esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Satellite communications are pivotal to global Internet access, connectivity, and the advancement of information warfare. Despite these importance, the open nature of satellite channels makes them vulnerable to eavesdropping, making the enhancement of interception resistance in satellite communications a critical issue in both academic and industrial circles. Within the realm of satellite communications, polarization modulation and quadrature techniques are essential for information transmission and interference suppression. To boost electromagnetic countermeasures in complex battlefield scenarios, this paper integrates multi-parameter weighted-type fractional Fourier transform (MP-WFRFT) with directional modulation (DM) algorithms, building upon polarization techniques. Initially, the operational mechanisms of the polarization-amplitude-phase modulation (PAPM), MP-WFRFT, and DM algorithms are elucidated. Secondly, it introduces a novel variable polarization-amplitude-phase modulation (VPAPM) scheme that integrates variable polarization with amplitude-phase modulation. Subsequently, leveraging the VPAPM modulation scheme, an exploration of the anti-interception capabilities of MP-WFRFT through parameter adjustment is presented. Rooted in an in-depth analysis of simulation data, the anti-scanning capabilities of MP-WFRFT are assessed in terms of scale vectors in the horizontal and vertical direction. Finally, exploiting the potential of the robust anti-scanning capabilities of MP-WFRFT and the directional property of antenna arrays in DM, the paper proposes a secure transmission technique employing directional variable polarization modulation with MP-WFRFT. The performance simulation analysis demonstrates that the integration of MP-WFRFT and DM significantly outperforms individual secure transmission methods, improving anti-interception performance by at least an order of magnitude at signal-to-noise ratios above 10 dB. Consequently, this approach exhibits considerable potential and engineering significance for its application within satellite communication systems.  
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## 3 Results
### Evaluation Using Independent Datasets
#### 3.2.2 Soil Moisture  
The statistical scores for surface soil moisture from the model and the analysis (third layer of soil between 4 and 10 cm depth) over 2010-2016 when compared with ground measurements from the USCRN network (at 5 cm depth) are presented in Table 3. Median R values on volumetric soil moisture time-series (anomaly time series), along with their 95% confidence interval of the median, derived from 10,000-sample bootstrapping are as follows: 0.72 \(\pm\) 0.02 (0.60 \(\pm\) 0.02) and 0.74 \(\pm\) 0.02 (0.60 \(\pm\) 0.02), while median ubRMSDs are 0.049 \(\pm\) 0.004 and 0.048 \(\pm\) 0.004 for the model and the analysis, respectively. Figure 7a,b illustrate correlation values on volumetric and anomaly time-series between the model and the observations, respectively, for each station. Figure 7c,d represent the added value of the analysis expressed through the NIC index (Equation (1)) applied for correlations (NIC\({}_{\text{R}}\)) values on volumetric and anomaly time-series; large blue circles represent a positive impact from the analysis at NIC\({}_{\text{R}}\) greater that +3 (i.e., R values are better when the analysis is used than when the model is used), large red circles a degradation from the analysis at NIC\({}_{\text{R}}\) smaller than \(-3\), while diamond symbols represent a rather neutral impact at NIC\({}_{\text{R}}\) in between \([-3;+3]\). While 46% (81%) of the pool of stations present a rather neutral impact for R values on volumetric (anomaly) time series, stations more impacted by the analysis tend to be positively impacted at 46% (18%), to be compared with 8% (1%) of negative impacts. Although differences between the model run and the analysis are rather small, these results underline the added value of the analysis with respect to the model run.  
Figure 7: Maps of correlation (R) on volumetric time-series (**a**) and anomaly time-series (**b**) between in situ measurements at 5 cm depth from the US Climate Reference Network (USCRN) network and soil moisture from the model (third layer of soil between 4 cm and 10 cm) from 2010 to 2016. NIC applied on R (anomaly R) values (**c,d**); analysis with respect to the model. NIC scores are classified according to three categories: (i) negative impact from the analysis with respect to the model with values smaller than \(-3\)% (red circles), (ii) positive impact from the analysis with respect to the model with values greater than +3% (blue circles), and (iii) neutral impact from the analysis with respect to the model with values between \(-3\)% and \(3\)% (diamonds).
