the Edge \\  & implement virtual & & & Cloud. \\  & client. & & & \\ \hline Deliver & Service and content. & Content only. & Content only. & Service and content. \\ \hline InP’s role & InPs are not involved. & InPs are not involved. & InPs are not involved. & Active involvement of \\  & Cloud computing & CDN provider & Cloud provider & the InPs who maintains \\  & provider maintains the CDN. & maintains the Storage & & the Edge Cloud \\  & Cloud infrastructures. & & Cloud and MetaCDN & infrastructures. \\  & & maintains the & & \\  & & & redirection utility. & \\ \hline At the & Nothing is required. & Redirection, caches & Nothing is required. & Edge Cloud that \\  & edge & and local storages. & & implements computing \\ \hline Business & Cloud, I/P/SaaS and & CDN and CPs are in & Storage Cloud, & InP, VNP (in case of \\ model & service providers are in & the value chain. & MetaCDN and CPs are in the value chain. & content overlay) and \\  & the value chain. & & in the value chain. & CP/SP are in the value \\ \hline Potential & Need major & Need major & Medium/small & Need small investment, \\ providers & investment, suitable for & investment, suitable for & companies (e.g., & good for even \\  & industry giants. & industry giants. & MetaCDN) could & medium/small \\  & & & invest in content & companies. \\  & & distribution business. & \\ \hline Potential & Cheap service, & Pricey service, & Cheap service, suitable & Cheap service, suitable \\ vendors & usage-based billing, & long-term contract, not & for small to medium \\  & suitable for small to & suitable for small & companies. & for small to medium \\  & big companies. & companies. & companies. \\ \hline Latency & High, especially if the & Low, when the content is delivered from local & Low, when the utility & Low, when the content \\  & Cloud is deployed far & is delivered from local & function finds a replica & is delivered from local \\  & away from user. & cache. & in a close proximity. & storage. Low for \\  & & & computing also. \\ \hline Require & High, specially for & Low for simple & Low for simple & Low for simple \\ bandwidth & data-intensive & content, high for rich & content, high for rich \\  & computing. & content (e.g., video & content (e.g., video \\  & & streaming). & computing. \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of proposed model with existing content/service delivery models.
## 5 Experiments
### Throughput of Write Operations
#### 5.3.2 Local Network  
As a virtualized environment is more suitable for intensive testing, a greater load test was performed to see the differences between PoA and PoW in terms of throughput. The used tool [109] is able to send a batch of transactions between externally-owned accounts in an automated way. The experimental results were recorded by collecting the data of a Geth node of the network.  
The network was set up with 16 nodes and 4 validators, configuring the gas limit to 1 million units of gas. Regarding the options provided by Pandoras, the transactions were set to EOA type, and the batch size was set to 1 (i.e., one transaction at a time). A fixed number of transactions was emitted (250, 500, 750 and 1000) and the resulting transactions per second (TPS) were averaged 100 times per point.  
The first set of tests measured the TPS of the network using PoW and PoA, when both made use of the same network parameters. The obtained results are shown in Figure 14. As it can be observed, PoA performs better with a moderate number of emitted transactions (up to 1000). As the number of transactions increases, the differences are reduced significantly, mainly because of setting the gas limit to 1 M units, which becomes rapidly a bottleneck for the throughput of the network.  
The influence of the gas limit was analyzed in a second set of tests that explored how TPS evolved when using different gas limits (from 1 to 60 million units of gas), while keeping the same consensus protocol (PoA) and network parameters unchanged. The obtained results are shown in Figure 15, where it can be observed that increasing the gas limit derives into increased TPS. However, there is a point in each curve where transaction throughput stops increasing, which is mainly due to the number of emitted transactions and to the impact of other blockchain parameters. In any case, in a realistic scenario, a gas limit of 1 M units of gas should be enough to deal with an eventual complex data manipulation for this application (as of writing, the public network of Ethereum uses 30 M as the gas limit).  
It is important to acknowledge the limitations of the aforementioned experiments. A network up to 16 nodes cannot be considered fully decentralized. However, such a scale allows researchers and developers to easily perform specific tests. In this case, the scale of these networks allows for finding out the performance and the energy consumption gap between both consensus algorithms and to localize bottlenecks.  
Figure 13: Evolution of the TPS for the tested testnet.
