esa-sceva/satcom-chunk-collection huggingface dataset:
# PPDC: A Privacy-Preserving Distinct Counting Scheme for Mobile Sensing
## 6 Conclusions  
Both privacy protection of users and the distinct counting problem on large datasets are essential issues in mobile sensing applications. In this paper, we propose a privacy-preserving distinct counting scheme, PPDC, to solve these two problems simultaneously. PPDC expands each bit of the hashing values of the users' original data, so that the FM sketch is enhanced for encryption to protect user privacy. We choose the bitwise XOR encryption algorithm as the encryption algorithm. According to the theoretical analysis, PPDC causes little damage to the accuracy of the FM sketch. Moreover, a set of experiments demonstrates that, with appropriate value of several parameters, PPDC achieves high counting accuracy and practical efficiency with scalability over large-scale datasets.  
For future work, we will aim to expend PPDC to support the screening of invalid users and wrong data, so as to make the aggregated conclusions more accurate and facilitate other subsequent analysis operations.  
Conceptualization, X.Y. and Y.L.; software, X.Y.; resources, M.X. and S.F.; funding acquisition, M.X. and S.F.; Writing--Original draft preparation, X.Y.; Writing--Review and editing, M.X., S.F. and Y.L.  
This work is supported in part by the National Natural Science Foundation of China (NSFC) under grants 61872372, 61379144, 61572026 and 61672195, and Open Foundation of State Key Laboratory of Cryptology (No: MMKFKT201617).  
The authors declare no conflict of interest.
# Enhancing Kazakh Sign Language Recognition with BiLSTM Using YOLO Keypoints and Optical Flow
## 5 Results and Discussion
### Results on Two-Class Dataset  
The first dataset consists of two gesture classes. The results (Table 1) indicate that incorporating optical flow with an optimal parameter (Bi-LSTMOF-10) greatly improves all performance metrics. Specifically, accuracy increases from 78.95% (without optical flow) to 89.47%. However, increasing the optical flow region size (Bi-LSTMOF-20 and Bi-LSTMOF-30) leads to a decline in generalization ability, as evidenced by a drop in accuracy and F1-score. Bi-LSTMOF-30, as the least effective option, achieves accuracy of (57.89%).
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 2 Charged Particle Irradiation
### Swift-Heavy Ion Irradiation
#### 2.1.4 Bismuth Ion Irradiation  
[PERSON] et al. [102] deposited 1-4-layer-ed MoS\({}_{2}\) on silicon substrates capped with SiO\({}_{2}\). The mechanically exfoliated MoS\({}_{2}\) nanosheets were irradiated by \({}^{209}\)Bi ions with energies of 0.45-1.23 GeV and fluences of \(1\times 10^{10}\)-\(3.6\times 10^{12}\) ions/cm\({}^{2}\) in a vacuum at room temperature under normal incidence. The hillock-like latent tracks were observed on the surface of irradiated MoS\({}_{2}\) few-layers and attributed  
Figure 6: (**a**) AFM images of Xe irradiated MoS\({}_{2}\) with varying thicknesses from bulk-like down to single layer (grazing angle \(\theta\leq 1^{\circ}\)) deposited on SiO\({}_{2}\) substrates and (**b**) STEM images of irradiated freestanding MoS\({}_{2}\) mono-layers [100] (grazing angle \(\theta\leq 0.2^{\circ}\)), showing middle (left panel) and end (right panel) of an incision created by one ion. Energy of Xe ions: 91 MeV. Fluence: 10–15 ions/\(\upmu\)m\({}^{2}\). The directions of the ion beam in (**a**) are marked by white arrows. The black stretches in (**b**) are parts of an incision cut into the MoS\({}_{2}\) mono-layer by the projectile. The round white spots in (**b**) are molybdenum clusters. Reprinted with permission from Reference [100]. 1L: mono-layer; 2L: bi-layer; 3L: tri-layer. Copyright © under the terms of the Creative Commons Attribution 3.0 license.  
to the ionization and excitation of energy transfer from \({}^{209}\)Bi ions to the electron system. The induced damages shifted the Raman A\({}_{1g}\) peak to a higher frequency and increased the intensity ratio between the A\({}_{1g}\) and E\({}_{2g}^{1}\) modes.