## 4 Discussion
### Limitations of the Proposed Method  
There are still some limitations in the proposed F-SFM algorithm.  
Firstly, the spectral characteristics of the reflectance and SIF data are extracted from the simulated training dataset. This means that the full-spectrum accuracy of the retrieved SIF spectra depends on how  
Figure 11: Comparison between retrieved ratios of SIF peak-values (SIF\({}_{735}\)/SIF\({}_{685}\)) for data measured by QE Pro and ASD FieldSpec4 spectrometers.  
representative the training dataset is. In addition, it is very difficult to validate the full-spectrum SIF retrieval directly by field-measured data because no direct measurement method is available.  
Secondly, only the SIF-infilling information at three quite narrow absorption bands can be used for SIF retrieval due to the limited spectral resolution. For spectral data with a higher resolution, it should be possible to detect more Fraunhofer lines (such as the Fe line at 757.8 nm and K I line at 770.1 nm) and, consequently, the algorithm will be more stable. However, a higher SR will normally lead to a lower SNR and so it is necessary to find an optimal balance between these two key parameters of the spectrometer.  
Thirdly, the values of the atmospheric parameters used in this study were fixed, which means that the effect of varying atmospheric conditions were not included. In addition, the SRs and SNRs of the two field spectrometers used for the algorithm testing are quite high--this would be difficult to achieve for space-borne or airborne spectrometers.  
In future work, optimization of the training dataset and the balance between the SR and SNR should be investigated. The F-SFM algorithm also needs to be further developed so that it can be used for space-based SIF retrieval.
## 7 Safety Analysis
It means that under the condition that \((Gen,Sig,Ver)\) is a secure digital signature scheme and \(h\) is a non-collision hash function, the message hash chain verification scheme \((MHC,Gen,Sig,Ver)\) is secure. Therefore, the authenticity and non-repudiation of the data flow between two digital signature intervals can be ensured by using the message hash chain verification. Through the chain signature and authentication of a message, all messages in the previous sequence of this message can be verified  
Under the same conditions as Theorem 1, the receiver verifies the digital signature of a message \(p_{\alpha}\) in the data stream. If \(\forall\delta\), \(0<\delta<\alpha\), then \(p_{\delta}\) can judge its own authenticity and non-repudiation according to the correctness of \(p_{\alpha}\)'s digital signature. There is a sequence \(p_{1},p_{2},\ldots,p_{k}\), the sender will sign the \(p_{k}\), and the receiver will verify the signature. Suppose there is an attacker who can use algorithm \(ATTCK\) to forge the node value of the message hash chain. This means that for the message hash chain sequences \(HC=HC_{1},HC_{2},\ldots,HC_{k}\) and \(HC^{\prime}=HC^{\prime}_{1},HC^{\prime}_{2},\ldots,HC^{\prime}_{k}\) constructed by \(m_{1},m_{2},\ldots,m_{k}\), the algorithm \(ATTCK\) can output the forged message hash chain node sequence \(HC^{*}=HC^{*}_{1},HC^{*}_{2},\ldots,HC^{*}_{k}\) according to \(m_{1},m_{2},\ldots,m_{k}\), and make \(HC^{*}=HC\). In the absence of an attacker, when the receiver receives the \(p_{k}=(m_{k},HC_{k},s_{k})\), the verification of the signature must