# A Game Theoretic Framework for Surplus Food Distribution in Smart Cities and Beyond
## 2 Materials and Methods
### Real-Time Operation  
**Lemma 4**.: _FDRM-CA produces results in real (polynomial) time._  
Proof.: We only analyzed the time complexity of a single iteration of the FDRM-CA mechanism since after each iteration agents receive their assignments. Since food is only recoverable inside a time window, we will focus our analysis from the donor point of view. For this, we analyzed the time complexity of each sub-process of the mechanism. Note that receivers with large requirements may acquire their complete matching (that is equal to their total requirement) after several iterations of the mechanism.  
* The NUIR sub-process is parallel to the main process and thus does not have any impact on the time complexity as it does not need to finish for the main process to execute;
* The CTFU sub-process takes \(\mathrm{O}(d+r+v)<\mathrm{O}(r^{2})\);
* The CA-DTB sub-process can be viewed as having two sub-parts:
* The sub-part to determine the neighbourhood of each donor and volunteer assignment to the donor takes \(\mathrm{O}(dv)<\mathrm{O}(r^{3})\);
* The update of preference and match generation sub-part has a time complexity of \(\mathrm{O}(d^{2}r)<\mathrm{O}(r^{3})\), since \(r>d\) for most practical situations, especially in developing countries such as India, at the time of writing this paper.  
where \(d\), \(r\), and \(v\) are the numbers of donors, receivers, and volunteers, respectively, per iteration of the FDRM-CA mechanism. Therefore, the effective time complexity of each iteration of the FDRM-CA mechanism is capped at \(\mathrm{O}(r^{3})\), which is a polynomial (real) time. This concludes the proof.
## 5 Result and Discussion
### Comparative Methods
#### 5.5.1 Comparative Analysis Based on TP  
Figure 6a-d show the comparative analysis based on TP for accuracy, NPV, PDV, and threat score. The BCHO-TCN LightGBM model demonstrates superior accuracy in predicting out-of-stock events, with a 2.13% improvement over the vulture-TCN LightGBM model, achieving an accuracy of 94.52%.  
Figure 5: Performance analysis based on k-fold: (**a**) accuracy, (**b**) NPV, (**c**) PDV, and (**d**) threat score.  
The BCHO-TCN LightGBM model shows better performance in predicting out-of-stock situations compared to the vulture-TCN LightGBM model. It surpasses the vulture-TCN model by 1.28% and achieves an NPV of 95.16% with a TP of 90.  
The BCHO-TCN LightGBM model outperforms the vulture-TCN LightGBM model in out-of-stock prediction by a margin of 13.08%. With a TP of 90, it boasts a PPV of 94.81%, surpassing existing methods.  
The BCHO-TCN LightGBM model shows better performance in predicting out-of-stock events compared to the vulture-TCN LightGBM model. It surpasses the vulture-TCN model by 6.81%, achieving a threat score of 95.76% with TP 90.
## 4 EAT Segmentation Methods via Pericardium Delineation
### Atlas-Based Methods  
Atlas-based methods can be beneficial for the task of pericardium delineation since the pericardium line can be sparse and fuzzy, in some cases leading to complete failures of methods such as active contours. Atlas-based segmentation can be more robust to these cases since they rely on more complex features than active contours. A representative fully-automatic method for EAT classification, presented in [PERSON] et al. [25], employs multi-atlas segmentation to segment the pericardium on non-contrast CT images. It is much easier to see the pericardium on contrast-CT images, so the atlases were collected by manually segmenting the contrast-CT scans of 8 patients. These atlases were then registered onto the non-contrast input images, and the registrations are then fused to produce a final pericardium segmentation. Once the pericardium is segmented, the total EAT segmentation and quantification are done by thresholding the segmented portion of the image followed by a connected component analysis step to remove noise from the EAT volume. The authors  
Figure 9: A summary of the method presented in [PERSON] et al. [42]. A CT scan is first registered to obtain an initial counter and a line detector is used to find the pericardium line. The detected line is refined using active contours.  
reported Dice coefficients of 89.1 and 89.2 on the remaining scans compared to manual segmentations of two experts on 98 patients' scans.
# Underwater Excavation Records Using Underwater Acoustic Survey: A Case Study in South Korea
## 3 Dangampo Site: Equipment Testing
### Experimental Setup
#### 3.1.3 Scanning Sonar  
An SS is an underwater sounding apparatus that measures pressure by transmitting a sound wave from a transducer and receiving echoes reflected from underwater targets, similar to the SiSS. However, the SS operates from a fixed position on the seafloor, where it performs 360-degree sonar sweeps and produces complete circular images.  
