esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction-Based Energy Saving Mechanism in 3 GPP NB-IoT Networks
## 5 Simulation and Results
### Results  
Figure 14 shows the active time for the case of legacy NB-IoT, PBESM, and ideal prediction. The ideal prediction is the case that applies the proposed mechanism, but it corresponds to a PPE prediction of processing delay with 0% error and it can be regarded as the improvement limit for the processing delay prediction. We considered a target probability of successful delay prediction of 90% and a tail index of 3 for the Pareto distribution. The ideal prediction can reduce the active time from 8% to 23% according to the channel condition and compared with the legacy NB-IoT. For the PBESM, the active time only reduces from 5% to 16% for the medium and bad channel quality. This performance difference is caused by the prediction error of the processing delay. In contrast, for a good channel quality, the active time rises by up to 8% depending on the scenario. The reason is that for a good channel quality, the time taken for random access is smaller than the prediction error of the proposed mechanism.  
Figure 13: Active time and energy consumption for UE report scenario.  
For energy consumption, the PBESM outperforms the legacy NB-IoT from 10% to 34% regardless of the channel quality, as shown in Figure 15. We can see that there is a slight decline in performance from 3% to 5% compared to the ideal prediction. Given that the uplink transmission is delayed by the prediction error, the total active time for one session can increase, but the radio transmission time and energy consumption decrease. Hence, the proposed scheme is effective in terms of energy consumption. For instance, when we assume a scenario of a metering device with a 5 Wh AA battery and check the energy consumption everyday through the network, the battery life is 12.26 years for case 4 and medium channel quality in the legacy IoT, but it increases to 15.95 years by applying the proposed mechanism.  
Figure 14: Active time according to channel condition for legacy NB-IoT, PBESM, and ideal prediction.  
Figure 16 shows the energy consumption and active time according to the processing delay distribution and target probability of successful delay prediction obtained from the PBESM algorithm. These results correspond to case 4 for a good channel quality. A high target probability of successful delay prediction in the PPE results in energy saving across more sessions. However, the prediction error also increases and time to complete the transmission becomes excessively larger than that for the legacy NB-IoT. In real environments, the target probability should consider the quality-of-service deadline of the session. For medium and bad channel quality, we recommend using 90% as the target probability. Considering the processing delay distribution, as the tail index becomes larger, the processing delay can be predicted with a smaller error; thus, it can improve both active time and energy consumption.  
Figure 15: Energy consumption according to channel condition for legacy NB-IoT, PBESM, and ideal prediction.
# A Novel Fragmented Approach for Securing Medical Health Records in Multimodal Medical Images
## 3 Materials and Methods
### Proposed Modified LSB Embedding Process for Medical Images
```
Input:  patient_info //patient's confidential text-based information (string)  secret_key //secret key (password) for encryption (string)  medical_images //list of medical images (CT/MRI/Ultrasound)  num_fragments //number of fragments to create from the encrypted data Output:  watermarked_images //modified medical images Steps:  ciphertext = encrypt(patient_info, AES128)  fragments = subStrings(ciphertext, num_fragments)  selected_images = random(medical_images, num_fragments)  lsb_replace = 2 //LSB to replace from [1, 2, 3, 4]  pixel_offset = 4 //pixel offset value from [1, 2, 4, 8, 16, 32] for each fragment \(f\) in fragments do  fragment_bits = reshape(binary(f),-1)//1D reshape  img = selected_images[index(f)]  h,v = shape(img)  image_pixels = reshape(img,-1)  pixel_bits = binary(image_pixels)  bits_inPixel = length(pixel_bits(1))  counter c = 0  counter j = 0 for i= 1; i<length(fragment_bits); i++ do  pixel_bits(c, bits_inPixel-1)= fragment_bits(i)  i = i + 1  pixel_bits(c, bits_inPixel)= fragment_bits(i)  c = c + pixel_offset  if c > length(pixel_bits) then  j = j + 1  c = j  end if  endfor  image_pixels = int(pixel_bits)  img = reshape(image_pixels, [h,w])  watermarked_images.append(img) endfor  return watermarked_images
```  
**Algorithm 1**Proposed Modified LSB Embedding Process for Medical Images
## 7 IoT-Defined Application Protocols
### Web Socket  
Web socket is a low-latency client/server communication that is standardized on HTTP for TCP. This protocol allows the client to get real-time updates from the server without requesting a new session, till the session is ended. As the uninterrupted communication of IoT devices with remote servers is very important, the web socket interface provides TCP-based communication of IoT nodes with remote servers. This communication is available for data updates on the server and from the server. Web socket provides a full duplex communication channel for remotely connected web servers and nodes. The data that are communicated using asynchronous client/server communication start exchanging without publish/subscribe and request/response mechanisms [52].  