satisfy \(Ver(dk,s_{k})=1\). If algorithm \(ATTCK\) can output \(s^{*}_{k}=Sig(ek_{ATTCK},HC^{*}_{k})\) to satisfy \(Ver(dk,s^{*}_{k})=1\), then it means that algorithm \(ATTCK\) can forge scheme \((Gen,Sig,Ver)\), but this obviously contradicts the assumption. This shows that if \(p_{k}\) can be verified by digital signature, then \(p_{1},p_{2},\ldots,p_{k-1}\) also has authenticity and non-repudiation; otherwise, \(p_{1},p_{2},\ldots,p_{k-1}\) do not have authenticity and non-repudiation. The message hash chain can ensure the integrity and immutability of the data flow. A message \(m_{i}=(hdr_{i},\ payload_{i})\) and its corresponding node value of message hash chain \(HC_{i}\) are jointly encapsulated into a message hash chain message \(p_{i}=(m_{i},seq_{i},HC_{i})\), where \(hdr_{i}\) includes the source address \(src\), destination address \(dst\) and other contents of the message header \(other\). Obviously, the equation \(HC_{i}=h(h(m_{i})||HC_{i-1})=h(h(hdr_{i}||payload_{i})||HC_{i-1})\) can be obtained from the Formula (1). If any content of the message hash chain message is tampered with by an attacker, and the tampered values are \(hdr^{*}_{i},payload^{*}_{i}\) and \(HC^{*}_{i}\), respectively, then \(h(h(m^{*}_{i})||HC^{\prime}_{i-1})\
eq HC^{*}_{i}\) must occur when the receiver verifies it.
# Optical Technology for NFV Converged Networks
## 2 Reference Network Scenario and Main Requirements  
The transport network requirements depend on both the E2E services to support, such as IoT, and the type of radio architecture. In a radio split architecture, the different layers of the protocols stack are allocated in different nodes, instead of being all in the same base station. Figure 1 reports a reference scenario for an access network able to support mobile transport features with alternative radio splits, concurrently supported. As shown in Figure 1, common topologies in this access area are Passive Optical Networks (PON) and ring optical networks.  
In this scenario, the antenna sites are connected to the Central Office (CO) using different radio interfaces, each one corresponding to a different protocol split: CPRI [3], which is the most critical in terms bandwidth and latency requirements; eCPRI [4], which requires less bandwidth but has similar latency requirements of CPRI; and S1 [5] and F1 [6] that have less demanding bandwidth and latency specifications. The CO hosts both VNFs and PNFs (Physical Network Functions). For the sake of simplicity, in Figure 1, the radio interfaces are shown in the ring topology case, but the same traffic applies to the PON.  
An efficient transport infrastructure should be able to accommodate different services, as well as different radio split options. This concept is usually referred as Xhaul [7]. In [7], alternative Xhaul implementations are reported, including a case where PON physical infrastructure is used, but not its Time Division Multiplexing Access (TDMA) protocol. Wavelength Division Multiplexing (WDM) has several advantages for the implementation of an Xhaul infrastructure: it is transparent to the different radio interfaces, it maximizes the transported capacity over optical fiber and has low latency [7]. However, a key aspect for enabling the path towards NFV is to provide a suitable level of automation. Automation in PON and rings presents peculiar requirements that will be better detailed in the next sections.