## 4 Experiments  
### Datasets  
Training dataset: We use the CUFED [3] dataset's training set to train our model. The training set contains 11,871 pairs of input and reference images, with each image having a resolution of 160 \(\times\) 160. It includes diverse scenes like people, buildings, and landscapes, helping the model learn rich features.  
Testing dataset: We evaluated the performance on several datasets, including the CUFED5 [3], Sun80 [34], Urban100 [35], Manga109 [36], and WR-SR [6] datasets. Specifically, CUFED5 is the test set of CUFED, with 126 input images and 5 reference images per input at different similarity levels. It helps evaluate how well the model handles varying similarity, especially low-similarity regions. The Sun80 dataset consists of 80 images, each paired with one reference image selected from 20 web-searched candidates. It tests the model's ability to select and use the most suitable reference in a multi-reference setting. The Urban100 dataset contains 100 images of urban scenes with strong lines and geometric structures. It uses the image itself as the reference to assess the model's ability to exploit internal self-similarity, particularly for complex textures and geometry. The Manga109 dataset includes 109 Japanese manga images. A random image from the dataset is used as the reference to evaluate how well the model handles content and structure unique to manga-style images. MR-SR is similar to CUFED5, but the LR and Ref images are paired one-to-one, with a total of 80 pairs of images. It is used to test the model's basic reconstruction performance in practical scenarios.  
Evaluation metrics: We use PSNR and SSIM on the Y channel of the YCbCr color space as evaluation metrics. The HR images are downsampled by a factor of 4 using bicubic interpolation to generate the input LR images for evaluation.
# Achieving Low Latency Communications in Smart Industrial Networks with Programmable Data Planes
## 2 Concepts and Technologies
### IIoT Communications  
Industrial communications have multiple factors that affect the quality and efficiency of services and applications. Some of those are straightforward elements, such as hardware equipment or physical technologies used for transmission (electrical or optical). However, there are other less-obvious aspects, such as the application layer protocol, which has a substantial impact on the performance of the communication, mainly due to the structure of IoT packets. These packets do not carry abundant information, because sensor measurements commonly have few bytes, which results in a poor header-to-payload rate. This means that sending a packet requires attaching a lot of control information as packet headers (Layer 2, IP, TCP...), just to send a small value over all the layers. IIoT specific application layer protocols provide a solution for that problem, having other benefits for sensors such as low resource usage.  
There are multiple specific protocols for IIoT message transmission in industrial scenarios, such as MQTT [5], XMPP [6] and OPC UA [7], which rely on a publisher-subscriber model, or Modbus [8] and CoAP [9], which follow a request-response model. Apart from the communication model, there are additional differences among these protocols, such as the used transport layer protocol (TCP, UDP, SCTP...), the implemented security mechanisms (TLS, X.509 certificates...), or the use of intermediary entities, such as message brokers.  
MQTT is one of the most common protocols for IIoT communications, following a publisher-subscriber model consisting of three different entities: the publisher, the subscriber, and the intermediate broker. This broker acts as an intermediary between the other two endpoint-communicating entities, as the publisher sends messages to a topic in the broker and any subscriber can join a topic to receive the messages that are published on it. In an IIoT scenario, a topic can represent a measurement, such as temperature, acceleration, etc.  
MQTT is sent over TCP, a lossless and bidirectional transport layer protocol. This means that a connection is established between a client and a server, keeping track of the session, which requires the exchange of additional messages. In fact, Figure 1 shows that before sending a publication, the publisher has to establish a TCP connection with the broker server through the TCP 3-way-handshake, then establish the application layer connection, send the message with the data, and finally close the connection.