The remote host starts its session after building a handshake from the client; this server gets busy only after communication is started. The client, on the other hand, can draw routes to the server without any external third-party tool. The session terminates when both server and client agree upon it.  
MQTT and XMPP are usually for client-to-server communications, and web socket is fairly new in this field. However, web socket seems to be more powerful, as the literature suggests. Web socket uses a single web-socket library at the client end for initiating web-socket-based real-time communication. The library can be used using programming language like Java or Python.  
Web socket is efficient for the following reasons.  
1. There is no real-time lag or polling interval while in communication with server.
2. There is no need of bidirectional authentication for each message. A request for each packet is not necessary while connections are set up beforehand.
3. Data are only sent when they are needed, avoiding network congestions and bandwidth deficiency.
4. After the client connects with a remote server, no headers are used for communication purposes and only data fields are shared.
## 2 Background
### Management Outcomes  
DM in IAM may involve a wide range of outcomes and optimization methods can analyze various outcomes in terms of objectives and constraints. The financial expense of management interventions is the most popular and important outcome in IAM. Over 60% of the reviewed articles attempt to reduce the expense (objective) and around 40% of the articles have a management budget (constraint). The main types of expenses are infrastructure user cost [78; 99; 100], M&R treatment cost [101; 102; 103], asset management agency cost [87; 100; 104], and the total life-cycle cost [48; 105; 106]. However, the exact calculation of the expenses may vary from case to case. Optimization can also help in allocating limited funds across different types of infrastructure assets, working zones, or management projects [107; 108; 109].  
Infrastructure asset condition is also an important outcome. The goal of IAM is to keep infrastructure assets in good condition in order to provide the required level of service. Around a quarter of the reviewed articles directly optimize infrastructure asset condition [110; 111; 112] or keep the condition at an acceptable level [44; 87; 113].  
Level of service, as another criterion to assess the infrastructure, obtains increasing attention and applications [11; 114; 115]. Rather than an average index, it measures the performance of infrastructure in a better and more meaningful manner.  
Besides the traditional outcomes, other outcomes are also analyzed with optimization, depending on the addressed problems, e.g., traffic [116; 117; 118], risk [49; 119; 120], investment efficiency [35; 77; 121], and greenhouse emission [48; 63; 91]. These outcomes indicate the deep and wide thinking when making management decisions. Presently, IAM does not only trade-off of expense and infrastructure condition but also looks at the needs of different parties and aspects. With the assist of optimization, the analysis of different outcomes becomes possible.
# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 1 Introduction
The objective of this paper is to evaluate the performance of a system that integrates monocular visual odometry (implemented with a camera pointing upward) and GNSS signals. Since GNSS signals are subject to severe multipath in urban canyons, an image-based multipath mitigation approach was developed. This approach consists of segmenting an image into sky and non-sky areas. Images taken with an upward-facing camera are complex and difficult to segment, we introduce a tailored segmentation method appropriate for vehicle navigation applications. The remainder of this paper is organized as follows: Section 2 briefly presents the existing works related to this research; Section 3 describes the vehicle motion estimation steps using a camera; Section 4 implements the improved segmentation approach, as well as the Non-Line-Of-Sight (NLOS) mitigation algorithm; Section 5 presents the GNSS/vision integration strategy we propose; Section 6 describes the experiment, results and analysis; and Section 7 draws some conclusions.
