esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Caching: Making Radio Access Networks More than Bit-Pipelines
## 2 Proactive Service: Gains, Costs, and Needs
### Caching Gains: A Time-Domain Perspective
In this way, we increase the transmission time while assuring that a user experiences small delay, as shown in Figure 1. In this case, the methods in Table 1 can be exploited to increase the EE or SE. Though their data rate is low or unstable, caching allows a user to experience real-time services virtually. Caching is expected to benefit the next generation RAN, e.g., 6G, in many aspects. A natural question to ask is which layer caching belongs to. If it only makes binary decisions on \"to cache or not to cache\" in each node, it is more like a network-layer protocol. In other words, a binary decision on \"to cache or not to cache\" is insufficient. By contrast, a more precise decision on \"when and how to cache\" is desired, which requires caching to function with the physical and link layers. In particular, since the data rate of any wireless link is bounded, neither content placement nor delivery can be accomplished immediately. The content placement and delivery tasks must be carefully scheduled in the time domain. For content placement or delivery over time-varying channels, radio resource allocation should adapt to instantaneous channel states, while meeting the deadlines. Furthermore, as the cache-enabled multicasting or interference cancellation requires concurrent transmission, asynchronous transmissions should be aligned in the time domain. In summary, separate caching from the physical and link layers is not possible. Instead of waiting for a user's command, a RAN itself not only makes binary decisions on whether \"to cache or not to cache\", but also determines \"when and how to cache\". Thus, it requires novel functions beyond a bit-pipeline. In [15, 16, 17], threshold-based policies avoid pushing unddemanded files that waste energy. When the demand probability for a content is below a certain threshold, its caching gain fails in compensating for the waste of radio resources. In addition, for popular files, caching too early results in the reduction of the available time for content pushing, resulting in the loss of SE/EE gains. Caching too late, however, may miss the request. To make caching practical, careful scheduling is desired in both placement and delivery phases. The time-domain framework in Figure 1 implies unified resource allocation that optimizes caching gains subject to the latency requirement. The framework can be applied with an arbitrary physical-layer scheme given its instantaneous rate-cost function. Moreover, by tracing the common root of caching gains, we may foresee how emerging wireless techniques with potentially large delay can be enabled by caching.
# Planetary-Gearbox Fault Classification by Convolutional Neural Network and Recurrence Plot
## 3 Briefs on Convolutional Neural Network  
### Typical Configuration of Convolutional Neural Network  
A CNN generally contains five layers: the convolutional, activation, pooling, fully connected, and softmax layers. Convolutional layers are composed of several convolutional kernels that abstract and adaptively learn features from the input to obtain feature maps. The first feature map is computed by convolving the input images with the first convolutional kernel, and the next feature map is obtained from the previous output through multiplying the shared weight and adding the bias. Then, the nonlinear activation function is applied on the above-mentioned convolutional results, which is defined by sigmoid _tanh_ or a rectified-linear-unit (ReLU) function [19]. The ReLU is widely applied after the output of every convolutional and fully connected layer.  
In the classification stage of the CNN model, there is generally a fully connected layer and classifier that mainly integrate and classify information from the previous layer. A softmax regression classifier is always placed at the last layer to estimate the probability of multiple faults. Cross-entropy is generally used as the loss function to represent the error between the true and estimated value. Weight matrix and bias term are optimized by the adaptive-moment-estimation method (ADAM). ADAM is able to dynamically adjust the learning rate of each parameter through first- (average) and second-order (variance) moment estimation [20]. Thus, it is appropriate for nonstationary objectives and engineering vibration data with a low SNR. Moreover, in applications, this method can efficiently compute and needs little memory, which is suitable for processing long data.
