esa-sceva/satcom-chunk-collection huggingface dataset:
# Micro-Satellite Systems Design, Integration, and Flight
## 3 ChipSat RTOS and RF Development
### Ground Station
#### 3.5.4 Tiny GS Satellite Balloon Launch  
If the ChipSat transmission could not be detected by our SDR, another alternative was to use a receiver network. The receiver network, however, depends on the microchip having LoRa (long-range capability). The CC1310 does not have this feature, but the next-generation ChipSat will be improved to include it. The receiver network was tried several times, one of them during a balloon launch on 10 October 2021. The aim was to attempt and set up a connection with the LoRa on the ChipSat during the mission using an Adafruit feather receiver. The balloon launch was performed to test the operation of the satellite and to capture footage of our satellite to raise awareness of our mission. See Figure 25 below for a picture taken by the satellite from the balloon.  
To communicate with the LoRa transceiver, a TinyGS ground station (consisting of an Adafruit feather and antennae) was selected. The TinyGS system enables anyone around the world to receive signals from LoRa satellites or any other flying device that uses low-power communication techniques. The TinyGS module uses an ESP32 board. It can communicate with the sx126x and sx127x LoRa transceivers. TinyGS has online instructions to help with the setup. In brief, the TinyGS software (version 21021701) is installed to connect to the TinyGS system. After the setup is completed, the TinyGS board can be plugged into the computer.  
The software will recognize it, and an IP address will show up on the LCD screen on the board. At this point, the operator will connect their laptop WiFi to the TinyGS SSID. Once connected, the IP address shown on the LCD screen can be entered in the web browser. The connected device will show a dashboard that will allow the user to set the parameters of the ground station. After that, the user can listen to the ChipSat LoRa through the TinyGS system. The received packets will show on the dashboard of the network. Figure 26 below shows an example of the dashboard.  
When the CubeSat balloon launch happened, packets were not yet ready to be sent. However, the test was performed to prove that we could successfully receive signals from the ChipSat LoRa transmitter. Although the transmissions could not be decoded, a timestamp was shown when the TinyGS ground station connected to the ChipSat (inside of the balloon payload). Each time the signal was detected, the timestamp and current height of the signal was detected. The timestamp was set to 1000 seconds. The timestamp was set to 1000 seconds.  
of the satellite were recorded. The current height was measured using GPS onboard the balloon payload. Figure 27 below shows the time of the transmissions we received and the respective height of the CubeSat.  
The experiment showed that the CubeSat could still send a signal when it was close to 100 k ft. In summary, a basic validation of the TinyGS system as a possible receiver for Alpha was performed.
# Prediction and Optimization of Open-Pit Mine Blasting Based on Intelligent Algorithms
## 7 Conclusions  
Blasting prediction and optimization are essential means for achieving controlled blasting and low-cost production in open-pit mines. Reliable prediction and optimization models can provide scientific blasting design parameters to guide actual production in mines. Therefore, this study proposed a GA-LSSVM prediction model and a MOPSO optimization model. The following conclusions are drawn:  
1. The GA-LSSVM model, compared with the RIME-LSSVM, PSO-LSSVM, unoptimized LSSVM, and Kuz-Ram empirical model, exhibited more accurate predictive performance in predicting ore blasting fragmentation. It had smaller error metrics and a larger fitting coefficient (RMSE = 1.947, MAE = 1.688, r = 0.962), significantly outperforming other prediction models;
2. By using MOPSO to solve the two-objective optimization model of blasting fragmentation and blasting cost, the blasting parameters were optimized as follows: burden (\(B\)) = 4.3 m, spacing (\(S\)) = 5.5 m, specific charge (\(q\)) = 0.51 kg/m\({}^{3}\), and subdrilling (\(H_{0}\)) = 2.0 m. The reliability of the scheme was verified through on-site blasting tests, which improved the blasting effect of the mine while reducing the overall cost;
3. There are many factors influencing blasting effects. To effectively carry out blasting prediction and optimization, in addition to increasing the necessary factors, the importance and relationships between different influencing factors should be considered. Furthermore, obtaining more sample data and finding better intelligent models are crucial for obtaining accurate data.  
All authors contributed to this study's conception and design. Field investigation and data collection were completed by [PERSON] Data analysis and management were completed by [PERSON] The first draft of the manuscript was written by [PERSON] and [PERSON], and all authors commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This project was supported by the Postgraduate Innovative Project of Central South University (2023 XQLH103), the Central South University-Hongda blasting Engineering Group Postgraduate Joint Training Base (2020 pyjd91) and the State Key Laboratory of Safety Intelligent Mining in Non-coal Open-pit Mines, National Mine Safety Administration (2024-K02).  