## 2 Materials and Methods  
### Laboratory Tests  
To simulate the acidification process based on CO\({}_{2}\) enrichment event such as CCS, a CO\({}_{2}\) injection system was adapted from the experimental set up described by [63; 64], with further detailed experiments in [65]. Briefly, the different acidification scenarios reached pH values of 8.0, 7.5, 7.0, 6.5, and 6.0, controlled using the injection system, and non-CO\({}_{2}\) injected treatment as control (pH 8.3). The experiment was developed using clean natural seawater from Enesota Beach in Guaruja, SP/Brazil [66]. Adult mussels _Perna perna_ were acquired from a farming zone located at the Cocanha beach-Caraguatatuba municipality in Sao Paulo's coastal zone. The organisms were transported to the laboratory, where they were kept for 24 h prior to the assays under temperature (22 \({}^{\circ}\)C) and salinity (35 ppt) controlled in a 300 L aquarium with seawater.  
Ten individuals of mussel _Perna perna_ were introduced per 20 L chamber (two replicates per treatment). The laboratory conditions were controlled: seawater temperature at 20 \(\pm\) 2 \({}^{\circ}\)C, salinity 35 ppt, 8.0 mg L\({}^{-1}\) dissolved oxygen, and photoperiod 12:12. Water quality was maintained through renewing every 48 h.  
After 48 and 96 h of exposure, survival was recorded, and ten organisms from each pH treatment (five organisms from each tank) had the hemolymph withdraw before dissection gills were frozen (\(-\)80 \({}^{\circ}\)C). The hemolymph was collected to assess the lysosomal membrane stability through neutral red retention time assay (NRTT), while gills were used to assess DNA damage (strand breaks) and lipid peroxidation (LPO).
# KRT-FUAP: Key Regions Tuned via Flow Field for Facial Universal Adversarial Perturbation
## 4 Experimental Results
### Effect of Different Regions on Recognition Accuracy  
We utilized the LFW and CASIA-WebFace datasets to examine the trend in the recognition accuracy of target models when occluding key and non-key regions on the IResNet50, MobileFaceNet, and MobileNetV1 backbone extraction networks. By controlling the level of random noise used for occlusion, we ascertained the extent of influence of different noise intensities on various regions. The results are depicted in Figure 4.  
We randomly selected 2000 pairs of facial images from each dataset. Gaussian noise of varying levels ranging from 0 to 1 was overlaid on different regions of one of the paired images. In increasing the noise level, the results of testing on the three backbone networks using both datasets demonstrate that overlaying noise on facial key regions leads to a more substantial decrease in the final recognition accuracy. This suggests that features in facial key regions carry greater weight in face recognition discrimination.
###### Abstract  
In subway tunnel construction, current hand-eye integrated drilling robots use a camera mounted on the drilling arm for image acquisition. However, dust interference and long-distance operation cause a decline in image quality, affecting the stability and accuracy of the visual recognition system. Additionally, the computational complexity of high-precision detection models limits deployment on resource-constrained edge devices, such as industrial controllers. To address these challenges, this paper proposes a dual-arm tunnel drilling robot system with hand-eye separation, utilizing the first-frame localization and follower tracking method. The vision arm (\"eye\") provides real-time position data to the drilling arm (\"hand\"), ensuring accurate and efficient operation. The study employs an RFBNet model for initial frame localization, replacing the original VGG16 backbone with ShuffleNet V2. This reduces model parameters by 30% (135.5 MB vs. 146.3 MB) through channel splitting and depthwise separable convolutions to reduce computational complexity. Additionally, the GIoU loss function is introduced to replace the traditional IoU, further optimizing bounding box regression through the calculation of the minimum enclosing box. This resolves the gradient vanishing problem in traditional IoU and improves average precision (AP) by 3.3% (from 0.91 to 0.94). For continuous tracking, a SiamRPN-based algorithm combined with Kalman filtering and PID control ensures robustness against occlusions and nonlinear disturbances, increasing the success rate by 1.6% (0.639 vs. 0.629). Experimental results show that this approach significantly improves tracking accuracy and operational stability, achieving 31 FPS inference speed on edge devices and providing a deployable solution for tunnel construction's safety and efficiency needs.  