## 4 Performance Evaluation
### Performance Results in the Real Scenario
Finally, two variants of the proposed EAMUD, i.e., EAMUD-P and EAMUD-D, perform best on the average ratio of the event link communication time among these five schemes in the real scenario. Whenever the UAV dispatch energy threshold is larger than 75%, EAMUD-P achieves the highest values of the average ratio of the event link communication time. Oppositely, EAMUD-D should be adopted as the UAV dispatching scheme when the UAV dispatch energy threshold is smaller than 75%. As mentioned in the beginning of Section 4, the number of events (\(N_{ev}=N_{rs}\times EP\)) in this simulation is equal to the product of the number of total road segments (\(N_{rs}\)) and the event percentage EP. Hence, the total number of sub-events in the simulation is equal to the sum of the number of sub-events of each event, i.e., \(\sum\limits_{j=1}^{N_{gt}}N_{j}\), where \(N_{j}\) is the number of sub-events of \(Ev_{j}\). In general, if two UAVs, one dispatched to serve the sub-event and the other one charged fast enough in GCS for replacing the dispatched one when needed, can be allocated to each sub-event, the average ratio of event link communication time can reach 1, as shown in Figure 13 when the number of UAVs is larger than 240. However, if the number of UAVs in GCS is lower than twice the total number of sub-events, the average ratio of event link communication time decreases faster as the ratio of the number of UAVs over the total number of sub-events becomes smaller, due to the more serious UAV contention and dispatch exception. We will try to model this complex relationship in the future.
## 2 Review
### Traditional Computer Vision-Based Techniques  
PCB inspection is essentially a task of comparing an input to a reference design with a goal to find deviations. The range of acceptable deviations depends on objects for inspection. The simplest technique could be to match two images directly pixel by pixels to find the differences. However, it is rarely used because of its sensitivity to even minor transformations of images, such as rotation, intensity, etc. Generally, an image is first converted into a vector of representative parameters, called feature vector, which is subsequently used for further processing.  
Surface inspection techniques are used in manufacturing to ensure quality of the produced items. In [11], the inspection techniques are classified into groups based on feature selection methods, e.g., statistical, structural, filter-based, and model-based. In statistical methods, the first order statistics (mean, histogram, range etc.) are used in conjunction with higher order statistics. In [12], Weibull distribution was used on local edges to detect texture anomalies, and in [13], images were compared using statistical parameters. In filter-based approaches, banks of filters, such as Gabor filters, Fourier transforms, etc., are applied to the image to get image features. In problems involving texture backgrounds, if the knowledge of texture pattern generating process is available, then the model-based approaches naturally yield very accurate results in texture analysis. Such approaches are very useful in textile industry. SURF (Speed Up Robust Features) [14] and SIFT (Scale-Invariant-Feature-Transform) [15] are two of the most popular feature extraction techniques, which have demonstrated excellent performance in many other applications.  
Soldering and layout defects in PCBs are highly specialized domains, containing their own set of techniques, and most of the existing work employ traditional CV-based techniques. Majority of the existing techniques are customized for defects relevant to specific soldering technology and/or PCB layout, and they cannot be used for another soldering technology without significant modification. In [16], soldering defects are categorized in 14 most common types, and various operations, such as image subtraction, logical XOR, etc., have been used to detect them.
# Performance Evaluation of Random Access Methods for IoT-over-Satellite
## 3 Method
### Access Models for IoT-over-Satellite  
In order to assess the uplink performance of IoT-over-Satellite system for different MAC layer configurations, the framework emulates IoT-over-Satellite uplink transmissions in four access models: (i) pure-ALOHA, (ii) scheduled access, (iii) scheduled access with repetition, and (iv) scheduled round robin transmissions. A conceptual snapshot of the transmissions under the random access scenarios is illustrated in Figure 9. The IoT traffic implemented in this work is randomly generated, i.e., each device randomly transmits its signals in cases I-III. Case IV deals with a hypothetical scenario where we assume that the traffic is scheduled to showcase the upper limit and not a practical scenario. The details are further explained below,  
Figure 8: Comparison of the uplink BER varying with transmit power for case II with and without the Doppler shift.  
Figure 9: Illustration of the different IoT-over-Satellite uplink access models that are considered in this framework.