Not applicable.  
The data presented in this study are available in article.  
The team of authors express their gratitude to the editors and reviewers for valuable recommendations that have been taken into account to significantly improve the quality of this article.  
Author [PERSON] was employed by the company Hongda Blasting Engineering Group Co., Ltd. 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.
# Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data
## 6 Conclusions  
In this study, we present an approach that relies on only a single source of common business data (i.e., invoice data) as a foundation for creating predictive models capable of reliably identifying churners in real-world settings. We find that using historical dataspanning longer periods of time generally leads to better performing models and that periods of customer inactivity other than the one currently used to define churn might be used by the company. In that regard, this is one of the first studies to explore the effects of using different churn definitions, variable window widths for feature extraction, and the multi-slicing approach to dataset creation on predictive model performance in one place, thus providing valuable insights to practitioners on the most promising approaches to identifying churners in non-contractual B2B settings.
# Extensions and Enhancements to \"the Secure Remote Update Protocol\"
## 3 Enhancements and Improvements to the Original Protocol
### Sender ID  
In the original description of SRUP, it was assumed that there was no requirement to communicate details of the sender of a given message, since the centralised nature of the protocol meant it would always either correspond to a message from a server to a device, or from a device to a server. The identity of the device would be contained within the MQTT topic that the message was sent over; and it was assumed that a device receiving a message could safely assume that it came from the server.  
The paradigm of using the MQTT topic to convey the source/destination information means that no additional data need be consumed in sending this as a part of the messages. However in the event that multiple C2 servers are used simultaneously, there is no way for a device receiving a message to know which server has sent the message and, therefore, against which public key the signature should be verified.  
There are three potential solutions to this problem. The simplest is that the receiver should try each of the keys it has in turn before concluding that the message cannot be verified. This is however neither a scalable nor an elegant solution, and so, it can be rejected.  
Another alternative is to further use the MQTT topic hierarchy to signify the sender. For example, rather than subscribing to SRUP/dev0x01, the device and server(s) could subscribe to SRUP/dev0x01/# (using the MQTT topic wildcard character #). This would then enable messages from the device to use the higher level topic and for messages from different servers to use subtopics (e.g., SRUP/dev0x01/sv0x2F) to indicate their origination. The topic field of the MQTT message could then be parsed by the receiving device to extract the sender identity.  
The third method is to include an additional field within the base SRUP message (i.e., the part that is common to all messages, consisting of the header, sequence ID, signature and token) to contain the sender ID. This method requires the smallest amount of work from the receiver to identify the client; however, it also has the largest additional overhead on the message traffic. However, if we assume that each device ID is a 128-bit UUID, then by requiring a sender ID field, we only expand the base message from 282 bytes (2 bytes for the header, 16 bytes for the token, 8 bytes for the sequence ID and 256 bytes for the signature, assuming PKCS (Public-Key Cryptography Standards)#1 and a 2048-bit key) to 298 bytes; approximately a 6% increase in size. Although not negligible, this may be considered a preferable approach compared with the relative complexity of using the topic hierarchy-based approach, and it is the method that has been selected.
## 1 Introduction and Motivation
In this paper, we propose the development of a proof-of-concept assistive strategy implemented through a collaborative robot in meat cutting tasks in order to reduce the musculoskeletal disorders on the wrist of human operators working in the meat industry. The developed impedance control strategy enables a KUKA LWR robot to provide assistive forces to a professional butcher while simultaneously allowing motion of the knife (tool) in all degrees of freedom. Previous robotic systems for autonomous meat handling [4] required one or several robots for performing very specific meat cutting operations. For instance, the ARMS system [5; 6; 7] was based on the separation of beef shoulder muscles, the GRIBBOT system [8] was applied to chicken breast fillet harvesting while the DEXDE system [9; 10] was useful for ham deboning. Therefore these robotic systems could not be reused for other meat handling tasks since the quality of their cut was not enough for other types of meat pieces. The new proposed robotic system (called Exoscarne) works on the principle of pHRI (physical Human-Robot Interaction) to solve this lack of generalization. From one side, the expertise of the skilled butcher is kept since the cutting trajectory is defined by the human operator, who is holding the tool at the same time as the robot. From the other side, the robot carries the load of the tool and increases the cutting force when touching the meat so that the effort applied by the human is smaller. This system results in a greater flexibility for different meat cutting tasks and can adapt itself to on-the-fly decisions made by the user. Therefore, our new Exoscarne system, which will be described in the next sections (see Section 3 for its software components and Section 4 for its hardware components), is able to:  
1. Compensate the weight of the cutting tool. 2. Permit the user to move the tool in all 6 degrees of freedom. 3. Permit impedance shaping according to the operation being performed by the user. 4. Provide assistive forces during the meat cutting operations as per the user's convenience. 5. Allow the user to perform the operations autonomously.