## 1 Introduction
### Figures of Merit  
[PERSON] [17] defined a dimensionless figure of merit that was referred to as 'effectiveness' to compare between different designs of vibration energy harvesters. The transmission coefficient \(\lambda\) was used as another definition of the efficiency \(\eta\). The transmission coefficient \(\lambda\) was defined as the output energy per cycle divided by the input energy per cycle. The maximum transmission coefficient \(\lambda_{\text{max}}\), at which maximum output energy was obtained, was found to be dependent only on the electromechanical coupling coefficient. The effectiveness figure of merit was dependent on the quality factor, the system coupling coefficient, the ratio of the actual density of the design to the baseline density, and the transmission coefficient ratio (\(\lambda/\lambda_{\text{max}}\)). [PERSON] and [PERSON] [18] suggested the square power of the bandwidth multiplied by the maximum displacement under a given acceleration to appraise between vibration energy harvesters. The normalized power density (NPD) was suggested by [PERSON] et al. [19]. The NPD was defined as the power output normalized to the product of the second power of the input acceleration and the volume of the transducer. [PERSON] et al. [20] mentioned that the power normalized mass acceleration (PNMA) was not proposed specifically in the literature; nevertheless, it was often used for appraising an energy harvester. The PNMA was based on the linear harvester's property that the power is proportional to the product of the mass and the second power of the ambient acceleration. [PERSON] et al. [21] proposed a figure of merit (FoM) that was very close to the NPD but divided by the quality factor. They estimated the volume of the energy harvester, taking into account the volume of motion. The volume figure of merit (FoMv) was suggested by [PERSON] et al. [22]. It was based on the power output, the volume, the input acceleration, and the excitation frequency. The density of gold was taken as a reference. To include the bandwidth, they suggested another figure of merit (FoMBW), which is simply the FoMv multiplied by the fractional bandwidth (the ratio between the half-power bandwidth and the excitation frequency). A recent systematic figure of merit with bandwidth information (SFoMBW) for evaluating wideband vibration energy harvesters was introduced by [PERSON] et al. [20]. The SFoMBW included the effectiveness, the normalized bandwidth (bandwidth/central frequency of the bandwidth), the mechanical quality factor, and the effective density (proof mass/entire volume). The effectiveness was defined as the ability to reach the maximum available power. Table 2 displays the formulas of the reviewed figures of merit. The symbols of the variables are edited from their original sources in order to be uniform for all the expressions.
###### Abstract  
Responses of two types of single-crystal diamonds, prepared by chemical vapour deposition (CVD) and high pressure high temperature synthesis (HPHT) methods, respectively, to a nanosecond pulsed neodymium-doped yttrium aluminium garnet (Nd:YAG) laser were investigated and compared. It was found that due to the difference in the transmission rate and refractive index, the laser-induced surface/subsurface features of the two types of samples were distinctly different. For the CVD sample, destructive interference takes place on the upper surface, leading to direct ablation of smooth grooves with deposition of graphite. For the HPHT sample, however, laser-induced grooves were formed on the reverse side of the irradiation surface (namely, the lower surface) at certain laser fluences due to the constructive interference phenomenon of the laser and the high refractive index of the material. The reverse-side irradiation resulted in the formation of deep and sharp grooves with rough bottoms and insignificant deposition of graphite on the area surrounding the groove. The machining thresholds for the upper and lower surfaces of both types of diamonds were experimentally obtained and theoretically verified. The findings of this study provide important process criteria for laser machining of different kinds of diamonds. The reverse-side irradiation method enables efficient machining of deep grooves in diamonds using a lower power laser.  