Keywords:hand-eye separation; initial frame positioning; object detection; object tracking
## 4 A Transmission Strategy for Minimizing Energy Consumption
### Energy Consumption Analysis
Although we have derived here the particular expressions for the specific case of our channel and receiver models, the strategy is directly extendable to other models. Let us also remark that the value of the localization period \(T_{0}\) does not have any influence on the applicability of our strategy as long as it is big enough to send the required number of packets, which is usually the case in practice. Furthermore, the value of \(T_{0}\) does not need to be constant in time. In fact, it can be optimized to adapt to the mobility characteristics of the mobile node [15; 16; 17; 18]. The combination of the proposed optimization with the optimization of the localization period may lead to further energy savings. Note that as we have assumed that there is a synchronization schedule known by all the nodes, the localization messages should be sent within the assigned periods. In principle, this does not affect the localization accuracy or the proposed strategy, as long as the required number of packets can be transmitted. Finally, we would like to point out that in practice, the synchronization of the network may not be perfect. In this case, the nodes should be listening to possible transmissions also during the idle periods in order not to lose packets, so the energy consumption in reception mode would increase. The proposed strategy is not directly affected, as it optimizes the energy consumption in transmission mode, but the energy savings, relative to the total energy consumption, would not be as high as in the ideal synchronization case. Another effect of an imperfect synchronization is that TOA measurements will not be so accurate, so its standard deviation will increase and the distance estimation errors will be higher. This does not affect the performance of the proposed strategy, as it was formulated in terms of the desired number of packets. But clearly, the desired number of packets to achieve a certain accuracy will increase with respect to the ideal case (see Equation (25) in A: if \(\sigma_{t}\) increases, \(n\) has to increase to achieve the same \(\sigma\)), and the energy consumption will increase accordingly.
# Planetary Gearbox Fault Diagnosis Based on ICEEMD-Time-Frequency Information Entropy and VPMCD
## 5 Experimental Verification
### Method Comparison
#### 5.2.1 Comparison between the Proposed Method with the Wavelet Entropy Method  
Wavelet entropy, as a traditional time-frequency analysis method, is widely used in weak signal fault diagnosis. The specific algorithm steps are as follows:  
First, the vibration signal is preprocessed.  
Second, the planetary gearbox vibration signal is decomposed into three layers to obtain eight sub-band signals by using the sym6 wavelet base.  
The third step is to calculate the total energy of each frequency band signal and normalize the energy. The wavelet entropy of each sub-band is calculated to obtain an 8-dimensional feature vector. The feature vector is dimension-reduced by principal component analysis (PCA). The clustering results are shown in Figures 11-14.  
Figure 11: Clustering result of wavelet entropy under condition 1.  
Figure 10: Clustering result of gear fault features under condition 4.  
As shown in Figures 11-14, when the feature vector extracted by the wavelet entropy method was used to identify the degree of fault type, a certain degree of aliasing appeared under the operation condition 1 and operation condition 4. Therefore, the method proposed in this paper is desirable and has better clustering results than does the wavelet entropy method in fault feature extraction.  
Figure 14: Clustering result of wavelet entropy under condition 4.  