# An Overview of Lidar Imaging Systems for Autonomous Vehicles
## 2 Basics of Lidar Imaging
### Imaging Strategies
#### 2.2.1 Scanners
Further, the optical arrangement for rotating lidar units may be simple and extremely efficient in order to collect faint diffuse light and thus achieve very long ranges as there is some margin in the size of the collection optics, especially when compared to other approaches. Lidars with mechanical scanners work almost always with pulsed sources and are usually significantly large and bulky. In the case of rotating mirrors, they can achieve large spatial resolution in the direction of turn (usually horizontal) although they become limited in the orthogonal direction (usually vertical) where the density of the point cloud is limited by the number of available sources and detectors measuring in parallel. Further, they need rather high power consumption and, due to the large inertia of the rotating module, the frame rate is limited (frame rate goes from below 1 Hz to about 100 Hz). They are, however, very efficient in long-range applications (polygon mirrors combined with coaxial optical systems easily reach distances well beyond 1 km). Despite the current prevalence of these type of scanners, the setup presents numerous disadvantages in a final consumer unit, in particular the question of reliability and maintenance of the mechanisms, the mass and inertia of the scanning unit which limits the scanning speed, the lack of flexibility of the scanning patterns, and the issue of being misalignment-prone under shock and vibration, beyond being power-hungry, hardly scalable, bulky and expensive. Although several improvements are being introduced in these systems [43], there is a quite general agreement that mechanically scanning lidars need to move towards a solid-state version. However, they are currently providing one of the performances closest to the one desired for the final long-range lidar unit, and they are commercially available from different  
Figure 6: Schematics of a typical light detection and ranging (lidar) imaging system based on mechanical scanning. vendors using different principles. This makes them the sensor of choice for autonomous vehicle research and development, such as algorithm training [54], autonomous cars [55], or robotaxis [56]. Their success also has brought a number of improvements in geometry, size and spatial resolution to make them more competitive as a final lidar imaging system in automotive.
## 4 Results
### Operations and Approaches
#### 4.2.1 Event-Driven Model Predictive Control (MPC) Approach  
The event-driven MPC approach is based on a snapshot of the EV fleet at a specific time. Each EV within this snapshot is characterized by three primary variables: (i) current state: the current charge level of the EV's battery; (ii) desired final state: the target charge level set by the driver upon arrival at the charging station; (iii) departure time: the intended departure time specified by the driver.  
Assuming no additional EVs join the fleet, the open-loop optimal control problem is solved to determine the optimal charging strategy for each EV. This strategy aims to (i) satisfy driver preferences: fulfill the driver's desired charge level and departure time, if feasible; (ii) minimize costs: reduce overall charging costs; (iii) minimize tracking error: ensure the EV's actual charging trajectory closely follows the optimal plan.  
The key inputs to this optimization problem include (i) the EV models: the specific characteristics and capabilities of each EV, such as battery capacity and charging rate; (ii) electricity tariff: the current pricing structure for electricity, including time-of-use rates or other relevant factors; (iii) power reference and threshold: limits or constraints imposed on the overall power consumption by the EV fleet.  
Although the initial solution is open loop, its optimality can degrade due to events that alter the initial conditions (e.g., new EVs arriving, changes in tariffs, power references, or DSM events). To address this, an iterative optimization process is employed, like MPC. Whenever a \"relevant\" event occurs, the optimization is repeated, and the newly calculated control sequence replaces the unimplemented portion of the previous plan.  
Note on DSO Communication: while DSO communication can occur before and after each iteration for grid constraint retrieval and aggregated power notification, it may not be necessary for every iteration, especially when the power reference is market-driven and known beforehand. However, in specific situations where additional grid constraints or technical validation can enhance grid efficiency, this interaction might be considered.
## 4 Discussion
### Benefits from Using the Google Earth Engine Clouding Computing Platform  
Since its emergence, Google Earth Engine has gradually become an important tool for Earth system science, allowing researchers to take advantage of massive datasets, powerful computing capabilities, a convenient and fast interface, and convenient data sharing and publishing [63; 64; 69]. Google Earth Engine stores all available Sentinel-1 SAR images and provides a complete set of image preprocessing functions that can quickly produce preprocessed Sentinel-1 SAR images, greatly reducing the time and tedious steps required for image downloads, preprocessing, and storage. It also stores the dataset of JRC Yearly Water Classification History v1.2 [65], which provided a complete auxiliary dataset for the rice extraction method in our study. Furthermore, the Google Earth Engine platform provides a high-performance parallel computing infrastructure to meet the powerful processing requirements of massive data over large areas in multitemporal applications. Thus, the algorithm codes were written with JavaScript in the Google Earth Engine online Code Editor, and all procedures in our rice paddy classification could be operated on this platform. The time-series Sentinel-1 SAR images quickly generated in Google Earth Engine can be combined with fine phenological characteristics to identify rice information with periodic rules. This study proved that a large amount of 402 Sentinel-1 SAR images could be used for rapid extraction of rice paddies in a large area.