When operating an SS system, a transducer is usually placed on the seafloor mounted to a tripod. The tripod is designed to keep the transducer perpendicular to the seafloor even if it is tilted. In addition, a weight is attached to its bottom to resist tidal currents.  
The SS used at the Dangampo site was a Kongsberg Mesotech 1171 series sonar. Its operating frequency and range were 600-1200 kHz and 0.5-100 m, respectively. At the site, we selected a survey area of 260 m (latitudinal) \(\times\) 180 m (longitudinal) because measuring the entire site would be a very time-consuming process due to the necessity of moving and reinstalling the SS several times to obtain high-quality data for each section. While lengthening the range to survey a larger area in one sweep is possible, this approach significantly reduces the resolution as a longer range degrades the reflection coefficient.  
An SS survey of the site was conducted during the highest tide on October 14 and 15 to ensure the best data quality by minimizing the effect of tidal currents on the transducer. Prior to the survey, we checked the coordinates of the 24 positions to install the SS and acquired data by descending the tripod on which the transducer was mounted from the rubber boat to the seafloor. The frequency and range were set to 675 kHz and 30 or 50 m. After operating the SS for two days, data were obtained from only eight of the originally planned 24 target positions due to the unexpectedly long SS installation time and short duration of the high tide period necessary for surveying. The SS survey of the SRA was conducted on October 27. We obtained data with the transducer placed next to the SRA, with the frequency and range set to 675 kHz and 25 m, respectively.
## 3 Results and Discussion
### TG-DSC Study of Salt-Assisted LTO Formation  
The thermogravimetric and differential scanning calorimeter (TG-DSC) analysis of LTO-0 wt % NaCl and LTO-4 wt % NaCl are depicted in Figure 7. It is clearly observed that both curves show a similar trend. At temperatures between 0 and 100 \({}^{\circ}\)C, there are weight losses around 12.06 and 10.10 wt % of LTO-0 wt % NaCl and LTO-4 wt % NaCl, respectively, which indicated the removal of absorbed water in the LTO precursor, according to the endothermic peaks at 107.5 and 95.8 \({}^{\circ}\)C [64]. At temperatures between 100 and 415 \({}^{\circ}\)C, both curves show a flat straight line, which indicates that no mass change is occurred. However, at temperatures of 411.1 and 406.8 \({}^{\circ}\)C, the endothermic peak is detected, which indicates the melting point of LiOH [65]. Then, the small weight losses of LTO-0 wt % NaCl and LTO-4 wt % NaCl (4.35 and 4.11 wt %) that occurred at 454.6 and 441.5 \({}^{\circ}\)C, respectively, can be interpreted as the hydroxylation [66]. While the temperature increases to 612.8 and 587.2 \({}^{\circ}\)C of both samples, a small weight loss is observed in each curve (3.13 and 3.08 wt %) corresponding to tiny endothermic peaks at 612.8 and 587.2 \({}^{\circ}\)C, respectively, which is attributed the reaction of LTO. The formation of spinel LTO-0 wt % NaCl and LTO-4 wt % NaCl occurred completely at temperatures of 717.4 and 624.6 \({}^{\circ}\)C, respectively, indicating no mass change occurred. However, a new endothermic peak is observed at 796.1 \({}^{\circ}\)C in LTO-4 wt % NaCl, which corresponds to the melting point of NaCl [67]. This phenomenon confirmed the XRD analysis that the addition of NaCl alters the sintering behavior. The crystal growth of LTO-4 wt % NaCl initiates earlier at 624.6 \({}^{\circ}\)C, whereas LTO-0 wt % NaCl initiates at 717.4 \({}^{\circ}\)C, which clarifies the higher crystallite size of LTO-4 wt % NaCl than LTO-0 wt % NaCl.
# RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach +
## 1 Introduction
Moreover, in the given works, the act of profiling a wireless device based on its RSSI values strongly relies on these two very assumptions. However, in our recently conducted study, the two assumptions (RSSI samples are stationary and i.i.d.) have come under scrutiny. Namely, through our extensive real-world experimentation, we have observed that RSSI values measured by a receiving node are highly affected by changes (e.g., moving objects) in their operating environments. In particular, we have observed that moving human bodies (and their absence) have a noticeable effect on RSSI values of IoT devices deployed in a residential environment, and as a result the variance of the RSSI time-series changes significantly when occupants are present and move around the property--we call this effect _time-series clustering_[9] (refer to Figure 1 where there are two different clusters, one with lower volatility than the other). Furthermore, it is clear from the depicted figure that there is a correlation between neighboring RSSI values; therefore, it would be hard to justify the claim that neighboring RSSI values are independent (as presumed by previous works [6; 7; 8]). Except in a few usage cases where there are no moving objects in the environment (e.g., farmland monitoring), most real-world IoT networks deploy computing/sensing nodes in environments with some number of movable objects. Thus, in order to account for changes in RSSI values due to the above described clustering effect, it is necessary to have an adaptive and/or multi-model RSSI-based profiling scheme that will be able to improve/reduce the rates of false positives (in our previous work [10], we demonstrated how i.i.d. assumption pertaining to RSSI values can lead to probable evasion of detection systems that rely on RSSI-based profiling). In this work, we have proposed and studied a multi-classifier system to profile IoT devices based on their RSSI values under two moving object conditions (presence vs. absence of objects in the surrounding environment). Also, our profiling approach takes into consideration the relationship between neighboring RSSI values in the time-series to further improve the accuracy and robustness of IoT node profiles. The content of this paper is organized as follows: In Section 2, we discuss some of the notable previous works in RSSI-based MAC address spoofing detection. In Section 3, we present the threat model and the main assumptions about the adversary's capabilities as pertaining to our work. In Section 4, we propose our LSTM-based (Long Short-Term Memory) profiling scheme that has been devised to detect and classify MAC-spoofing traffics. In Section 5, we discuss adversarial traffic generation used to test the robustness of our approach and compare the effectiveness of our approach with the state-of-the-art RSSI based approaches previously proposed to deal with adversarial attacks.
Therefore, DnaK could control the activation of several TA systems in a manner comparable to that of Rv1957 from TAC. Further reinforcing the potential link between molecular chaperones and toxin activation, an independent study showed that overexpression of the HipA toxin of the type II TA system HipAB facilitates _E. coli_ adaptation and survival in response to a severe proteotoxic stress induced by the lack of both major chaperones Trigger Factor and DnaK [80]. More work is warranted to elucidate such fundamental links between networks of stress-induced molecular chaperones and TA systems in bacteria. _M. tuberculosis_ H37 Rv possesses two ParDE systems (Figure 1). ParDE was originally discovered on the broad-host-range plasmid RK2 as a stabilization system [81]. The ParE toxin does not target RNA but acts by inhibiting the DNA gyrase, thereby blocking DNA replication [82]. The crystal structure of the ParDE1 complex from _Caulobacter crescentus_ revealed a heterotetrameric complex composed of two homodimers of toxin and antitoxin [54]. As _C. crescentus_ ParD1, the ParD1 antitoxin from _M. tuberculosis_ is predicted to have an RHH DNA binding domain, whereas ParD2 does not seem to contain any conserved domain. Ectopic expression of the ParE2 toxin from _M. tuberculosis_ inhibited both _E. coli_ and _M. smegmatis_ growth, whereas, under the same conditions, ParE1 only affected _E. coli_ growth [15,16]. Thus far, none of these systems were tested in _M. tuberculosis_. _M. tuberculosis_ H37 Rv genome potentially encodes 11 TA systems that do not yet belong to canum00@ell-characterized TA fam This includes the MosAT TA system involved in the maintenance of the SXT conjugative element in _Vibrio cholerae_[85] and the recently discovered lactococcal AbiE bacteriophage abortive infection system [84]. DUF1814 toxins belong to the polymerase nucleotidyltransferase (pol NTase) superfamily and, accordingly, it has been shown that the toxin of AbiE specifically binds GTP _in vitro_[84]. Remarkably, no interaction between the toxin and the antitoxin of AbiE could be detected by co-immunoprecipitation experiments performed in _E. coli_, thus, suggesting that conserved DUF1814-COG5340 pairs might belong to type IV TA systems [84]. This study also revealed that the DUF1814-COG4861 (Rv0836c-Rv0837c) pair might belong to the same AbiE family and thus potentially represents a third type IV member in _M. tuberculosis_.