Figure 12: Clustering result of wavelet entropy under condition 2.  
Figure 13: Clustering result of wavelet entropy under condition 3.
## 2 Related Work
### Infrared Small Object Detection  
The main objective of infrared small object detection is to pinpoint small, heat-emitting objects like missiles and bombs. For the past 10 years, numerous procedures have been presented and developed for the study of infrared small object detection. For example, [PERSON] et al. [51] developed a new infrared patch tensor model for detecting infrared small targets. This model employs a novel constraint that combines the tensor kernel norm with a weighted \(L_{1}\) norm, effectively suppressing the background and enhancing the detection performance. [PERSON] et al. [52] introduced a multiscale local uniformity measure to distinguish small targets from the infrared background, and the results demonstrated that the model exhibited outstanding performance. [PERSON] et al. [53] proposed a method to improve the detection effect of small targets in the infrared image via the target intensity and gradient. In addition, some scholars have proposed other methods, such as asymmetric contextual modulation [54], dense nested attention network [55], and nonconvex tensor fibered rank approximation methods [56], etc., to achieve the identification of infrared minor objectives. While these techniques successfully differentiate the movement of minor objectives, their detection outcomes are heavily dependent on the thermal contrast between the background and the object. Furthermore, identifying minor objects in infrared images always requires a distinct background, a situation that is uncommon in natural environments.
# Qom--A New Hydroologic Prediction Model Enhanced with Multi-Objective Optimization
## 5 Experimentation
### Results After Choosing Solutions  
Once the resulting solutions are selected after calibration and validation, their corresponding decision variables are registered and analyzed in terms of the hyetographs and hydrograms, where the volumes of losses and excesses, as well as the fitting error of observed versus estimated data series are plotted. These decision variables are shown in Table 2 for each problem instance, which have been used to set the Qom model. The corresponding NSE and PBIAS obtained by using these variables on the different calibration and validation data series are shown in Table 3, where additional information concerning the time period of observation, time slot of registration (10 min, 15 min, 1 h, and 24 h) and the number of registries is also provided. In general, values of NSE \(\geq\) 0.75 and PBIAS \(\leq\) 10% are obtained for calibration series, while NSE \(\geq\) 0.6 and PBIAS \(\leq\) 25% can be observed for validation series of data. In accordance with reference studies in the literature [39], where registration periods are conducted monthly, values of NSE \(\geq\) 0.50 and PBIAS \(\leq\) 25% can be considered as satisfactory. In the light of these results, we can claim that the prediction errors obtained by our proposed Qom model based on multi-objective calibration are more than satisfactory, since it is able to deal with complex problem instances with observation time periods significantly lower than 1 month, as done in Reference [39]. \begin{table}
\begin{tabular}{l r r r r r r} \hline \hline
**Problem Instance** & **\(Rmax\) (mm)** & **\(Smax\) (mm)** & **\(VC\) (mm/h)** & **\(K_{i}\) (h)** & **\(K_{p}\) (h)** & **\(K_{g}\) (h)** \\ \hline
1. Asua River & 16.0 & 131.0 & 0.09 & 8,613.0 & 6.6 & 20,446.0 \\
2. Artibai River & 12.7 & 45.0 & 0.02 & 653.0 & 7.6 & 75,596.0 \\
3. San Antonio River, Loop 410 & 13.7 & 46.0 & 0.29 & 132.0 & 1.1 & 3.0 \(\times\) 10\({}^{6}\) \\
4. San Antonio River, Goliad & 218.0 & 435.0 & 0.29 & 107.0 & 50.0 & 16,150.0 \\
5. Juquia River, 4F-018R & 7.0 & 10.0 & 0.01 & 50.0 & 4,500.0 & 3,000.0 \\
6. Bermejo River, Balapuca & 17.0 & 349.0 & 0.27 & 1829 & 60.0 & 50,398.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Selected solutions from resulting RF approximations after experiments and validations.