# An Energy-Efficient and Obstacle-Avoiding Routing Protocol for Underwater Acoustic Sensor Networks
## 5 Performance Evaluation
### Experimental Framework  
In our simulation, sensor nodes are randomly deployed in a 9000 m \(\times\) 9000 m \(\times\) 7000 m three-dimensional underwater space. There is at most one sensor node per 1 km\({}^{3}\) area. These sensor nodes are identical in every feature, such as the initial energy, communication power, transmission range and so on. Among these sensor nodes, we only set up one source-destination node pair to communicate with each other. Marine animal is also randomly active in this area. The source node generates packets follows the Poisson distribution. The transmission bit rate is 9 kb/s. The location information of marine animal accounts for 12 bytes, including latitude and longitude and depth information, the time-stamp consists of two unsigned 4-bytes integers representing seconds and nanoseconds, and the length of the packet which contains the location information of the marine animal and the time-stamp is 200 bytes. The transmission radius is 1500 m. The bottlenose dolphin is chosen as the marine animal, which occupies a frequency band of 0.52 kHz to 33 kHz [32], and the frequency used by the UASNs is 10 kHz. We use the same broadcast MAC protocol as in [16]. The experiment was simulated with Aqua-Sim [33] software, which is an underwater network simulation software based on NS-2. The simulation parameters are listed in Table 4.
# Resilience of LTE-A/5G-NR Links Against Transient Electromagnetic Interference
## 3 Materials and Methods
### Testbed Configuration and Parameter Specifications  
The experimental setup facilitates a thorough analysis of how various interference parameters affect the stability and performance of the communication link. In the context of an LTE-A/5G-NR communication link, a series of experiments were carried out. The PHY layer parameters of the LTE-A and 5G-NR are shown in Tables 2 and 3, respectively. For the transmission and reception of the baseband waveforms, we used separate USRPs to rule out the effect of cross-channel leakage. Figure 3 shows the photo of the experimental setup where three National Instruments X310 USRPs (Europe) are connected to a laptop with an Intel Core i7 processor and at least 32 GB of RAM. via an Ethernet switch. We generate transient EMI at various RF frequencies within the bandwidth of interest. The initial phase included frequency scanning to pinpoint the frequencies most susceptible to performance degradation under jamming conditions. The findings revealed that the centre frequencies of 2.1955 GHz, 2.2 GHz, and 2.2045 GHz were especially vulnerable to interference.  
Figure 3: Photo of the experimental setup.  
Figure 2: Road transport powered by electricity.  
The bandwidth used in this experiment is 10 MHz. Additionally, the EVM is worse as the interference increases, particularly for higher-order modulation such as 64-QAM. In the absence of interference, it was observed that the EVM RMS is approximately 5%, which is as low as possible with a zero BER. The generated waveform uses 64-QAM modulation with a forward error correction (FEC) code rate of 3/4, a subcarrier spacing of 30 kHz, and a bandwidth of 10 MHz, and it supports up to 24 resource blocks.
## 2 Materials and Methods
### Methods
#### 2.3.2 Methods to Detect Forest Degradation from Remote Sensing Data
And the indicators were normalized to make the trends of different indicators comparable. By analyzing the degradation rate, and adopting the same classification strategy as the degradation from MODIS products, the forest degradation map with a high resolution (30 m) was obtained. Degradation rate, defined as the area degraded over a given period of time compared to the initial forest area [37], could be described as follows: \[\theta=1-\left(1-\frac{I_{1}-I_{2}}{I_{1}}\right)^{\frac{1}{\
u}}\] (1) where \(\theta\) is the degradation rate of the indicator, \(I_{1}\) is the indicator at data 1 and \(I_{2}\) is the indicator at date 2, \(Y\) is the weighted average time interval. * Validation of the Results To assess the performance of the forest degradation detecting method, ground survey forest degradation data was collected to validate the results. Specifically, the forest degradation area and degree from satellite observation data were compared with the provincial ground survey data. Besides, forest degradation map was validated with high resolution forest degradation map from Landsat ETM+ data pixel by pixel. The Determination Coefficient (R\({}^{2}\)) and Root Mean Square Error (RMSE) were used to quantify the accuracy of the detection method.