Keywords:single-crystal diamond; laser irradiation; micro machining; phase transformation; graphitization; crystal defect +
Footnote †: journal: Applied Physics Letters
# On the Use of a Feedback Tracking Architecture for Satellite Navigation Spoofing Detection
## 6 Results Using the Spoofing Detection Algorithm
### Detecting an Evolved Static Matched-Power Time Push Attack  
In this section, we present the results obtained processing scenario ds7, which, as explained in [40], is a static matched-power evolved time push attack, based on the clean static dataset, where a spoofer signal is injected after 110 s aligned in phase with the LOS signal. In the following 20 s, the spoofing signal doubles its amplitude and modifies its phase by a 180 degree rotation. In this way, the spoofer slowly takes control of the receiver without the need to synchronize to the navigation data bits [40]. The spoofing signal maintains its status for another 20 s, where it could perform a navigation data bit attack. At 150 s, the spoofer starts the push off phase, increasing the code phase linearly at a rate of 1.2 m per second and decreasing its amplitude until having a similar amplitude as the original LOS signal.  
In Figure 13, we observe how the ECADLL plus RM detector is able to detect the spoofing presence around 120 s. After that, it loses the lock and recovers it once the signals are being pulled apart by the spoofing attack.  
Figure 12: Phase estimation for Unit0 (**top**) and Unit1 (**bottom**) for TEXBAT ds3.  
In Figures 14 and 15, we observe the correct estimation of the delay difference \(\delta_{\tau}\) and the amplitude estimation that follows the description presented in [40], and we observe how the relative difference in amplitude decreases, as the spoofing signal pushes off the real signal and its modular amplitude is reduced.  
Figure 14: Delay difference estimation for satellite13, using TEXBAT ds7.  
Figure 13: Decision of the spoofing detection algorithm for TEXBAT ds7.
# Uncertainty Analysis of Spherical Joint Three-Dimensional Rotation Angle Measurement
## 3 Uncertainty Analysis of Spherical Joint Space Rotation Measurement System
### Uncertainty Caused by Network Model and Sensor
As shown in Figure 9, the coincidence degree of scatter and straight lines is relatively high. The deviation from the line of normal cumulative probability was in the range of 0.02. Therefore, it can be considered to obey a normal distribution. According to the Monte Carlo method, the probability density function is clearly known and the statistical calculation is carried out. Therefore, it is verified that the sensor and the neural network obey the normal distribution at specific points. The sensor \(S_{1}\) and the mean value and standard deviation (SD) of 9 sampled data are shown in Table 2. The data in Table 2 was the output of sampling of \(S_{1}\). Figure 8: Histogram of the sensor S1 normality test. Figure 9: (a) P–P diagram of sensor \(S_{1}\). (b) The deviation from the line of normal cumulative probability. The above data indicate that the actual mean value and variance differ greatly, so the process is not an ergodic process of each state. Given that the difference between the variances of all parties is less than 0.007 V, the variances can be considered consistent. Therefore, this random process can be judged as a general stationary process, and the evaluation parameter of an error sample can be used to replace the evaluation parameter of the population average. Table 2 determines whether the sample mean data can be used to replace the overall mean data.
# Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTM
## 2 Methodology
### Channel Attention Embedded in CNN
#### 2.3.1 SE Layer  
The structure of the SE Layer is shown in Figure 2. It primarily consists of three algorithmic modules: Squeeze, Excitation, and Scale.  
The squeeze algorithm module consists of a global average pooling layer. In order to solve the problem of channel dependency, the global average pooling method is used to compress the global spatial information into a channel descriptor. Global average pooling is applied to each channel, and the feature map containing global information in each channel is directly compressed into a feature vector. In this way, the generated channel-level statistics \(Z_{c}\) contain contextual information, alleviating the problem of channel dependency. The calculation formula of \(Z_{c}\) is as follows:\[Z_{c}=\frac{1}{\mathrm{H}\times W}\sum_{i=0}^{H-1}\sum_{j=0}^{W-1}x_{\mathrm{m},n,c },c=1,2,\ldots,n \tag{4}\]  
where \(Z_{c}\) denotes the output of the \(c\)-th channel, \(x_{\mathrm{m},n,c}\) signifies the element located in the \(m\)-th row and \(n\)-th column of the \(c\)-th channel, while \(H\) and \(W\) indicate the dimensions (rows and columns) of the \(c\)-th input channel, and \(c\) represents the total number of channels. In this paper, \(c=64\).  
