esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To achieve a more comprehensive examination of the existence of stitching tampering in digital images, a multi-feature tampering detection algorithm combining chroma, sharpness, and natural scene statistics (NSS) is proposed in this paper. The color filtering feature of complementary color wavelet (CCW) [15] is used to obtain the chromatic change of the image. Sharpness detection is then applied to detect the change in the sharpness of the image. Fused with texture differences and distortion statistics extracted by NSS, it is shown that a multi-feature classifier implemented by the random forest classification algorithm can comprehensively detect whether the image has been tampered with. The proposed model's performance is evaluated on a newly built tampering detection dataset based on the ImageNet database (Stanford University, CA, USA) [16] (hereinafter referred to as \"Tampering ImageNet\"), Columbia Uncompressed Image Splicing Detection Evaluation Dataset (Columbia University, NY, USA) [17] (hereinafter referred to as \"Columbia Dataset\"), and CASIA Image Tampering Detection Evaluation Database V2.0 (CASIA ITDE V2.0) (Institute of Automation, Chinese Academy of Sciences, Beijing, China) [18]. The experimental results show that the proposed algorithm favorably outperforms the existing methods in the literature, in terms of the accuracy, precision, recall, and F1-score evaluation metrics. The main contributions of this paper are listed as follows:  
* Besides adopting other existing datasets, a dataset of over 500 pairs of stitching tampered/untampered images is built for training and evaluating our model. It is named Tampering ImageNet because its entire untampered content is drawn from the existing image classification public ImageNet dataset, which has been manually prepossessed and optimized to overcome most shortcomings of the existing related public datasets, with an untampered vs. tampered images ratio of one, and with the minimum content size able to achieve the maximum possible tampering detection performance. * In contrast to image single-feature-based methods, within this research work, novel multi-feature extraction techniques (CCW and NSS) are introduced, and innovatively applied for the first time to image stitching tampering detection, making full use of image chroma information and texture differences and distortion. * In this paper, a newly designed architecture of a random forest model for stitching tampered/untampered image classification is proposed, and the model is trained on the previously extracted multi-feature data. The rest of this paper is organized as follows: In Section 2, a multi-feature method for image tampering detection based on CCW chroma detection, sharpness detection, and texture difference and distortion statistics described by NSS is presented. In Section 3, the algorithm proposed in this paper is used to detect images and tampered images on several public benchmarked datasets, and the classification accuracy is compared with the existing algorithms. Finally, Section 4 summarizes this article.
## 2 Materials and Methods--Implementation, Integration, and Testing of the IDEASSat Flight Model
### Ground Station
#### 2.4.1 S-Band Ground Station  
The S-band ground station at NCU uses a COTS solution provided by Comtech Telecommunications Corp and the QubeFlex satellite modem from Teledyne Paradise Datacom for signal reception and signal processing [21; 22]. The use of the COTS modem was motivated as a result of the self-developed GNU Radio signal processing software exhibiting an unacceptably high bit error rate when operated at S-band. The demodulated S-band data packets are transmitted to a PC using the user datagram protocol (UDP), and depacketized using self-developed Matlab processing software.  
The IDEASSat S-Band ground system only had a receiving function. Figure 6 is the ground receiving station architecture of IDEASSat S-band. A 3.4 m diameter parabolic dish antenna with an L-band/S-band feed is installed on a rotator inside a radome. A 2200-2400 MHz bandpass filter (BPF) was also installed behind the feed in order to block interference from S-band Wi-Fi and Bluetooth signals. A low noise amplifier (LNA) is included as part of the COTS Comtech antenna solution, serving to amplify signals received by the antenna feed to make up for losses while propagating along the cable connecting the antenna to the satellite modem. The satellite tracking software included as part of the COTS Comtech system can automatically update the two line elements (TLEs) for tracked satellites via the Internet, and can continuously receive GPS time as the basis for clock synchronization. A photograph of the S-band ground station during construction is shown in Figure 7, with the antenna exposed prior to completion of the radome.  