# Experimental Study of LoRa Transmission over Seawater
## 4 Experimental Results and Analysis
### Discussion
The optimum set of parameters can be chosen considering only the particular application requirements since the reliable transmission can be achieved for virtually any combination of LoRa parameters within the bandwidth of \(BW=125\,\mathrm{kHz}\). Taking into account LoRa bit rate, these systems may be very useful for connection of environmental monitoring stations deployed in island countries. Moreover, this technology could be applied in early-warning systems for disaster prevention, for instance detecting volcano eruptions at long distances since the elevation of the volcano allows for a very long range LOS propagation paths.
## 4 Experimental Results
### Results and Analysis
#### 4.2.3 Results of Target Re-Identification
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Video ID**} & \multicolumn{4}{c}{**OSTrack-384**} & \multicolumn{4}{c}{**MixFormer-L**} & \multicolumn{4}{c}{**NeighborTrack-OSTrack**} & \multicolumn{4}{c}{**TRrack (Ours)**} \\ \cline{2-13}  & **ET** & **ED** & **TD** & **ET** & **ED** & **TD** & **ET** & **ED** & **TD** & **ET** & **ED** & **TD** \\  & **(Times)** & **(s)** & **(s)** & **(Times)** & **(s)** & **(Times)** & **(s)** & **(s)** & **(Times)** & **(s)** & **(Times)** & **(s)** & **(s)** \\ \hline
001 & 20 & 44.62 & 13.98 & 26 & 49.83 & 18.65 & 9 & 25.42 & 24.49 & **2 * & **2.72** & **2.64** \\
002 & 15 & 27.93 & 8.72 & 24 & 32.11 & 14.56 & 5 & 430.76 & 416.88 & **2** & **2.71** & **2.71** \\
003 & 16 & 34.77 & 14.21 & 13 & 24.89 & 17.55 & 6 & 29.56 & 27.14 & **4** & **5.27** & **5.27** \\
004 & 19 & 41.10 & 17.44 & 19 & 29.74 & 18.33 & 8 & 24.11 & 23.98 & **4** & **2.94** & **2.94** \\
005 & 22 & 137.73 & 95.56 & 26 & 113.86 & 106.49 & 5 & 38.66 & 36.54 & **3** & **3.01** & **3.01** \\
006 & 8 & 146.29 & 136.61 & 12 & 90.01 & 74.79 & 4 & 9.12 & 7.55 & **2** & **2.01** & **1.89** \\
007 & 17 & 24.80 & 8.56 & 8 & 17.13 & 6.59 & **0** & **0.00** & **0.00** & **0** & **0.00** & **0.00** \\
008 & 9 & 13.11 & 6.78 & 6 & 14.89 & 4.23 & 1 & 1.29 & 1.29 & **0** & **0.00** & **0.00** \\
009 & 14 & 47.88 & 24.96 & 12 & 39.47 & 26.55 & 11 & 26.54 & 24.48 & **3** & **5.42** & **4.89** \\
010 & 7 & 16.22 & 14.56 & 9 & 21.54 & 17.77 & 4 & 5.89 & 5.43 & **2** & **1.69** & **1.43** \\
011 & 16 & 42.98 & 36.66 & 17 & 54.32 & 47.62 & **1** & **4.32** & **4.16** & 2 & 5.67 & 4.93 \\
012 & 24 & 145.96 & 115.43 & 22 & 121.11 & 103.67 & 13 & 254.55 & 241.69 & **7** & **9.43** & **9.12** \\
013 & 9 & 54.22 & 38.14 & 10 & 43.12 & 34.56 & 6 & 16.55 & 14.29 & **3** & **8.76** & **7.84** \\
014 & 12 & 15.77 & 12.97 & 11 & 18.56 & 12.13 & 3 & 3.98 & 3.68 & **0** & **0.00** & **0.00** \\
015 & 14 & 66.35 & 38.54 & 16 & 64.59 & 42.84 & 8 & 54.51 & 48.67 & **2** & **7.56** & **7.07** \\
016 & 15 & 101.56 & 87.52 & 18 & 121.76 & 105.43 & 7 & 43.56 & 41.70 & **4** & **11.56** & **8.79** \\ \hline Avg & 14.81 & 60.08 & 41.92 & 15.56 & 53.56 & 40.74 & 5.69 & 60.55 & 57.62 & **2.50** & **4.30** & **3.91** \\ Sum & 237 & 961.29 & 670.64 & 249 & 856.93 & 651.76 & 91 & 968.82 & 921.97 & **40** & **68.75** & **62.53** \\ \hline \hline \end{tabular}  
* The bold numbers in the table represents the optimal value in the evaluation metrics.  