## 3 Theory of Proposed Method
### Reconstruct Linear Equations  
After filtering outliers, the linear equations in Equation (3) are reconstructed by using the remaining \(m\) measurements as:  
\[\dot{A}_{(m)}\theta_{o}=\dot{b}_{(m)} \tag{15}\]However, the linear equations are always close to ill-conditioned, because the elements in the coefficient matrix differ by orders of magnitude. Besides, the unreasonable sensor layout can also result in the linearly dependence of certain equations, in turn to the ill condition of linear equations. The ill condition of linear equations indicates a large difference in the final location results, with a minor change in the coefficient matrix. Moreover, an ill-conditioned linear equation system always has a large condition number of the coefficient matrix \(\dot{A}\):  
\[cond(A)=||\dot{A}||\cdot||\dot{A}^{-1}|| \tag{16}\]  
Besides, location results deviate largely with a minor disturbance in the coefficient. Therefore, a preconditioning method is applied to linear equations to reduce the condition number and give a more accurate closed-form solution, which is named the preconditioned closed-form solution.  
The non-singular and diagonal matrix \(P\) is found, then the solution of Equation (15) is transformed into that of Equation (17) as:  
\[P\dot{A}\theta_{o}=P\dot{b} \tag{17}\]  
where \(P=diag(\frac{1}{s_{1}},\frac{1}{s_{2}},\cdots,\frac{1}{s_{n}})\) and, \(s_{i}=\max\limits_{1\leq j\leq m}|(A)_{ij}|\), \(i=1,2,\cdots,n\).  
Finally, a more accurate location result \(\theta_{o}\) can be obtained by:  
\[\theta_{o}={[(P\dot{A})^{T}P\dot{A}]}^{-1}{(P\dot{A})^{T}P\dot{b}} \tag{18}\]  
due to the improved condition number \(cond(P\dot{A})\ll cond(\dot{A})\).
# Advanced Methods for Photovoltaic Output Power Forecasting: A Review
## 3 AI-Based Techniques Used for PV Output Power Forecasting  
This section describes the most advanced ML and DL-based techniques used in PV output forecasting. With reference to Figure 2 (where papers published in journals between 2010 and 2019 have been considered), the most popular methods use a ML approach (55%) followed by hybrid methodsused (36%). Among DL techniques, ANN-based methods including MLP, RNN, and RBF are the most used (48%).  
Finally, long-term prediction is certainly the most investigated field (90%), and the Italian scholars are the most committed in this area of research.  
In the last few decades, one-step ahead forecasters were the most common, while during the last years due to the progress achieved in the soft-computing techniques and data analysis, multi-step forecast methods are becoming more and more attractive.  
With reference to ML and DL methods, there are mainly three approaches and methods:  
Methods that use only historical output powers record (on site measurement) [40]:  
\[(p_{t},p_{t+1},..,p_{t+k})=f(p_{t-n},p_{t-n-1},p_{t-n-2},\ldots p_{t-1}) \tag{1}\]  
where \(p_{t}\) is the actual power, \(p_{t-n}\) is the previous power, \(p_{t+k}\) is the forecasted power at step \(k\), and \(f\) is a functional dependency between past and future. \(t\in\{1,..,n\}\), \(n\) is the length of the measurements.  
Methods that rely upon forecasted meteorological parameters such as solar irradiance, air temperature, relative humidity, cloud index, wind speed, pressure, etc. These parameters can be from satellite images, numerical weather prediction models, or statistical models:  
\[(p_{t},p_{t+1},..,p_{t+k})=f(G_{t+k},T_{t+k},WS_{t+k},RH_{t+k},C_{t+k}\ldots) \tag{2}\]  
where \(G_{t+k}\), \(T_{t+k}\), \(WS_{t+k}\), \(RH_{t+k}\), and \(C_{t+k}\) are the forecasted solar irradiance, air temperature, wind speed, relative humidity, and cloud cover respectively.  
Figure 2: (**a**) AI-based PV power forecasting method; (**b**) the most common deep learning (DL)-techniques; (**c**) investigated time scale horizons, and (**d**) countries involved in PV forecasting.  
Methods which combine the use of historical power data records with meteorological parameters forecasts:  
\[(p_{t},p_{t+1},..,p_{t+k})=f(p_{t-n},p_{t+n-1},..,p_{t-1},G_{t+k},T_{t+k},WS_{t+ k},RH_{t+k},C_{t+k}\ldots) \tag{3}\]  
These methods can be used both for one-step and multistep ahead forecasting.