Figure 6: The basic architecture of the NCU S-band ground station.
## 2 Experimental Study
### Experimental Results  
In one test case, when the inverter output power and the power of local load (point c in Figure 2) did not change significantly, the total harmonic distortion (THD) of the inverter output current abnormally rose, as shown in Figure 3c.  
Figure 3: The measurement data for the Photovoltaic Charging Station: (**a**) grid admittance amplitude spectrogram; (**b**) partial enlargement of (**a**); (**c**) THD (total harmonic distortion) of inverter output current.  
Figure 2: The single-line diagram of the PV Charging Station.  
By using the passive line impedance estimation method [20; 21], the grid admittance amplitude spectrogram is obtained from the grid voltage and current information, as shown in Figure 3a. Its partial enlargement is shown in Figure 3b. In Figure 3a,b, the abscissa represents time, the ordinate represents frequency, and the color in the figure represents the amplitude (dB) of the grid admittance, with warm colors representing larger amplitudes and cool colors representing smaller amplitudes.  
As shown in the red dotted square, indicated by the black arrow in Figure 3a, there was a resonance component of grid admittance with about a 10 dB amplitude rising from 1650 Hz to 1730 Hz. It passed through 1730 Hz from below at around 1000 s (see the partial enlargement (Figure 3b) for a clearer view). The THD of the inverter output current also started to increase around t = 1000 s (Figure 3c). This may be caused by a change in the operating state of other loads in the grid.  
The undesired increasing of the THD of the inverter output current can lead to increased copper losses and a shortened life of the transformer, and it can also cause relay protection equipment to malfunction. If the THD continues to increase, it will also cause the inverter to lose stability and stop operation.
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
## 2 Background and Related Work
### TSCH Energy Modeling  
As minimizing the energy consumption is one of the major challenges of IoT networks, much research has already been conducted on this topic. Some of the research already focused on TSCH energy modeling.  
Some works target specific features in TSCH. [PERSON] et al. proposed an analytical model of the IEEE 802.15.4e TSCH CSMA-CA algorithm that is used in shared time slots [17]. The authors also observed that the capture effect has a significant impact on the performance of the CSMA-CA algorithm. [PERSON] et al. investigated the impact of the guard time in TSCH [18]. The authors decreased the guard time duration when motes were closer to their sink and concluded that this results in significant savings in energy consumption without compromising network reliability. While these works only aim at specific TSCH elements, the proposed work provides an energy consumption model for the whole of the IEEE 802.15.4e TSCH mode. Other works such as [PERSON] et al. compared the performance of the TSCH and Deterministic and Synchronous Multichannel Extension (DSME) modes of 802.15.4e [19]. The authors do not propose a model themselves. They observed that TSCH mode tends to consume more energy than DSME mode. This is due to the large fixed guard time in TSCH and because DSME can aggregate multiple acknowledgments and transmit a single group of acknowledgments.  
Finally, [PERSON] et al. presented an energy model for TSCH networks, using the OpenMote and OpenWSN for their experimental validation [2]. The values from the model were compared to measurements on the GINA and OpenMote-STM32 platforms. Our paper continues the work of [PERSON], but explores several differences and improvements. As such, we propose a model with an extra time slot type (i.e., TxDataRxNoAck), provide an extended and a more up-to-date set of states per time slot and extend the model to support variable packet sizes. Furthermore, the OpenWSN firmware has been continuously updated, and the current software version has changed substantially since the version used by [PERSON] in 2013. Finally, by using the OpenMote-CC2538 and OpenUSB board, this paper focuses on state-of-the-art hardware. This allows us to consider the TSCH energy consumption in both the 868 MHz and 2.4 GHz band. To the best of our knowledge, we are the first to do this. We also explicitly look at the difference in power consumption between using a SoC and the case with a separate micro-controller and radio chip. All the steps in developing the model are explained in detail, allowing it to be used for different types of hardware by simply changing the measured consumption values.