\end{table} 
Table 4: Target re-identification results of the CLV dataset. errors. The average value of the TD of TRTrack is 3.91 s, and the sum value is 62.53 s, indicating the effective target re-identification ability of our method. The comparison between the target re-identification performance of TRTrack and other algorithms is shown in Figure 8.
# Frequency-Based Adaptive Interpolation Filter in Intra Prediction
## 4 Experimental Results
With a slight increase in computational complexity, the proposed method allows for achieving a reduction in the BD rate compared with the VVC anchor. The sequence that shows the highest BD rate reduction is the BasketballDrill sequence in class C, in which the proposed method yields a gain of the Y component of \(-\)1.20%. The proposed method did not obtain the BD rate gain in some sequences because we used CIF sequences and FHD sequences for determining only the threshold of _high_freq_ratio_. However, if the thresholds for correlation and _high_freq_ratio_ are computed together for more various video sequences, the BD rate gain can be achieved in all test sequences with the computational complexity increases. The proposed method shows slightly better subjective image quality than the VVC anchor when the BD rate gain is achieved, and the proposed method shows quite similar subjective quality to the VVC anchor even when the BD rate losses are 0.01% to 0.06%.
## 3 The Proposed Optimization Algorithm
### Power Allocation
\begin{split}\frac{\partial\mathcal{L}(p_{m,k})}{\partial p_{m,k}}=& (1+\rho_{k})\eta_{m,k}\sqrt{\frac{(1+\gamma_{m,k})\abs{\mathbf{v}_{m,k}\mathbf{h }_{m,k}}^{2}}{p_{m,k}}}-\sum\limits_{k^{\prime}\in\mathcal{S}_{m,k}\cup k}{(1+ \rho_{k^{\prime}})\eta_{m,k^{\prime}}^{2}\abs{\mathbf{v}_{m,k^{\prime}}\mathbf{ h}_{m,k}}^{2}}\\ &-\sum\limits_{m^{\prime}\in\mathcal{R}\setminus m}\sum\limits_{ k^{\prime}\in\mathcal{U}_{l}}{(1+\rho_{k^{\prime}})\eta_{m^{\prime},k^{\prime}}^{2} \abs{\mathbf{v}_{m^{\prime},k^{\prime}}\mathbf{h}_{m,k}}^{2}}=0,\ \forall m\in\mathcal{R},k\in\mathcal{U}_{m},\end{split}  \tag{18}\]  
Then, the optimal power allocation is derived by  
\[p_{m,k}^{*}=\min\{\hat{p}_{m,k},P_{k}^{max}\} \tag{19}\]  
where  
\[\hat{p}_{m,k}=\frac{((1+\rho_{k})\eta_{m,k})^{2}(1+\gamma_{m,k})\abs{\mathbf{ v}_{m,k}\mathbf{h}_{m,k}}^{2}}{\left(\sum\limits_{k^{\prime}\in\mathcal{S}_{m,k} \cup k}{(1+\rho_{k^{\prime}})\eta_{m,k^{\prime}}^{2}\abs{\mathbf{v}_{m,k^{ \prime}}\mathbf{h}_{m,k}}^{2}+\sum\limits_{m^{\prime}\in\mathcal{K}\setminus m }\sum\limits_{k^{\prime}\in\mathcal{U}_{l}}{(1+\rho_{k^{\prime}})\eta_{m^{ \prime},k^{\prime}}^{2}\abs{\mathbf{v}_{m^{\prime},k^{\prime}}\mathbf{h}_{m,k}} ^{2}}}\right)^{2}} \tag{20}\]  
The Lagrangian multipliers \(\rho_{k}\) can be obtained by utilizing the gradient update method [21]. Specifically, in the \(t\)-th iteration, the Lagrangian multiplier is updated by the following expression  
\[\rho_{k}^{(t)}=[\rho_{k}^{(t-1)}-\phi_{k}(\bar{R}_{m,k}-r_{k})]^{+}, \tag{21}\]  
where the update step-size \(\phi_{k}\) plays a crucial role in controlling the convergence of the Lagrangian multiplier. After iterations, we need to deal with constraint C4\({}^{\prime}\). Assume \(\bar{\mathcal{R}}\) representing the set of APs that cannot meet \(\sum\limits_{k\in\mathcal{U}_{m}}{R_{m,k}}\leq C_{m}\), then we set \(\Delta C_{m}=\sum\limits_{k\in\mathcal{U}_{m}}{R_{m,k}}-C_{m}\), \(\forall m\in\bar{\mathcal{R}}\). The power allocation results can be adjusted as  
\[p_{m,k}=\left(2^{\left(R_{m,k}-\Delta C_{m}\left(R_{m,k}/\sum\limits_{j\in \mathcal{U}_{m}}{R_{m,j}}\right)\right)}-1\right)\frac{I_{m,k}}{\abs{\mathbf{ v}_{m,k}\mathbf{h}_{m,k}}^{2}},\ \forall m\in\bar{\mathcal{R}} \tag{22}\]  
where \(I_{m,k}=\sum\limits_{j\in\mathcal{U}_{m}\setminus\mathcal{S}_{m,k}}{\abs{ \mathbf{v}_{m,k}\mathbf{h}_{m,j}}^{2}p_{m,j}}+\sum\limits_{i=1,t\
eq m}^{M+1} \sum\limits_{j\in\mathcal{U}_{k}}{\abs{\mathbf{v}_{m,k}\mathbf{h}_{m,j}}^{2} p_{i,j}}+\
orm{\mathbf{v}_{m,k}\mathbf{h}_{m,j}}^{2}\sigma_{m}^{2}\).