# A Comprehensive Review on Music Transcription
## 3 Transcription Techniques: Insights and Advances
### Single-Instrument Transcription  
Single-instrument transcription is the process of converting an audio recording containing a single musical instrument into a symbolic representation, such as musical notes or sheet music. Most of the previous automatic music transcription focused on single-instrument piano transcription [25; 62; 63; 64; 65]. The proposed methods in [25; 62; 63; 65] use deep learning-based approaches for piano transcription. Similarly, the proposed method in [64] uses non-negative matrix factorization by modeling a piano note as a percussive and harmonic decay in a supervised way. The work in [25] is designed only for frame-level transcription, while the work in [62] jointly estimates the onset events and pitch. Similarly, the method proposed in [63] can estimate the onset and offset times with an arbitrary time resolution, which outperforms the accuracy in the MAESTRO dataset [66]. Similarly, the work in [65] automatically predicts the piano note onset events given a video of a person playing a piano. The works described above are all for piano transcription. However, there are some other single-instrument transcriptions like guitar [67; 68], violin [69; 70], and flute transcriptions [71; 72]. The work in [67] is the conventional approach which iteratively processes frequency peaks by marking fundamental frequency, while [68] is the recently published paper based on an attention mechanism and convolutional neural network for guitar transcription. Similarly, the research works in [69; 70] discuss the analysis of violin music, but they have different objectives and methods. Additionally, [69] focuses on transcribing the pitch, duration, and playing techniques based on the time and frequency domain properties of the audio signal. In contrast, [70] aims to recover fingerings from violin music to recreate timbre, using the hidden Markov model. Moreover, the work in [71; 72] is related to the transcription of Arabian flute music using audio features like Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT). The work in [71] is designed to detect octave falls and to perform the mapping from intensity to MIDI velocity, while [72] detected the Arabian flute pitches, which improved the accuracy over the occidental flute.
## 4 Results  
Clear-air echoes are frequently and typically detected from sunset, when they first appear on the radar display, until sunrise, when they gradually become less distinct. Usually, clear-air echoes are seen as shown in Figure 5. Although the reflectivity factor in Figure 5 reaches 20 dBZ, the correlation coefficient and differential reflectivity indicate non-precipitation. Hundreds of automatic weather stations owned by the Beijing Meteorological Observation Center also did not observe precipitation at this time. To prevent misinterpretation of the weather echo as a clear-air echo, which could result in unreliable outcomes, we utilized the total precipitation data from the ERA5 reanalysis product and the 11.2 \(\upmu\)m brightness temperature from the Himawari-8 satellite to substantiate the case as a clear-air echo. Figure 6 illustrates that there were no thick clouds and no precipitation at that time. The evidence presented collectively indicates the existence of clear-air echoes in Figure 5.  
In the theory of Bragg scattering, as in Equation (4), a 10 to 20 dBZ reflectivity factor needs \(C_{n}^{2}\) to be greater than \(10^{-11}\)m\({}^{-2/3}\). However, this is far above the observed \(C_{n}^{2}\). Prior to the introduction of troposcatter, existing theories could not sufficiently explain clear-air echoes. Some researchers believe that bio-scatterers are the main cause of clear-air echoes, but there are increasing irrationalities in daily monitoring and ecological studies. Therefore, it is necessary to expose the irrationality of the original viewpoint and then to establish a correct understanding of clear-air echoes.  
Figure 4: Histogram of dual-polarization products with a reflectivity factor greater than 20 dBZ from the CINRAD/SA radar on 15 May 2021 at 14:30 UTC for an elevation angle of 1.45\({}^{\circ}\). The histograms (**a–d**) represent the probability distributions of different values of radar products during precipitation, respectively.