The excitation algorithm module consists of two stacked linear layers to capture the channel dependencies fully. To handle the nonlinear relationships between channels, the ReLU function is applied after the first linear layer, and the Sigmoid function is applied after the second linear layer. After two linear layers, we obtain the weights assigned to each channel. After assigning channel weights to the input data, the focus of the model on important signals is significantly enhanced [25]. The formula is as follows:  
\[S=\sigma(W_{2}\delta(W_{1}Z_{c})) \tag{5}\]  
where \(W_{1}\) and \(W_{2}\) are the parameter matrices of the two linear layers, \(\delta\) represents the ReLU function, and \(\sigma\) represents the Sigmoid function.  
The scale algorithm module incorporates the weight produced by the Sigmoid function into the original feature, modifies its significance, and enhances network focus. The formula is as follows:  
\[\widetilde{X}_{\mathrm{n}}=X_{n}\otimes S \tag{6}\]  
where \(\otimes\) denotes the multiplication between elements and \(X_{n}\) is the original feature.
# Lamb Wave-Based FDM-PPM Method Data Transmission Scheme in Plate Structures
## 2 Lamb Wave Communication Method Based on FDM-PPM
### Design of FDM-PPM Signal Structure  
In the FDM-PPM communication system, a data set consists of one header packet and multiple data packets. For example, a structure containing one header packet and six data packets is illustrated in Figure 4. The header packet carries the identification information of the transmitting sensor in the sensor network, with a signal time width of \(T_{s}\). Moreover, the header packet not only marks the beginning of the group of data packets but also assists in locating the positions of the subsequent data packet signals. Its signal waveform is obtained through FDM modulation. In the FDM-PPM communication system, each data packet contains multiple FDM-PPM signals, which carry information regarding the path damage between the upper-level sensing nodes monitored with Lamb waves. The time width of each frame signal within the FDM-PPM is \(T_{f}\), while the total time width of the entire signal is \(T_{s}\).  
Figure 5 illustrates the designed signal encoding sets for the header packet and data packets. The signal for the header packet is obtained through the FDM method, featuring eight distinct signal waveforms, each capable of carrying three bits of information. In contrast, the signals for the data packets are obtained via FDM-PPM, comprising 32 different signal waveforms, each capable of carrying five bits of information.  
Figure 3: Schematic of Lamb wave-sensing network communication.
## 2 Signal Transmission System
### Receiving Circuit  
The receiving circuit is mainly used for amplifying, filtering and demodulating the signal output from the Mach-Zehnder EO sensor. As shown in Figure 4, our receiving circuit consists of a variable gain amplifier, a band-pass filter, a FPGA module and a battery, _etc._ In order to provide a signal with appropriate amplitude for the demodulation module based on FPGA, a variable gain voltage amplifier with functions of coarse adjustment and accurate adjustment was developed, in which the coarse adjustment (linear gain control) can be used for controlling gain artificially, while the accurate adjustment (index gain control) makes it possible to control the output signal voltage automatically according to the signal from FPGA through a Frequency to Voltage (F/V) converter circuit. Moreover, an active band-pass filter was developed for filtering noises. Finally, the receiving modulated signal is demodulated by the FPGA module, and thereby the original baseband signal can be achieved at the output interface of the receiving circuit.