# Socialism and the Blockchain
## 2 Bitcoin
### Bitcoin's Energy Use  
PoW is a scheme based on a SHA-256 hashing algorithm, which produces a cryptographic hash with a value less than a target nonce. The average CPU effort needed to compute the PoW for a mined block is exponential to the number of zeroes required. The BTC network examines timestamps and calculates the time that has elapsed creating 2106 blocks; ideally, 1,209,600 s will have passed, which equates to the creation of a single block every ten minutes [13]. However, the production of blocks is via a random Poisson process whereby it might be that many blocks, or very few blocks, are found in a given hour [32]. BTC raises the hashing difficulty if it discovers that the creation of 2016 blocks took less time than 1,209,600 s. Conversely, the difficulty is dropped if creation took longer [33]. Invariably, as more processing power comes online to the BTC network, the difficulty goes upwards. For example, at the time of writing, the BTC difficulty is 163,491,654,909. The very first BTC had a difficulty of 1. This means that it is nearly 164 billion times harder to mine a BTC now than it was to mine the very first.  
All of that computing power comes at an enormous energy cost. The authors of a BTC mining guide state that, based on price per hash and electrical efficiency, the ASIC miner _AntMiner S7_ is the best BTC mining hardware available [30]. This mines at 4.73 Terra hashes per second (Th/s) and consumes 1293 Watts [34]. At the time of writing, the network hash rate of BTC was 1,411,498 Th/s [35]. Assuming that the _AntMiner S7_ does indeed represent the best in class, we can use those figures to calculate the best case scenario for the total energy consumption of mining on the BTC network:  
\[(1,411,498/4.73)\times 1293\approx 385.84\text{ MegaWatt hours (MWh).}\]  
Therefore, at that rate of consumption, the daily energy use of BTC mining is given by:  
\[385.84\text{ MWh }\times 24\approx 9.26\text{ GWh.}\]  
Hence, BTC mining's approximate annual electricity consumption is given by:  
\[9.26\times 365\approx 3.38\text{ TWh.}\]  
To put that 3.38 TWh in context, according to the International Energy Agency, in 2014, the 2.72 million people of Jamaica consumed 3.03 TWh of electricity, or slightly less than the annual consumption of mining on the BTC network [36].  
Figure 2: BTC validation process [31].
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 7 Future Directions  
Our team's future work will consider using an online detection tool for the railway coach frame acceleration and signalization of the peak values using the train intercom to the driver and static diagnosis systems.  
The data recorded with accelerometers can be analyzed using machine learning methods to create a rapid and precise online diagnosis system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Output** & **Range** & **Frequency** & **Supply** \\ \hline Colybris VS9005, Safran, Mere, France & \(\pm\)5 g & DC \(\div\)1100 Hz & 5.5 Vdc \(<\) 0.5 mA \\ Colybris MS1005, Safran, Mere, France & Differential \(\pm\) 2.7 V @full scale & \(\pm\)5 g & DC \(\div\)1400 Hz & 3.3 V \\ Althen ASC5521 MF, Althen, & \(\pm\)5 g & DC \(\div\)1150 Hz & 5\(-\)40 Vdc \(<\) 15 mA \\ Rijswijk, Netherlands & & & & \\ ECO-Rail-3325, ASC Gmbh, & \(\pm\)4 g & DC \(\div\)1500 Hz & 24 Vdc \(<\) 35 mA \\ Pfatherhof, Germany & \(\pm\)4 g & DC \(\div\)1500 Hz & 24 Vdc \(<\) 35 mA \\ Triaxial 356A19, PCB Piezotronics, & Analog @10 mV/1 g & max \(\pm\)500 g & max 15 kHz & 18\(-\)30 V 20 mA \\ Depew, NY, USA & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected accelerometers dedicated to railway applications.  