# Voxel-Based Navigation: A Systematic Review of Techniques, Applications, and Challenges
## 4 Key Techniques for Voxel-Based Navigation
### Voxel Storage and Management
#### 4.4.2 Dynamic Grids  
Dynamic grid structures need to include real-time updating capabilities, flexible data structures, and support for high-frequency updates, to reflect changes in the environment and adapt to dynamic scenes. The main challenges lie in handling large amounts of dynamic data and maintaining consistency. Dynamic Tiling Grid (DT-Grid) [73] introduces dynamic-grid segmentation methods, allowing flexible adjustment of voxel grids based on different resolutions and data densities, improving visual effects in dynamic scenes but also adding complexity to dynamic adjustments. Hierarchical Run-Length Encoding (HRLE) [74] reduces voxel data storage needs through systematic run-length encoding while maintaining spatial hierarchy, making access and manipulation of voxel data feasible. VDB uses a hierarchical sparse voxel data structure, dividing volumetric data into small chunks and managing these chunks through a tree structure (typically an octree). Nodes containing only valid data are rapidly stored, significantly reducing unnecessary storage and computational overhead while allowing quick access and manipulation of voxel data. With advancements in hardware technology, GPU methods such as GVDB [75] and GSPGrid [78] are becoming dominant, also focusing on compatibility with more APIs [79].
## 3 Results
### Experiments  
As the background is water in our experiment, the wavefronts backscattered by the wires are well-detected, and the signal quality is, therefore, excellent to estimate the AP with the PS method. The GB method results in higher image quality (see Figure 8d), although the geometry and position of the bone-mimicking plate are accurately described during image reconstruction with ARC reconstruction. Different results are obtained by switching from AP estimated with scatterers 4 and 6 in Figure 8b to scatterers 3 and 5 in Figure 8c. The image of scatterers 2 and 3 is improved in Figure 8c compared to Figure 8b since the AP for the central patch was estimated with scatterer 3, and scatterer 2 is closer to scatterer 3 than 4. This suggests that the actual size of the central isoplanatic patch is close to 10 mm; therefore, more than three isoplanatic patches may be required to obtain optimal image quality with the PS method. Table 1 indicates that the PS method improves the FWHM by 0.96 mm compared to the conventional reconstruction. FWHM was slightly improved (0.1 mm) with the GB method compared to the PS method.  
The GB method increases the SCR by 6.5 dB and 8.2 dB compared to the conventional and PS methods (see Table 1 and Figure 8f), respectively, and reduces the axial/lateralization error by 2.21/0.7 mm (see Table 1) compared to the conventional method. The SCR obtained by the PS method is 1 dB lower than the conventional reconstruction.  
Figure 8: The reconstructed experimental images using the (**a**) conventional beamforming, (**b**,**c**) phase-screen modeling, and (**d**) geometry-based reconstruction techniques when a Sawbones plate was used in front of the probe as an aberrator. The aberration profiles (AP) of scatterer 4/6 and 3/5 were used in (**b**,**c**), respectively. The lateral FWHM (**e**), the signal-to-clutter ratio (**f**), and the localization error (**g**) obtained with all the methods for each scatterer. The red dashed boxes were used for signal-to-clutter ratio calculation in Table 1. The red dots indicate the true location of the scatterers. The numbers in (**a**) are used for referencing in the text and (**e**–**g**).