# Perturbations During Gait on a Split-Belt Treadmill: A Scoping Review
## 4 Discussion
### Studies on Mediolateral Perturbations and Treadmill Tilts
Participants adjusted their stepping patterns, modifying step length by approximately 7% and step width by 9% to accommodate the rotational movements. The angular velocity of yaw perturbations resulted in a 10% increase in whole-body angular momentum, requiring effective gait adjustments to maintain forward progression. [PERSON], et al. [44] analyzed only pitch perturbations, which included three slope angles (downhill: \(-8^{\circ}\); level: \(0^{\circ}\); uphill: \(+8^{\circ}\)). Participants demonstrated increased whole-body angular momentum as they adjusted their posture to counteract the tilt. Muscle activation increased significantly to manage the perturbation. Participants exhibited up to a 20% increase in activation of the quadriceps and hamstring muscles and a 25% increase in core stabilizing muscles such as the rectus abdominis and obliques. Moreover, participants modified their gait to cope with the tilt. They adjusted their step length by approximately 10% and increased step width by about 12%. These changes helped maintain balance and adapt to the altered treadmill orientation. Overall, these studies demonstrated that different types of perturbations (pitch, roll, and yaw) required specific muscle activation and gait adjustments, highlighting the intricate nature of maintaining dynamic stability and the importance of targeted rehabilitation strategies for gait and balance training.
## 2 Rocking Foundations for Seismic Loading  
### Rocking Mechanism and Acceleration Amplification Ratio  
Figure 1 shows the schematic of a simplified, relatively rigid, shear wall-type rocking structure supported by a shallow foundation, the major forces acting on the structure, and the forces and displacements acting at the soil-footing interface. The key parameters that govern the behavior of a rocking system include the critical contact area ratio of the rocking foundation (\(A/A_{c}\)), slenderness ratio of the rocking structure (\(h/B\)), and rocking coefficient of the soil-foundation-structure system (\(C_{r}\)) [26]. \(C_{r}\) is essentially a non-dimensional, normalized form of the ultimate moment capacity of rocking foundations and can be expressed by [29]:  
\[C_{r}=\frac{B}{2\cdot h}\left[1-\frac{A_{c}}{A}\right] \tag{1}\]where \(B\) is the width of the foundation in the direction of shaking and \(h\) is the effective height of the structure. The \(A/A_{c}\) is essentially a factor of safety for rocking foundations and defined as the ratio of total base area of the foundation (\(A\)) to minimum foundation area required to be in contact with the soil (\(A_{c}\)) to support the applied vertical load [18].  
The output parameter of machine learning models developed in this study is acceleration amplification ratio (AAR) of the rocking foundation and it is defined as:  
\[\text{AAR}=\frac{a_{max,str}}{a_{max}} \tag{2}\]  
where \(a_{max,str}\) is the peak horizontal acceleration at the effective height of the structure and \(a_{max}\) is the peak horizontal ground acceleration of earthquake shaking. By comparing the maximum moment experienced by the soil-foundation system due to the inertial forces from the structure with the moment capacity of the rocking foundation, the following approximate relationship can be obtained for a theoretical upper bound for the AAR of a rocking foundation supporting a relatively rigid, SDOF-type structure [26].  
\[\text{AAR}\leq\frac{C_{r}}{a_{max}} \tag{3}\]  
Equation (3) implies that the foundation moment capacity limits the maximum seismic force demands transferred to the structure because of nonlinear soil-foundation-structure interaction. Though the relationship given in Equation (3) is approximate, it can be used to obtain simple, statistics-based empirical relationships for AAR.
# Review of Recent Phased Arrays for Millimeter-Wave Wireless Communication
## 1 Introduction  
Of the many fundamental inventions whose histories have been well documented, the origin of the antenna array is not generally known. There has been much focus on [PERSON], who was a Nobel Prize-winning scientist, and his famous transatlantic wireless communication in December 1901 [1]. An array of 20 antenna elements was designed for the experiment. Unfortunately, strong winds destroyed the designed array system; therefore, a two-element antenna array was used to successfully transmit a repeated Morse code signal letter \"S\" from Poldhu, UK to St. John's in Canada. However, another Nobel Prize-winning scientist, [PERSON], was awarded the recognition globally for the discovery of the electronically scanning phased array. His innovation was initially triggered by the role of the U.S. in World War II, and was the reason for the development of _Eagle_, which was the first reported radar-based bombing system. Until now, the use of phased arrays has been very common in the defense domain, and the design of warships and military jets focus on phased array radars.