**Author Contributions:** Conceptualization, E.T. and I.V.; methodology, I.V.; formal analysis, G.P.; investigation, I.V.; resources, D.L, C.D., and N.T.; writing--original draft preparation, I.V.; writing--review and editing, I.V. and E.T.; visualization, C.D. and D.L.; supervision, G.P. and F.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by a grant from the Romanian Ministry of Research Innovation and Digitization, CCCDI--UEFISCDI, project number 22 PTE/2025, within PNCDI IV.  
**Data Availability Statement:** No new data were created.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Tehmin-Brasov. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods
### Measurement  
Measurement in the present study included SA, task performance and mental workload. SA was assessed with two commonly used methods [51; 52; 53; 54; 55], including the three-dimensional situation awareness rating technique (3D-SART) [56] and the situation present assessment technique (SPAM) [57]. 3D-SART is convenient to employ with good measurement validity [58], while SPAM is less invasive in high-fidelity task situations [59; 60]. 3D-SART scale has three dimensions, including attention resource demand, attention resource supply and situation understanding. The 3D-SART score, calculated as the sum of the latter two dimensions, deducting the former one, was used to represent the SA levels. Moreover, we used the value of attention resource supply minus demand to assess the participants' attention resource surplus (ARS). The SPAM method was implemented with queries focused on the displayed range of parameters for altitude, speed, course, pitch, roll, thrust and flap, so that the participants' awareness on the current and future flight attitudes could be evaluated. SPAM response accuracy (RA) was recorded as the proportion of the participants' correct answers in all queries, and the SPAM response time (RT) was recorded as the average time interval from the queries' release to the time the participants answered the queries.  
Task performance was assessed by operation accuracy, which was measured as the proportion of times that the participants successfully achieved the requirements in compliance with the plan or instruction. Mental workload was assessed with the National Aeronautics and Space Administration--Task Load Index (NASA-TLX), which consists of six dimensions, including mental demand, physical demand, temporal demand, performance, effort, and frustration [26]. A weighted sum of the six dimensions was computed as the mental workload. Furthermore, visual behavior of the participants was also assessed for analyzing the information perception and attention allocation, which have been verified associated with SA and mental workload. Two eye movement measures of the fixation rate and average pupil diameter were adopted [61; 62; 63]. Fixation rate was the ratio of fixation counts in an AoI to the total fixation counts, and the average pupil diameter was calculated as the average value of the pupil diameters of the participant's left and right eyes.  
Figure 2: Experimental independent variables of air route alternation (**left**) and display design (**right**) with areas of interests (AoIs).
# Dynamic Frequency Support from a DFIG-Based Wind Turbine Generator via Virtual Inertia Control
## 2 Frequency Control of an Electric Power System  
Keeping the frequency in the reasonable range is essential for reliable operation of power systems, as the frequency reflects the relationship between the power generation and the load demand. When a frequency event happens, the power system strives to work against the system frequency variation via the inertia response as well as the primary and the secondary frequency controls of synchronous fleets [21]. In other words, conventional synchronous fleets intrinsically release their kinetic energy with a purpose of compensating for the power deficit. Conventional synchronous generators increase input mechanical power to stabilize frequency and arrest the frequency decrease according to the measured rotor speed deviation; afterward, the fast-starting conventional synchronous generators, such as diesel and open-cycle generators, initiate to restore the frequency to 50 Hz or 60 Hz. The relation of the inertia response and the primary frequency control can be given as:  
\[\Delta P_{in}=2H_{sys}\times f_{sys}\times\frac{df_{sys}}{dt}, \tag{1}\]  
\[\Delta P_{pf}=-\frac{1}{R}\times(f_{sys}-f_{nom}), \tag{2}\]  
where \(f_{sys},f_{nom}\), \(H_{sys}\), and \(R\) respectively are system frequency, nominal frequency, system inertia, and droop gain of a power system. \(\Delta P_{in}\) and \(\Delta P_{pf}\) indicate the additional output power of the inertia response and the primary frequency control, respectively.  