###### Abstract  
Sugarcane croplands account for ~70% of global sugar production and ~60% of global ethanol production. Monitoring and predicting gross primary production (GPP) and transpiration (T) in these fields is crucial to improve crop yield estimation and management. While moderate-spatial-resolution (MSR, hundreds of meters) satellite images have been employed in several models to estimate GPP and T, the potential of high-spatial-resolution (HSR, tens of meters) imagery has been considered in only a few publications, and it is underexplored in sugarcane fields. Our study evaluated the efficacy of MSR and HSR satellite images in predicting daily GPP and T for sugarcane plantations at two sites equipped with eddy flux towers. Louisiana, USA (subtropical climate) and Sao Paulo, Brazil (tropical climate). We employed the Vegetation Photosynthesis Model (VPM) and Vegetation Transpiration Model (VTM) with C4 photosynthesis pathway, integrating vegetation index data derived from satellite images and on-ground weather data, to calculate daily GPP and T. The seasonal dynamics of vegetation indices from both MSR images (MODIS sensor, 500 m) and HSR images (Landsat, 30 m; Sentinel-2, 10 m) tracked well with the GPP seasonality from the EC flux towers. The enhanced vegetation index (EVI) from the HSR images had a stronger correlation with the tower-based GPP. Our findings underscored the potential of HSR imagery for estimating GPP and T in smaller sugarcane plantations.  
crop; photosynthesis; remote sensing; model; precision farming Article
## 5 Proposed Framework
### ZTA Design
#### 5.1.1 Continuous Authentication  
Every RIC entity undergoes continuous multi-factor behavioral authentication to ensure dynamic trust evaluation across the O-RAN environment. This mechanism proactively identifies anomalies and enforces isolation before they escalate into security breaches. The authentication system integrates the following layers:  
* **API Call Sequences**: Each xApp's API call patterns are monitored in real time. An LSTM-based anomaly detection model, trained using OpenRAN Gym logs [19], continuously predicts expected behavior. Deviations greater than 2\(\sigma\) from the learned baseline immediately trigger an alert for closer inspection or automatic quarantine.
* **Resource Fingerprinting**: The usage profiles of computational resources (GPU, CPU, DSP) are collected and compared against expected hardware attestation baselines. Sudden changes, such as unexpected DSP load spikes or unauthorized GPU utilization, are treated as strong indicators of compromise (IoCs).
* **Latency Constraints**: Inference latency is a critical indicator of operational integrity. Near-RT xApps exceeding a 10 ms threshold are assumed to have been tampered with, misconfigured, or experiencing performance degradation. Such xApps are flagged and immediately migrated to a quarantine zone for further evaluation.  
Authentication tokens for each RIC component are refreshed every 50 ms using elliptic curve cryptography (specifically ECDSA-P384). This cryptographic agility satisfies the stringent Near-Real-Time (Near-RT) requirements outlined by the O-RAN Alliance [5], ensuring minimal authentication overhead while maintaining robust protection against session hijacking and token replay attacks.  
By combining behavioral analytics, hardware-level validation, and real-time performance monitoring, this continuous authentication mechanism establishes a resilient trust framework adapted to the highly dynamic and distributed nature of O-RAN networks.
## 3 Proposed Method
### Datasets  
Two different datasets that provide subjective quality scores for two different viewing distances were used to evaluate our method.  
* CID:IQ (Colourlab Image Database: Image Quality) [52]: This dataset is one of the few publicly available datasets with subjective scores collected at different viewing distances. CID:IQ has 690 distorted images made from 23 original images with high-quality. Subjective scores were collected at two viewing distances (50 cm and 100 cm, which correspond respectively to 2.5 and 5 times the image height) for each distorted image. Distorted images were generated with six types of degradation at different five levels: JPEG2000 (JP2K), JPEG, Gaussian Blur (GB), Poisson noise (PN), \(\Delta E\) gamut mapping (DeltaE) and SGCK gamut mapping (SGCK). An original image and five distorted images are presented in Figure 4.
* VDID2014 (Viewing Distance-changed Image Database) [53]: This dataset has 160 distorted images made from 8 high-quality images. For each distorted image, subjective scores were collected at two different distances (4 and 6 times the image height). Distorted images were made using four types of degradation at five different levels: JPEG2000 (JP2K), JPEG, Gaussian Blur (GB) and White Noise (WN). An example of distorted images is shown in Figure 5.