As aforementioned, DFIG-based WTGs decouple the rotor speed from the system frequency. As a result, the frequency deviation and the maximum _df/dt_ become severe and may even trigger the under frequency load shedding relays to prevent the frequency decline further.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 5 Handover Challenges in Drone Networks
### Radio Link Failures  
The RLF is an alternative challenge that may increase with the implementation of drones as compared to terrestrial UEs. Both drone characterizations and inefficient handover techniques are factors that contribute to the increase in RLF. Handover optimization algorithms and handover decision techniques play a key role in controlling the occurrence of RLFs in mobile networks. These factors must be collectively considered. Several optimization algorithms have been proposed for robust distributed movement to mitigate the RLFs and HOFs by altering the offset parameters, such as Handover Margin (HOM) and Time-To-Trigger (TTT). However, the issue remains a challenge, especially with 6G mobile networks that will be characterized with high requirements and specifications.
# Time-Dependent Probabilistic Model for Hierarchical Structure in Failure Mode and Effect Analysis
## 3 Hierarchical Time-Dependent FMEA Model
### Detection of the Failure-Cause (FC)  
As the loss function is separated by detection before and after the SF, we need to consider the separation of the detection function before and after the SF. It is not easy to recognize the FC until the system stops operating due to the \(0^{\text{th}}\) level failure. Therefore, the probability that the detection occurs before the SF time \(t_{0}\) is uniform with the inspection schedule. However, if the \(0^{\text{th}}\) level failure occurs and the system stops operating, this method will focus on finding the FC regardless of the detecting schedule. Therefore, the detection probability of the FC will jump up right after the SF and decrease after jumping. Assuming that detection is based on the memory-less property, we can easily estimate that the probability of detection after an SF is exponential.  
Figure 3 expresses the probability density function of the FC detection in a case function. Let \(w(t_{0})\) be the probability of uniform distribution, with \(s<t_{0}\), and let \(1-w(t_{0})\) be that of the exponential distribution. That is, \(P(S<t_{0})=w(t_{0})\) and \(P(S\geq t_{0})=1-w(t_{0})\). Then, the probability density function, \(g_{S|T_{0}=t_{0}}(s)\), becomes a case function such that  
\[g_{S|T_{0}=t_{0}}(s)=\left\{\begin{array}{ll}w(t_{0})\frac{1}{t_{0}},&s<t_{0} \\ [1-w(t_{0})]\mu e^{-\mu(s-t_{0})},&s\geq t_{0}\end{array}\right.. \tag{4}\]  
Suppose the detection probability is \(c\) times jumping up at the time \(s=t_{0}\) of the SF. Thus,  
\[cw(t_{0})\frac{1}{t_{0}}=[1-w(t_{0})]\mu e^{-(t_{0}-t_{0})}=[1-w(t_{0})]\mu. \tag{5}\]  
Hence,  
\[w(t_{0})=\frac{\mu t_{0}}{c+\mu t_{0}}. \tag{6}\]  
The weight function equals zero where \(t_{0}\) is zero and becomes one as \(t_{0}\) approaches infinity. This is very reasonable because the probability of detection before the failure is zero where \(t_{0}\) is zero, and that of detection before the failure becomes one as \(t_{0}\) approaches infinity. Therefore,  
\[g_{S|T_{0}=t_{0}}(s)=\left\{\begin{array}{ll}\frac{\mu}{c+\mu t_{0}},&s<t_{0 }\\ \frac{1}{c+\mu t_{0}}e^{-\mu(s-t_{0})},&s\geq t_{0}\end{array}\right.. \tag{7}\]  
Figure 3: Probability density function of FC detection, \(g_{S|T_{0}=t_{0}}(s)\), conditioned by the SF time \(T_{0}=t_{0}\).